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# Pooled Ols Stata

I know the regress command for a normal regression but how do I run a POLS regression ?. Khóa học ngắn hạn hướng dẫn thực hành về các bước thực hiện hồi quy dữ liệu bảng bằng Stata, các phương pháp hồi quy FEM/ REM/ OLS/ POOLED OLS/ FGLS và hệ thống các kiểm định liên quan; các sự cố hay gặp trong xử lý dữ liệu. I know that by clustering the standard errors at CompanyID will take into account the panel structure. 7 Problem of missing observations, applications of Hausman's test. 但是，如果个体间（组间）的差异不明显，那么采用OLS 对混合数据（Pooled OLS）进 行估计即可。 检验的基本思路为，在个体效应不显著的原假设下，应当有如下关系成立： (8. Measures of effect size in Stata 13. Pooled ols models only stack observations for each case over time, one on the top of the other, which does not result in distinctions across cases and over time. I'm using Stata/MP 13. This regression is not considering that you have different individuals across time periods, and so, it is not considering for the panel nature of the dataset. [email protected] ols에서는 오차항의 크기 만큼 가중치가 주어지는데, gls에서는 오차항의 이분산성이나 자기상관성을 상쇄할 수 있도록 보정된 가중치를 부여하여 최소제곱 추정을 한다고 생각하면 편하다. Hausman Test and Chow Test 8. In my understanding, a pooled OLS regression in STATA is provided through the command reg or regress (which is completely the same). Conclusions. An example of the benchmarking code in Stata is given below: * Stata benchmarking program : Example set processors 4 timer clear timer on 1 bs, nodrop reps(1000) seed(1): reg y x timer off 1 timer list. Partitioned Regression Model and the FWL Theorem. Consider OLS (ordinary least squares) estimation. Dec 17, 2013 #1. Stata's RE estimator is a weighted average of fixed and between effects. 79, the RSS decreased from 1. to regression analysis with panel data, pooled regression, the fixed effects model, and the random effects model. Colin Cameron Univ. equations estimated from pooled data using OLS procedure and pooled data tend to generate five complications (Hicks 1994, 171-72). Vengono proposti, infine, alcuni risultati asintotici. OLS: functional form specifications, dummy variables, time trend and seasonality, model misspecifications and how to avoid them, and lastly, model selection and goodness of fit measures. Of course, this assumption can easily be violated for time series data, since it is quite reasonable to think that a prediction that is (say) too high in June. 01) * RE vs Pooled OLS？. The null hypothesis is that the variance of the unobserved heterogeneity is zero, e.  Pooled data occur when we have a “time series of cross sections,” but the observations in each cross section do not necessarily refer to the same unit. Meanwhile, pooled OLS comes from a panel data context and thus it is not as general. In this case, we could apply OLS using models for pooled data (pooled regression). 16 standard deviations (OLS); 0. Linear (static) unobserved-effects regression model with panel data: - Pooled OLS. I am now struggling a lot on to how I should specify this pooled OLS. OLS regression with time series data This might be a really dumb question, but I'm doing undergraduate research in economic history and I have time series data that I was told to run an OLS regression on and analyze it. alternative hypothesis, while the pooled within dimension estimators do not. The data sets used are those used in the STATA cross-sectional time series reference manual. Say I want to fit a linear panel-data model and need to decide whether to use a random-effects or fixed-effects estimator. Apply the formula In the following example, we assume that two groups have difference slopes of cost and. The variance of the estimates can be estimated and we can compute standard errors, t -statistics and confidence intervals for coefficients. Pooled OLS versus Random Effects. region id year grpmlnr grppc cpi 1 region1 1 1998 18245. 5 2000 101 2010 243,000 4 3. By running an ordinary least squares regression with robust standard errors. We suspect that one reason might be that the coding and/or computational costs outweigh the benefits for many researchers. Persamaan Regresi Data Panel. 1 Pooled OLS 推定法 評価版では割愛しています。 6. 2014-05-18 用stata做ols回归后出来的数据分别代表什么？ 14; 2017-09-24 如何正确理解混合ols回归模型 1; 2018-04-27 有虚拟变量的stata模型回归命令 1; 2016-07-29 用stata怎么检验是混合回归模型合适还是固定效应模型合适 3; 2013-08-20 stata中的reg命令是作什么回归的呀？ 10. 2 用例 評価版では割愛しています。 7. I know that by clustering the standard errors at CompanyID will take into account the panel structure. Compare the results of these analyses. In Eviews, we cannot execute the LM Test (Common VS Random) alike STATA. 1-7 追蹤資料模型的效果選擇(pooled OLS、固定效果、隨機效果?) 1-7-1 F 檢定( 該選pooled OLS vs. One more thing, sometimes robust regression itself can not remove heteroscedasticity. Anova In Eviews. Δ y i t {\displaystyle \Delta y_ {it}} Δ x i t {\displaystyle \Delta x_ {it}} The FD estimator avoids bias due to. Hello everyone! Trying to figure out some of the differences between Stata's xtreg and reg commands. Similarly, treatment status accounted for almost none of the variability in reading scores while it accounted for roughly 17% of the. Hausman 検定 評価版では割愛しています。 9. As political scientists have. Analysing Multiply Imputed Datasets. An example of the benchmarking code in Stata is given below: * Stata benchmarking program : Example set processors 4 timer clear timer on 1 bs, nodrop reps(1000) seed(1): reg y x timer off 1 timer list. This paper presents additional estimates using different techniques of estimation: mean-group, pooled mean-group and GMM. Linear regression is. This feature is included to aid in analysis and comparison across model types and to give you access to HCCME standard errors and other panel. is estimated by the pooled mean group estimator (Shin et al. Linear Non Dynamic Panel Data Models 2014-15 Panel Data Models Pooled Model Individual and Time Dummies 2 I xit does not include an intercept I If an intercept is included then one of the N individual dummies must be dropped I Many packages prefer to do that I Focus on short panels where N !1but T does not I Then the 's can be consistently estimated I At least in the sense that there. OLS Regressions of Crimes/1000 Popluation on Unemployment Rate. This example data set would be considered a balanced panel because each person is observed for the defined characteristics of income, age, and sex each year of the study. matrices for the panel data estimators, including a general treatment of cluster effects. Dalla scomposizione del termine di errore della stima OLS si ricavano momenti campionari misti temporali e cross-section di cui, con l’aiuto di alcune simulazioni non riportate nel testo, è stata riscontrata la convergenza a quantità non nulle. you can output your results using "outreg2"). xtreg logy h inv gov open,fe *-- R^2 * y_it = a_0 + x_it*b_o + e_it (1) pooled OLS * y_it = u_i + x_it*b_w + e_it (2) within estimator * ym_i = a_0 + xm_i*b_b + em_i (3) between estimator *. coefficient at means f 0. est store m_stata. Find and replace null value indicators (e. This entails that most advantages of panel modelling are lost. In order to start with pooled regression, first, create dummies for all the cross-sectional units. In Stata, we obtain FE estimates from the ™xtreg™command if we use the option ™fe™, e. Overall model for both the Pooled OLS and the LSDV regression model tested to be statistically significant and a greater percentage (99. 1 Seemingly Unrelated Regressions. Hello, I am running a fixed effects model on an unbalanced dataset. However, if Cov(X j,c)≠0, the the pooled. The OLS results show a 53. Consider OLS (ordinary least squares) estimation. I am running regressions on an unbalanced dataset with large N small T. Tutorial ini menjelaskan bagaimana caranya melakukan uji regresi data panel dengan menggunakan aplikasi STATA. Yes, please send me the News. [clarification needed]The FD estimator avoids bias due to some omitted, time-invariant variable using the repeated observations over time:. Panel Data Set A shows the data collected for two people (person 1 and person 2) over the course of three years (2013, 2014, and 2015). Multilevel Models: Pooled and Clustered Data (Chapel Hill, NC) Instructor(s): Multilevel models (also known as hierarchical linear models or mixed models) provide an extremely flexible approach to the analysis of a wide array of social and behavioral science data. The residual versus income plot shows clear evidence of heteroscedasticity. I know that this indicates that the. But is it always. The second volume of the EViews 7 User’s Guide, offers a description of EViews’ interactive tools for advanced statistical and econometric analysis. Equations for the Ordinary Least Squares regression Ordinary Least Squares regression ( OLS ) is more commonly named linear regression (simple or multiple depending on the number of explanatory variables). You can conduct a likelihood ratio test: LR[i+1] = -2LL(pooled model) [-2LL(sample 1) + -2LL(sample 2)] where samples 1 and 2 are pooled, and i is the number of independent variables. (I) Basic panel commands in Stata • xtset • xtdescribe • reshape (II)Panel analysis popular in Economics • Pooled OLS • Fixed-Effects Model & Difference-in-Difference • Random Effects Model. Pooled Cross-Section w/City Fixed Effects Notes: Heteroskedasticity-Robust Standard errors in Parentheses. Ask Question Asked 3 years, 6 months ago. Colin Cameron and Pravin K. 01) * 解读 xtreg,fe 的估计结果 xtreg logy h inv gov open,fe *-- R^2 * y_it = a_0 + x_it*b_o + e_it (1) pooled OLS * y_it = u_i + x_it*b_w + e_it (2) within estimator * ym_i = a_0 + xm_i*b_b + em_i (3) between estimator *. command, which is a Stata command. Regression Analysis with Cross-Sectional Data 23 P art 1 of the text covers regression analysis with cross-sectional data. In Stata, we obtain FE estimates from the ™xtreg™command if we use the option ™fe™, e. The residual versus income plot shows clear evidence of heteroscedasticity. Linear regression is. al (2010); Eq. FE and OLS when there is Panel Non-Response Michael Lechner a pooled model in which the regressors are a constant term, a dummy variable for (future) treatment group membership, an indicator variable for the period, as well as an interaction of the latter two variables. coefficient at means f 0. I have already performed xtreg, fe, and the F tests indicated that Pooled OLS is the better option. Optional table of explanatory variable coefficients. When computing a large number of quantiles (e. Ordinary Least Squares regression, often called linear regression, is available in Excel using the XLSTAT add-on statistical software. Pencina , 3 David R. Panel Data Analysis with Stata Part 1 Fixed Effects and Random Effects Models Abstract The present work is a part of a larger study on panel data. Before using xtregyou need to set Stata to handle panel data by using the command xtset. advantage or disadvantage relative to the pooled wage structure. If all coefficients are either pooled or heterogeneous, xtdcce2 calculates an adjusted R2 following Holly et. 100 2000 134,000 4 2. Microeconometrics using Stata, Stata Press, forthcoming. Our thanks go to two referees for helpful comments on the original version. The residual versus income plot shows clear evidence of heteroscedasticity. I know that by clustering the standard errors at CompanyID will take into account the panel structure. Should I include Pooled OLS, Random Effects and Fixed Effects in Robust Standard Errors in Fixed Effects Model (using Stata Code Riffs: Stata and Regression Tables - Eyal Frank. (I) Basic panel commands in Stata • xtset • xtdescribe • reshape (II)Panel analysis popular in Economics • Pooled OLS • Fixed-Effects Model & Difference-in-Difference • Random Effects Model. New Opportunities When we have a dataset with both a time series and a cross-section dimension, this opens up new. Vengono proposti, infine, alcuni risultati asintotici. panel, yaitu Pooled Least Square atau biasa menggunakan metode Ordinary Least Square (OLS), Fixed Effect (FE), dan Random Effect (RE). A cross-sectional dataset consists of a sample of individuals, households, firms, cities, states, countries, or any other micro- or macroeconomic unit taken at. This suggests that we cannot reject the null hypothesis that the coefficient is equal to zero. Pooled and Panel Data Analysis 1 Topics Pooled Data Example -Stata & R 6 •Suppose we have a pooled data with information for the variable bias). The remainder of the paper (Sections 5 and 6) sets the new estimators in the context of the plmpackage and provides some examples of application. Trong Stata: regress fdi x1 x2 x3. To estimate the LSDV model, Let us examine fixed group effects by introducing group (airline) dummy variables. 7% p-value for our coefficient on $\hat{y}^2$. We suspect that one reason might be that the coding and/or computational costs outweigh the benefits for many researchers. 2 Panel-data methods overview 229 8. Random-effects model Pooled model or population-averaged model Two-way-effects model. I am running regressions on an unbalanced dataset with large N small T. Partitioned Regression Model and the FWL Theorem. Random Effects (RE) Model with Stata (Panel) The essential distinction in panel data analysis is that between FE and RE models. If all coefficients are either pooled or heterogeneous, xtdcce2 calculates an adjusted R2 following Holly et. By running an ordinary least squares regression. Consider OLS (ordinary least squares) estimation. Presenting Panel Data Models 9. equations estimated from pooled data using OLS procedure and pooled data tend to generate five complications (Hicks 1994, 171-72). 3 Fixed eﬀects estimation. Dear Statalists, I am struggling with choosing firm or industry fixed-effect for my regression with unbalanced panel data of around 800 firms of 48 industry. See a nice summary for Pooled OLS, Fixed effects and Random effects HERE. Heteroscedasticity tests use the standard errors obtained from the regression results. This is the key reason why pooled cross sections are di⁄erent from panel data. The residual versus income plot shows clear evidence of heteroscedasticity. OLS: The Least Squares Assumptions Y i = 0 + 1X i + u i Assumption 1:conditional mean zero assumption: E[u ijX i] = 0 Assumption 2: (X i;Y i) are i. of California - Davis (Prepared for West Coast Stata Users™Group Meeting Based on A. It is eﬃciently estimated by least squares (OLS). 1面板数据的特点 面板数据(panel data 或longitudinal data)，指在一段时间内跟踪 同一组个体(individual)的数据。. o Ordinary Least Squares (OLS) o Weighted least squares (WLS) o Generalized least squares (GLS) Least-squares estimation of panel models typically entails three steps: (a) Data transformation or first-stage estimation (b) Estimation of the parameters using Ordinary Least Squares. Fixed Effects, First Differences and Pooled OLS - intuition - Duration: 7:02. Pooled Medical utilization and economic burden – adjusted medical expenditures Time 2-part logit model Not described, 1992-2000 Survey weights STATA, survey estimators Robust Huber-White SEs Noyes(Noyes, Liu, Holloway, & Dick, 2007) Pooled Diagnosis of Parkinson’s disease and medical expenditures Case identification algorithms. For: H0: σu2 = 0. First Difference for two period panel data. I have already performed xtreg, fe, and the F tests indicated that Pooled OLS is the better option. My personal goal I would like to divide into Patients seek out professional health care with their own goals in mind. It calculates the standard un-adjusted R2 and the adjusted R2 as common in the literature. By taking the mean of each variable for each unit across time, and running a regression on the collapsed dataset of means. default uses the default Stata computation (allows unadjusted, robust, ols ols regression (between dependent variable and endogenous variables; useful as a benchmark) Number of variables that are pooled together into a matrix that will then be transformed. It is important, however, to remember that the standard errors and test statistics obtained are known to be valid only as G → with each Mg fixed. In order to start with pooled regression, first, create dummies for all the cross-sectional units. 17 standard. I am running regressions on an unbalanced dataset with large N small T. 1 Introduction 229 8. I know that by clustering the standard errors at CompanyID will take into account the panel structure. If all variables are exogenous both OLS and 2SLS are consistent If there are statistically significant differences in the coefficients we conclude that y 2 is endogenous 2. I have a panel of different firms that I would like to analyze, including firm- and year fixed effects. Thus, this is a fully interacted (saturated) regression, which is entirely. Pooled Ordinary Least Squares and robust standard errors ˜˜ 15:45-16:15 Break 16:15-18:00 Lab: • Exploring serial correlations of continuous and categorical variables ˜ • De-meaning ˜ • Comparing the estimates and serial correlation of ˜ o Pooled OLS˜ o Pooled OLS using time demeaned data ˜. Hello, I am running a fixed effects model on an unbalanced dataset. Panel data (also known as longitudinal or cross -sectional time-series data) is a dataset in which the behavior of entities are observed across time. o Ordinary Least Squares (OLS) o Weighted least squares (WLS) o Generalized least squares (GLS) Least-squares estimation of panel models typically entails three steps: (a) Data transformation or first-stage estimation (b) Estimation of the parameters using Ordinary Least Squares. Dear Statalists, I am struggling with choosing firm or industry fixed-effect for my regression with unbalanced panel data of around 800 firms of 48 industry. Introduction to Robust and Clustered Standard Errors Miguel Sarzosa Department of Economics University of Maryland 3 Consequences 4 Now we go to Stata! Regressions and what we estimate A regression does not calculate the value of a relation between two variables. A second approach is to estimate the panel regression by pooled OLS and use the robust standard errors proposed by Driscoll and Kraay (1998). *"Year Effects" here really just means a dummy for 1987(!) since there are only two years of data, 1982 and 1987. abc New Member. 2014-05-18 用stata做ols回归后出来的数据分别代表什么？ 14; 2017-09-24 如何正确理解混合ols回归模型 1; 2018-04-27 有虚拟变量的stata模型回归命令 1; 2016-07-29 用stata怎么检验是混合回归模型合适还是固定效应模型合适 3; 2013-08-20 stata中的reg命令是作什么回归的呀？ 10. 4 Robust Asymptotic Variance Matrix 175 7. xtdcce2 calculates up to three different coefficients of determination (R2). Stack Exchange network consists of 176 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Provided the fixed effects regression assumptions stated in Key Concept 10. ECON 407: Companion to Panel Data Models This document, a companion to the Panel Data series of lecture notes, provides a brief description of how to implement panel data models in both R and Stata. The Fixed Effect Least-Square Dummy Variable Model (LSDV) 2. I think that would have then the same parameterization as a pooled OLS, including the constant, and I think would also correspond to the random effects model. I cannot reject the null hypothesis that all ui = 0. xtreg logy h inv gov open,fe *-- R^2 * y_it = a_0 + x_it*b_o + e_it (1) pooled OLS * y_it = u_i + x_it*b_w + e_it (2) within estimator * ym_i = a_0 + xm_i*b_b + em_i (3) between estimator *. Este modelo se expresa como: (1) Donde i significa la i-ésima unidad transversal (estado) y t el tiempo t (año). My understanding of pooled OLS is that it is most appropriate when you have observational units observed in more than one time period, but individual units are not repeatedly observed across periods. 79, the RSS decreased from 1. regress lwage exp exp2 wks ed union ind occ. POOLED OLS. The remaining gap of the Fixed Effect estimation of 3. 2 用例 評価版では割愛しています。 8. 2 manual entry for the mixed command. I am running regressions on an unbalanced dataset with large N small T. Lecture9_2015 Georgetown University ECON 554 - Fall 2014. Similarly, treatment status accounted for almost none of the variability in reading scores while it accounted for roughly 17% of the. abc New Member. Using conditions with 'if ' command. panel, yaitu Pooled Least Square atau biasa menggunakan metode Ordinary Least Square (OLS), Fixed Effect (FE), dan Random Effect (RE). Trivedi, is an outstanding introduction to microeconometrics and how to do microeconometric research using Stata. OLS using Matrix Algebra Finite Sample Properties, Gauss-Markov Theorem, Hypothesis Tests 4. then pooled OLS is a consistent and efﬁcient estimator of [ ]. Unlike the pooled cross sections, the observations for the same cross section unit (panel, called long form required by Stata command xtreg. Introduction Since the publication of Nathaniel Beck's and Jonathan Katz's influential article on pooled time-series analysis (Beck/ Katz 1995) analyzing panel data has become a standard in comparative political science. One thing you should know, STATA can give the same result with SPSS while executing this Common Effect Model but it’s different with Eviews result. = Mεi (from 8) This is distributed as Χ2; if it exceeds the critical value we conclude OLS is inappropriate and random effects is. The table below shows the pooled OLS estimates with a comparison of alternative standard errors. Then data viewed as clustered on the individual unit. Keyword Research: People who searched pooled also searched. pooled OLS will be inconsistent. This paper presents additional estimates using different techniques of estimation: mean-group, pooled mean-group and GMM. Simulations, Econometrics, Stata, R,intelligent mulit-agent systems, Psychometrics, latent modelling, maximization, statistics, quantitative methods. *"Year Effects" here really just means a dummy for 1987(!) since there are only two years of data, 1982 and 1987. These notes present the annotated log of a STATA session demonstrating the use of many of these commands. In Eviews, we cannot execute the LM Test (Common VS Random) alike STATA. As the name implies, MLE. I think that would have then the same parameterization as a pooled OLS, including the constant, and I think would also correspond to the random effects model. r2_a package was written in 2001 by Jeff Pitblado to calculate Adjusted R Squared after running a regression analysis. In principle, implementation is straightforward as econometrics packages include cluster-robust as an option for the commonly-used estimators; in Stata it is the. It is important, however, to remember that the standard errors and test statistics obtained are known to be valid only as G → with each Mg fixed. One more thing, sometimes robust regression itself can not remove heteroscedasticity. Wilcoxon Rank Sum procedure demonstrated with an example. The residual versus income plot shows clear evidence of heteroscedasticity. I have a sample of 94 elements and a time horizon of 5 years,a dependent variable (94x5) and 6 independent variables (94x5). 100 2000 134,000 4 2. As political scientists have. Statlab computers use Stata 10. I Autocorrelation in the errors: Since individual i is repeatedly observed (contrary to pooled data) Corr( i;s; i;t) 6= 0 with s 6= t is very likely. An Example Is the evacuation behavior from Hurricanes Dennis and Floyd statistically equivalent?. Goodness of Fit. Moutinho and G. Dummy variables, also known as indicator variables, are those which take the values of either 0 or 1 to denote some mutually exclusive binary categories like yes/no, absence/presence, etc. Dear Statalists, I am struggling with choosing firm or industry fixed-effect for my regression with unbalanced panel data of around 800 firms of 48 industry. (Nguồn: tác giả tự tổng hợp từ phần mềm Stata 12. 1 Pooled Cross Sections versus Panel Data Pooled Cross Sections are obtained by col-lecting random samples from a large polula-tion independently of each other at di erent points in time. PROC PANEL allows you to pool time series cross-sectional data and run regressions on the data. I am now struggling a lot on to how I should specify this pooled OLS. 隨機效果?)：隨機效果的事後指令xttest0 1-7-3 F 檢定、Lagrange 乘數(multiplier) 檢定、Hausman 檢定之流程. 2 Dynamic Completeness 173 7. Use meals, ell and emer to predict api scores using 1) OLS to predict api from the full set of observations, 2) OLS to predict api using just the observations with api scores of 550 or higher, and 3) using truncreg to predict api using just the observations where api is 550 or higher. Run OLS on a constant, educ and all interactions except one, for instance the one for the rst year. Stata Command Syntax. We suspect that one reason might be that the coding and/or computational costs outweigh the benefits for many researchers. In other terms, they might be serially correlated, such that errors in country i at time t are correlated with errors in country i at time t+1. If all coefficients are either pooled or heterogeneous, xtdcce2 calculates an adjusted R2 following Holly et. The (pooled) OLS is a pooled linear regression without fixed and/or random effects. From the 2SLS regression window, select the dependent, independent and instrumental variable. Overview of OLS for Linear Models Linear Panel Data Models: Basics Linear Panel Data Models: Extensions assumptions for OLS y i = α+ βx i +ε i βˆ = x 0x 1 x y 1 Var (ε i) = σ2 for i = 1, ,n. In order to start with pooled regression, first, create dummies for all the cross-sectional units. 0 1250 In contrast, pooled cross sectional data is multiple snapshots of multiple bunches of (randomly selected). [clarification needed]The FD estimator avoids bias due to some omitted, time-invariant variable using the repeated observations over time:. OLS regression with time series data This might be a really dumb question, but I'm doing undergraduate research in economic history and I have time series data that I was told to run an OLS regression on and analyze it. PROC PANEL allows you to pool time series cross-sectional data and run regressions on the data. Compare the results of these analyses. Trivedi, Microeconometrics using Stata, Stata Press, forthcoming. Such variables may be age or income. Therefore, the first step is to run the regression with the same three variables considered in the previous article for the same period of 1997-98 to 2017-18. * Seems to be working well, let's simulate it 1,000 times. Last time Practical Statistics met to try to wrap our heads around fixed, mixed, and random effects. Contents vii 6. Estimates differ slightly because different algorithms are being used. in diﬀerent ways: by either (pooled) ordinary least squares (OLS), ﬁxed eﬀects (FE), random eﬀects (RE), or by ﬁrst-diﬀerencing methods (FD). Simulations, Econometrics, Stata, R,intelligent mulit-agent systems, Psychometrics, latent modelling, maximization, statistics, quantitative methods. year in factor-variable notation) which will estimate a coefficient for each year. Khóa học ngắn hạn hướng dẫn thực hành về các bước thực hiện hồi quy dữ liệu bảng bằng Stata, các phương pháp hồi quy FEM/ REM/ OLS/ POOLED OLS/ FGLS và hệ thống các kiểm định liên quan; các sự cố hay gặp trong xử lý dữ liệu. We can calculate F in STATA by using the command. LEAST squares linear regression (also known as “least squared errors regression”, “ordinary least squares”, “OLS”, or often just “least squares”), is one of the most basic and most commonly used prediction techniques known to humankind, with applications in fields as diverse as statistics, finance, medicine, economics, and psychology. The Stata command to run fixed/random effecst is xtreg. Stata's RE estimator is a weighted average of fixed and between effects. Abadie, Alberto. 2: 1488: 69: pooled ols. Trivedi, Microeconometrics using Stata, Stata Press, forthcoming. Moutinho and G. I know the regress command for a normal regression but how do I run a POLS regression ?. In econometrics, Ordinary Least Squares (OLS) method is widely used to estimate the parameters of a linear regression model. 混合OLS模型、固定效应模型、随机效应模型的区别是什么 关键词：混合ols 固定效应,固定效应还是混合ols,stata随机效应模型,面板数据随机效应模型 混合OLS模型、固定效应模型、随机效应模型的区别是什么？. Scatter diagrams and histograms. A complete and up-to-date survey of microeconometric methods available in Stata, Microeconometrics Using Stata, Revised Edition is an outstanding introduction to microeconometrics and how to execute microeconometric research using Stata. Yang Hu 34,298 views. And the model must have one or more (unknown) parameters. Their goals may not match ours, but we as health care providers have a duty to inform and treat our clients with competence and afford them the utmost With constantly evolving technology, poor economics leading to major hospital cutbacks, and healthcare reforms, strong nursing. Here, analysis of multiply imputed data is achieved by commands that start with mi. … Continue reading Ordinary Least Squares (OLS. 35)其中，u 表示不受约束的模型，即我们的固定效应模型；r 表示受约束的模型，即混合. 2 manual entry for the mixed command. here is the code that I have right now: data have; input fundid time flow flow_lag return; datalines; 10001 1 10. Bootstrapping in Stata. Hello, I am running a fixed effects model on an unbalanced dataset. Dec 17, 2013 #1. Under this sampling scheme, the observations form different time periods are pooled together and OLS is conducted on the pooled sample. Testing for functional form and structural break. Trivedi, is an outstanding introduction to microeconometrics and how to do microeconometric research using Stata. Which one: Fixed Effects or Random Effects?: The generally accepted way of choosing between FE and RE is running a Hausman test. To that effect I was planning to estimate a fixed effect panel regression in Stata. This suggests that we cannot reject the null hypothesis that the coefficient is equal to zero. Logistic models require an additional step in coding to make the interaction terms interpretable. Lecture 9: Heteroskedasticity and Robust Estimators In this lecture, we study heteroskedasticity and how to deal with it. If you use instead a time trend, it does not matter whether it starts from 1 or starts from 1990; any variable for which D. regress motivation salary salary_d d size culture. First, we'll compute a pooled OLS model for the years 1990 and 1993: By using pooled OLS, we are disregarding the term in the regression equation above. Econometric Methods for Panel Data University of Vienna and Institute for Advanced Studies Vienna 19/21 Introduction Fixed effects Random effects Two-way panels. Without that part you will get overall difference, which is meaningless for our purpose. 4 Random eﬀects estimation. This article has also been viewed 2,124,979 times. Dear Statalists, I am struggling with choosing firm or industry fixed-effect for my regression with unbalanced panel data of around 800 firms of 48 industry. 固定效果?)：「xtreg?, fe」指令 1-7-2 Lagrange 乘數檢定(pooled OLS vs. ' and they indicate that it is essential that for panel data, OLS standard errors be corrected for clustering on the. Adrienne Cupples 1, 4. 3: 2038: 65: pooled ols. Visualizations and the right statistical. o HGL is ambiguous about this and sometimes use pooled to refer to panel. Section 8 Models for Pooled and Panel Data o Panel data commands in Stata start with xt, as in xtreg. Pooled Estimator: PROC PANEL allows you to pool time series cross-sectional data and run regressions on the data. Stata users, especially social scientists, will find helpful advice in fitting statistical models to a diverse set of examples encountered when investigating the complexity and subtlety of real data. o Ordinary Least Squares (OLS) o Weighted least squares (WLS) o Generalized least squares (GLS) • One strategy is to combine pooled OLS with cluster-consistent standard errors. To make the dummies for all 30 companies, use the below command:. Pooled Estimator PROC PANEL allows you to pool time series cross-sectional data and run regressions on the data. Panel Data Models Example. The second volume of the EViews 7 User’s Guide, offers a description of EViews’ interactive tools for advanced statistical and econometric analysis. Pooled OLS (1), FE (1) 4) Specific dummies for each rebate: a. I know that this indicates that the. I cannot reject the null hypothesis that all ui = 0. We also compare the results with what you get if you just use OLS regression instead. [If you use this code, please cite: Hsiang (PNAS 2010)]. 17 standard deviations, FE of 0. 3 Systems of Equations. ) follow the suggestion by Wooldridge and report three measures: within R2, between R2,. How to create dummy variables. Ordinary Least Square (OLS) Model ini menggabungkan data cross section dan data time series, kemudian dengan menggunakan metode OLS terhadap data panel tersebut. Also: the estimation technique they recommend is just pooled OLS; they only recommend a modification of the standard errors, so the point estimates are the same as using lm() in R or reg in Stata. I have already performed xtreg, fe, and the F tests indicated that Pooled OLS is the better option. How do these estimates compare? Does a Hausman test accept RE as the more appropriate estimator? 24. I know that by clustering the standard errors at CompanyID will take into account the panel structure. 's (or "i options" as Joe Glass called it) have a name. Analysing Multiply Imputed Datasets. The Multiple Regression Model Controlling for Confounding Factors 3. The previous article explained the procedure to run the regression with three. I know the regress command for a normal regression but how do I run a POLS regression ?. Random Effect Model 7. Both RE and OLS rely on the same assumptions for consistency, so if your test rejects RE it will likely reject OLS. These results can be compared with. By running an ordinary least squares regression. 2012 Applied Microeconometrics Using Stata 2. Tức là, mô hình này sử dụng dữ liệu như một phân tích OLS bình thường, sử dụng tập hợp thành đám mây dữ liệu để ước lượng tương quan trong mô hình. Hausman Test and Chow Test 8. Regression Analysis with Cross-Sectional Data 23 P art 1 of the text covers regression analysis with cross-sectional data. • Coefficients will be unbiased but not efficient. 7 Problem of missing observations, applications of Hausman's test. According to Wooldridge (2010), pooled OLS is employed when you select a different sample for each year/month/period of the panel data. By taking the mean of each variable for each unit across time, and running a regression on the collapsed dataset of means. Pooling is admissible if there are no fixed effects or random effects present in the data. (-) Benchmarks computed 10 quantiles. of California - Davis Prepared for West Coast Stata Users'Group Meeting Based on A. Fixed and random effects models. Knowing that supervised ML tasks are normally divided into classification and regression, we can collocate Linear Regression algorithms in the latter category. In short, pooled regression is just another way of saying that you are assuming no id or time fixed effects. In my understanding, a pooled OLS regression in STATA is provided through the command reg or regress (which is completely the same). Package version and installation. For example, regress. However, by specifying pooled OLS you are specifying a multiple linear regression. We can calculate F in STATA by using the command. 096 million barrels a day. From the 2SLS regression window, select the dependent, independent and instrumental variable. 1 Pooled Cross Sections versus Panel Data Pooled Cross Sections are obtained by col-lecting random samples from a large polula-tion independently of each other at di erent points in time. 0 2600 102 2010 65,000 2 1. Stack Exchange network consists of 176 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Dear Statalists, I am struggling with choosing firm or industry fixed-effect for my regression with unbalanced panel data of around 800 firms of 48 industry. Pooled OLS 推定量 2 式を次のように書き換えます. 5 Stata linear panel-data commands. Had the id variable in the example above not been available, we would have referred to this as a pooled OLS. Trivedi,Panel methods for Stata October 25, 2007 1 / 39 Microeconometrics using Stata, Stata Press, forthcoming. In panel data analysis (the analysis of data over time), the Hausman test can help you to choose between fixed effects model or a random effects model. In my eyes, every scientist, data analyst or informed person should have a minimal understanding of this method, in order to understand, interpret and judge the. Cluster robust standard errors (clustered on ID) would be the correct way. 10:00 - 12:00 Session 4 - Between and within-effects of level-1 covariates - pooled OLS models, be able to apply multilevel models to their own data using Stata. How to run the linear regression with dummy variables use STATA - Duration: 3:23. txt) or read online for free. REGRESIÓN AGRUPADA (POOLED OLS) El enfoque más simple de analizar datos tipo panel es omitir las dimensiones del espacio y el tiempo de los datos agrupados y sólo calcular la regresión MCO usual. 22 standard deviations 6) Government: pooled OLS of 0. However, one of the barriers to widespread usage in development economics. p 個の説明変数によるモデルの場合，OLS 回帰モデルは次式で書かれる：. Nếu kết quả là strongly balanced thì điều này có nghĩa dữ liệu của bạn khá đầy đủ. xtscc produces Driscoll and Kraay (Rev. Pooled OLS Regression Model In pooled OLS regression, we simply pool all observations and estimate the grand regression, ignoring the cross-section and time series nature of the data, in which case the error term captures everything. This is the standard pooled model where intercepts and slope coefficients are homogeneous across all N cross-sections and through all T time periods. Section 13 Models for Pooled and Panel Data Data definitions o Panel data commands in Stata start with xt, as in xtreg. alternative hypothesis, while the pooled within dimension estimators do not. Stack Exchange network consists of 176 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Dear Statalists, I am struggling with choosing firm or industry fixed-effect for my regression with unbalanced panel data of around 800 firms of 48 industry. 1 PA 推定法 評価版では割愛しています。 7. Microeconometrics Using Stata, Revised Edition, by A. It is important, however, to remember that the standard errors and test statistics obtained are known to be valid only as G → with each Mg fixed. The estimator is obtained by running a pooled OLS estimation for a regression of. 가령 우리에게 두 기업에서 15년 간 모은 투자, 주가, 매출액, 순이익 등의 패널 데이터가 있다고 해보자. [email protected] 私はStata歴2ヶ月強なのでゆるしていただきたい。ただ、一応これでも綺麗な表は出力されるはず。検索でたどり着いた方は試してみてください。なお、Stataに関しては英語で情報がたくさんネットに上がっているので英語で検索されることをおすすめする。. The Stata News—a periodic publication containing articles on using Stata and tips on using the software, announcements of new releases and updates, feature highlights, and other announcements of interest to interest to Stata users—is sent to all Stata users and those who request information about Stata from us. pooled | pooled testing | pooled standard deviation | pooled | pooled sample test | pooled ols | pooled means | pooled t-test | pooled synonym | pooled proporti. If you need to open a Stata 10 file on a Stata 9 machine, you can: o Use Stat/Transfer to convert the file between versions OR. These notes present the annotated log of a STATA session demonstrating the use of many of these commands. first -difference command on STATA. Task 3c: How to Generate Means Using Stata. Visualizations and the right statistical. equations estimated from pooled data using OLS procedure and pooled data tend to generate five complications (Hicks 1994, 171-72). The data sets used are those used in the STATA cross-sectional time series reference manual. pooled OLS residuals, while estimation of the alternative model involves generalized least squares either based on a two step procedure or maximum likelihood. Stata ® Applications: Panel Data Methods To understand the structure and formats of panel data, and to be able to carry out analysis of empirical issues using multiple periods of panel data using the methods of pooled OLS (POLS), first differencing (FD) and fixed effects (FE). I have already performed xtreg, fe, and the F tests indicated that Pooled OLS is the better option. Clustered SEs in R and Stata Clustered standard errors are popular and very easy to compute in some popular packages such as Stata, but how to compute them in R? With panel data it's generally wise to cluster on the dimension of the individual effect as both heteroskedasticity and autocorrellation are almost certain to exist in the residuals at. Panel Data Analysis with Stata Part 1 Fixed Effects and Random Effects Models Abstract The present work is a part of a larger study on panel data. wikiHow is a “wiki,” similar to Wikipedia, which means that many of our articles are co-written by multiple authors. In my eyes, every scientist, data analyst or informed person should have a minimal understanding of this method, in order to understand, interpret and judge the. 2 用例 評価版では割愛しています。 7. Under this sampling scheme, the observations form different time periods are pooled together and OLS is conducted on the pooled sample. (The completely pooled line is y = + x, with a common line for all counties, and the unpooled lines are y = j + x, with the 85 jÕs estimated by least squares. Introduction 2. This feature is included to aid in analysis and comparison across model types and to give you access to HCCME standard errors and other panel. Since Stata provides inaccurate R-Square estimation of fixed effects models, I explained two simple ways to get the correct R-Square. 2014-05-18 用stata做ols回归后出来的数据分别代表什么？ 14; 2017-09-24 如何正确理解混合ols回归模型 1; 2018-04-27 有虚拟变量的stata模型回归命令 1; 2016-07-29 用stata怎么检验是混合回归模型合适还是固定效应模型合适 3; 2013-08-20 stata中的reg命令是作什么回归的呀？ 10. It is eﬃciently estimated by least squares (OLS). 100 2000 134,000 4 2. Panel data models (pooled model, fixed effects model, and random effects model) Estimator properties (consistency and efficiency) Estimators (pooled OLS, between, fixed effects, first differences, random effects) Tests for choosing between models (Breusch-Pagan LM test, Hausman test) Handouts, Programs, and Data. This suggests that we cannot reject the null hypothesis that the coefficient is equal to zero. Example of pooled cross sectional data house (i) year (t) hprice bdrms bthrms sqrft 1 2000 85,500 3 2. mean-group, pooled mean-group and GMM estimators Resumé: The paper DSI30414 presented a panel estimation of manufactured exports using the new export market data described in DSI10513. Analysing Multiply Imputed Datasets. Pencina , 3 David R. We can calculate F in STATA by using the command. Johnston & DiNardo (1997) recall that the POLS estimators ignore the panel structure of the data, treat observations as being serially uncorrelated for a given individual, with homoscedastic errors across individuals and time periods:. p 個の説明変数によるモデルの場合，OLS 回帰モデルは次式で書かれる：. Multilevel Models: Pooled and Clustered Data (Chapel Hill, NC) Instructor(s): Multilevel models (also known as hierarchical linear models or mixed models) provide an extremely flexible approach to the analysis of a wide array of social and behavioral science data. Pooled OLS, RE vs. Panel data (also known as longitudinal or cross -sectional time-series data) is a dataset in which the behavior of entities are observed across time. Introduction Since the publication of Nathaniel Beck's and Jonathan Katz's influential article on pooled time-series analysis (Beck/ Katz 1995) analyzing panel data has become a standard in comparative political science. txt) or read online for free. est store m_stata. Section 8 Models for Pooled and Panel Data Data definitions • Pooled data occur when we have a “time series of cross sections,” but the observations in each cross section do not necessarily refer to the same unit. pdf), Text File (. The data sets used are those used in the STATA cross-sectional time series reference manual. - OLS Sampling Properties - Violation of the ideal conditions for OLS - Hypothesis testing and model selection - Instrumental Variables (IV) estimation - Generalized Method of Moments (GMM) estimation - Control function approach to endogeneity. If x does not vary with (e. wikiHow is a “wiki,” similar to Wikipedia, which means that many of our articles are co-written by multiple authors. Which should I choose: Pooled OLS, FEM or REM? I am building panel data econometric models. xtdcce2 calculates up to three different coefficients of determination (R2). 0 1600 2 2000 67,300 3 2. What is going on? Say I want to fit a linear panel-data model and need to decide whether to use a random-effects or fixed-effects estimator. The null hypothesis is that the variance of the unobserved heterogeneity is zero, e. The interpretation of the results would be easiest if the absorbed fixed effects have mean zero so that the left over regression has the interpretation of estimating the mean effect. I know that by clustering the standard errors at CompanyID will take into account the panel structure. 16 standard deviations (OLS); 0. abc New Member. The Fixed Effect First Difference Model 2. Pooled OLS Regression Model 2. webuse nlswork. Pooled Cross Sections. When computing a large number of quantiles (e. A second approach is to estimate the panel regression by pooled OLS and use the robust standard errors proposed by Driscoll and Kraay (1998). Stack Exchange network consists of 176 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Hierarchical modeling seems to be very rare. Goodness of Fit. Linear regression is. )2/ Σ Σeit2)-1] Where ei. That is, pooled OLS could be treated as a special case of multiple linear regression. * The Kao & Chiang (2000) Dynamic OLS (DOLS) estimator for Cointegrated Panel Data with homogeneous covariance structure was recently coded in Stata by Diallo Ibrahima Amadou (CERDI) as xtdolshm; you will also need to install ltimbimata. wikiHow is a “wiki,” similar to Wikipedia, which means that many of our articles are co-written by multiple authors. Ordinary Least Squares regression, often called linear regression, is available in Excel using the XLSTAT add-on statistical software. The Stata version follows the format of all Stata estimates, so it should be compatible with post-estimation commands (eg. Let's call back the dataset nlswork we already discussed in the OLS post. Become familiar with your dataset. wls0 exp age ownrent income incomesq, wvar (income) type (abse) noconst graph. Introduction into Panel Data Regression Using Eviews and stata Hamrit mouhcene University of khenchela Algeria [email protected] Mark , ,§andDonggyu Sul– OhioStateUniversity(e-mail:mark. 10:00 - 12:00 Session 4 - Between and within-effects of level-1 covariates - pooled OLS models, be able to apply multilevel models to their own data using Stata. Hello, I am running a fixed effects model on an unbalanced dataset. draws from joint distribution Assumption 3:Large outliers are unlikely Under these three assumption the OLS estimators are unbiased, consistent and normally distributed in large samples. It is eﬃciently estimated by least squares (OLS). Section 6 considers robust estimation of covariance 11. 35)其中，u 表示不受约束的模型，即我们的固定效应模型；r 表示受约束的模型，即混合. In my understanding, a pooled OLS regression in STATA is provided through the command reg or regress (which is completely the same). The dataset is an unbalanced panel which looks like ï. By running an ordinary least squares regression. webuse nlswork. Lecture 9: Heteroskedasticity and Robust Estimators In this lecture, we study heteroskedasticity and how to deal with it. 2 User's Guide. Statistical Properties of the OLS Coefficient Estimators 1. pooled OLS residuals, while estimation of the alternative model involves generalized least squares either based on a two step procedure or maximum likelihood. In order to start with pooled regression, first, create dummies for all the cross-sectional units. Moutinho and G. In statistics and econometrics, panel data or longitudinal data are multi-dimensional data involving measurements over time. Hashem PESARAN, Yongcheol SHIN, and Ron P. Econometric Methods for Panel Data University of Vienna and Institute for Advanced Studies Vienna 19/21 Introduction Fixed effects Random effects Two-way panels. Many panel methods also apply to clustered data such as. Outline 1 Introduction 2 Linear models overview 3 Example: wages 4 Standard linear panel estimators 5 Linear panel IV estimators 6 Linear dynamic models 7 Long panels 8 Random coe¢ cient models 9 Clustered data 10 Nonlinear panel models overview 11 Nonlinear panel models estimators 12 Conclusions A. Simons – This document is updated continually. This example data set would be considered a balanced panel because each person is observed for the defined characteristics of income, age, and sex each year of the study. I know that by clustering the standard errors at CompanyID will take into account the panel structure. Output generated from the OLS Regression tool includes: Output feature class. Keywords: st0001, parameter heterogeneity, ﬁxed eﬀects, pooled OLS, mean-group estimator, cross section dependence 1 Introduction Today, panel data is widly used for empirical studies in several research areas and the beneﬁts of panel data are well known (Baltagi 2013, p. Become familiar with your dataset. Linear regression is. In principle, implementation is straightforward as econometrics packages include cluster-robust as an option for the commonly-used estimators; in Stata it is the. First, we'll compute a pooled OLS model for the years 1990 and 1993: By using pooled OLS, we are disregarding the term in the regression equation above. Dec 17, 2013 #1. Thus, this is a fully interacted (saturated) regression, which is entirely. Has two problems: 1 Heteroskedasticity (see clustering from diagnostics week) 2 Possible violation of zero conditional mean errors Both problems arise out of ignoring theunmeasured heterogeneity inherent in a i. ' and they indicate that it is essential that for panel data, OLS standard errors be corrected for clustering on the. The case in which coefficients are equal across groups. 0 1250 In contrast, pooled cross sectional data is multiple snapshots of multiple bunches of (randomly selected). Pooled Variance b) Pool variances, accounting for sample size yWeighted average of the two estimates of variance – one from each sample – that are calculated when condi idpd pl ducting an independent samples t test. Panel data (also known as longitudinal or cross -sectional time-series data) is a dataset in which the behavior of entities are observed across time. Causal Interpretations vs Predictive Applications 2. Interpreting Regression Results. Example: Pooled OLS estimates in crime rate regression d =93 42 (12 74) +7 94 (7 98) × 87 + 427 (1 188) × =92(46 x 2), 2 =0 012 • unemp is not signiﬁcant in pooled regression • It is likely that unemp is endogenous; e. A comprehensive resource funded by the ESRC to support researchers, teachers and policymakers who depend on high-quality social and economic data. Pooled Cross-Section w/City Fixed Effects Notes: Heteroskedasticity-Robust Standard errors in Parentheses. Ask Question Asked 3 years, 6 months ago. The residual versus income plot shows clear evidence of heteroscedasticity. The estimator is obtained by running a pooled OLS estimation for a regression of. , has been frequently employed in the Framingham Heart Study. This is the key reason why pooled cross sections are di⁄erent from panel data. The results of almost all Stata commands can be bootstrapped immediately, and it's relatively straightforward to put any other results you've calculated in a form that can be bootstrapped. est store m_stata. Here, analysis of multiply imputed data is achieved by commands that start with mi. draws from joint distribution Assumption 3:Large outliers are unlikely Under these three assumption the OLS estimators are unbiased, consistent and normally distributed in large samples. * The Kao & Chiang (2000) Dynamic OLS (DOLS) estimator for Cointegrated Panel Data with homogeneous covariance structure was recently coded in Stata by Diallo Ibrahima Amadou (CERDI) as xtdolshm; you will also need to install ltimbimata. Pooled and Panel Data Analysis 1 Topics Pooled Data Example -Stata & R 6 •Suppose we have a pooled data with information for the variable bias). Panel Data Models Example. It builds upon a solid base of college algebra and basic concepts in probability and statistics. Introduction into Panel Data Regression Using Eviews and stata Hamrit mouhcene University of khenchela Algeria [email protected] POOLED OLS. 's (or "i options" as Joe Glass called it) have a name. Basics of Panel Data Models 4. al (2010); Eq. This article explains how to perform pooled panel data regression in STATA. Cointegration Vector Estimation by Panel DOLS and Long-run Money Demand* Nelson C. Simulations, Econometrics, Stata, R,intelligent mulit-agent systems, Psychometrics, latent modelling, maximization, statistics, quantitative methods. Multiple linear regression analysis is an extension of simple linear regression analysis, used to assess the association between two or more independent variables and a single continuous dependent variable. I know that by clustering the standard errors at CompanyID will take into account the panel structure. Introduction. of California - Davis Prepared for West Coast Stata Users'Group Meeting Based on A. Conclusions. 混合OLS模型、固定效应模型、随机效应模型的区别是什么 关键词：混合ols 固定效应,固定效应还是混合ols,stata随机效应模型,面板数据随机效应模型 混合OLS模型、固定效应模型、随机效应模型的区别是什么？. 0) Dựa vào bảng 4. Econometric Methods for Panel Data University of Vienna and Institute for Advanced Studies Vienna. x y y' y-y' (y-y') 2 1. Hierarchical modeling seems to be very rare. $\endgroup$ – Arthur Tarasov Nov 25 '15 at 5:40. Under this sampling scheme, the observations form different time periods are pooled together and OLS is conducted on the pooled sample. xtscc produces Driscoll and Kraay (Rev. First, we'll compute a pooled OLS model for the years 1990 and 1993: By using pooled OLS, we are disregarding the term in the regression equation above. then pooled OLS is a consistent and efﬁcient estimator of [ ]. Furthermore, as Pesaran & Smith (1995) argue in the context of OLS regressions, when the true slope coefﬁcients are heterogeneous, group mean. 000 a = 10%, do đó, với mức ý nghĩa 10% ta bác bò Ho. My decision. I think that would have then the same parameterization as a pooled OLS, including the constant, and I think would also correspond to the random effects model. Dear Statalists, I am struggling with choosing firm or industry fixed-effect for my regression with unbalanced panel data of around 800 firms of 48 industry. I have already performed xtreg, fe, and the F tests indicated that Pooled OLS is the better option. • The fixed-effects estimator is a straightforward application of OLS, and has the usual. Namun, untuk mendapatkan parameter a dan b yang konstan dan efisien, hanya dapat diperoleh dalam bentuk regresi yang lebih besar dan melibatkan sebanyak NT observasi. It is important to note that the temporal dimension. ) Terlihat terima H 0 berti model terpilih adalah common/pooled effect model. This article has also been viewed 2,124,979 times. If you need to open a Stata 10 file on a Stata 9 machine, you can: o Use Stat/Transfer to convert the file between versions OR. Ordinary Least Squares regression, often called linear regression, is available in Excel using the XLSTAT add-on statistical software. 61–71 Estimating panel time-series models with heterogeneous slopes Markus Eberhardt School of Economics University of Nottingham Nottingham, UK markus. ECON 407: Companion to Panel Data Models This document, a companion to the Panel Data series of lecture notes, provides a brief description of how to implement panel data models in both R and Stata. In panel data analysis (the analysis of data over time), the Hausman test can help you to choose between fixed effects model or a random effects model. Potential sources of bias after DiD. Both RE and OLS rely on the same assumptions for consistency, so if your test rejects RE it will likely reject OLS. The LSDV results seem fits better than the pooled OLS. Dear Statalist, 1) I have a equation as this: y=a+b1*X1+b2*X2+b3*X3++ c*T + error, where a, b, c are coefficients; 2) Y is a couple of dependent. xtreg yvar xvar, i(-rm) fe Rather than time demeaning. We focus on OLS, for simplicity and because this is the most commonly-used estimation method in practice. There are a number of benefits to using do-files. 1998) standard errors for coefficients estimated by pooled OLS/WLS or fixed-effects (within) regression. Apply the formula In the following example, we assume that two groups have difference slopes of cost and. pdf), Text File (. r/stata: Stata news, code tips and tricks, questions, and discussion! We are here to help, but won't do your homework or help you pirate software. In statistics and econometrics, panel data or longitudinal data are multi-dimensional data involving measurements over time. Our F statistic is 9. Mit der offiziellen Frankfurt Airport App können Sie Ihre Reise vom Frankfurter Flughafen entspannter und komfortabler als jemals zuvor gestalten. This set of assumptions is usually labelled pooling model, although this actually refers to the errors’ properties and the appropriate estimation method rather than the model itself. gioi thieu 3 phương phap uoc luong mo hinh du lieu bang: pooled ols, tac dong co dinh (fixed effects), tac dong ngau nhien (random effects) cung voi kiem dinh Hausamn, Breusch-Pagan. 1 Pooled OLS. first -difference command on STATA. As political scientists have. I am now struggling a lot on to how I should specify this pooled OLS. * The Kao & Chiang (2000) Dynamic OLS (DOLS) estimator for Cointegrated Panel Data with homogeneous covariance structure was recently coded in Stata by Diallo Ibrahima Amadou (CERDI) as xtdolshm; you will also need to install ltimbimata. The result explanation of the analysis is same as the OLS, MLE or WLS method. Keywords: st0001, parameter heterogeneity, ﬁxed eﬀects, pooled OLS, mean-group estimator, cross section dependence 1 Introduction Today, panel data is widly used for empirical studies in several research areas and the beneﬁts of panel data are well known (Baltagi 2013, p. The first-difference (FD) estimator is an approach used to address the problem of omitted variables in econometrics and statistics with panel data. Performing pooled panel data regression in STATA Projectguru. Random Effects (RE) Model with Stata (Panel) The essential distinction in panel data analysis is that between FE and RE models. "XTSCC: Stata module to calculate robust standard errors for panels with cross-sectional dependence," Statistical Software Components S456787, Boston College Department of Economics, revised 03 Apr 2018. Panel data (also known as longitudinal or cross -sectional time-series data) is a dataset in which the behavior of entities are observed across time. Compare the results of these analyses. Measures of effect size in Stata 13. Stata's RE estimator is a weighted average of fixed and between effects. It's free to sign up and bid on jobs. 下面使用 Stata 提供的一个虚构数据集 xtcoint. 0) Dựa vào bảng 4. Be careful about models and default assumptions in these commands. • Panel methods over OLS to exploit OR remove unobserved heterogeneity. 100% of toll: 0. I have a sample of 94 elements and a time horizon of 5 years,a dependent variable (94x5) and 6 independent variables (94x5). Assuming iid errors and applying OLS we get consistent estimates, if E( it) = 0 and E(xit it) = 0, if the xit are weakly exogenous. But is it always. Este modelo se expresa como: (1) Donde i significa la i-ésima unidad transversal (estado) y t el tiempo t (año).
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# How do you solve 288 = 104x - 8x^2?

Jul 2, 2015

Solve $y = 8 {x}^{2} - 104 x + 288 = 0$
$y = 8 \left({x}^{2} - 13 x + 36\right) = 8 g \left(x\right) = 0.$ Solve g(x)

format: plain


text:
G13 Chapter Contents
G13 Chapter Introduction
NAG Library Manual

# NAG Library Routine DocumentG13BDF

Note:  before using this routine, please read the Users' Note for your implementation to check the interpretation of bold italicised terms and other implementation-dependent details.

## 1  Purpose

G13BDF calculates preliminary estimates of the parameters of a transfer function model.

## 2  Specification

 SUBROUTINE G13BDF ( R0, R, NL, NNA, S, NWDS, WA, IWA, WDS, ISF, IFAIL)
 INTEGER NL, NNA(3), NWDS, IWA, ISF(2), IFAIL REAL (KIND=nag_wp) R0, R(NL), S, WA(IWA), WDS(NWDS)

## 3  Description

G13BDF calculates estimates of parameters ${\delta }_{1},{\delta }_{2},\dots ,{\delta }_{p}$, ${\omega }_{0},{\omega }_{1},\dots ,{\omega }_{q}$ in the transfer function model
 $yt=δ1yt-1+δ2yt-2+⋯+δpyt-p+ω0xt-b-ω1xt-b-1-⋯-ωqxt-b-q$
given cross-correlations between the series ${x}_{t}$ and lagged values of ${y}_{t}$:
 $rxyl, l=0,1,…,L$
and the ratio of standard deviations ${s}_{y}/{s}_{x}$, as supplied by G13BCF.
It is assumed that the series ${x}_{t}$ used to calculate the cross-correlations is a sample from a time series with true autocorrelations of zero. Otherwise the cross-correlations between the series ${b}_{t}$ and ${a}_{t}$, as defined in the description of G13BAF, should be used in place of those between ${y}_{t}$ and ${x}_{t}$.
The estimates are obtained by solving for ${\delta }_{1},{\delta }_{2},\dots ,{\delta }_{p}$ the equations
 $rxyb+q+j=δ1rxyb+q+j-1+⋯+δprxyb+q+j-p, j=1,2,…,p$
then calculating
 $ωi = ± sy / sx rxy b+i - δ1 rxy b+i- 1 - ⋯ - δp rxy b+i-p , i= 0,1,…,q$
where the ‘$+$’ is used for ${\omega }_{0}$ and ‘$-$’ for ${\omega }_{i}$, $i>0$.
Any value of ${r}_{xy}\left(l\right)$ arising in these equations for $l is taken as zero. The parameters ${\delta }_{1},{\delta }_{2},\dots ,{\delta }_{p}$ are checked as to whether they satisfy the stability criterion.

## 4  References

Box G E P and Jenkins G M (1976) Time Series Analysis: Forecasting and Control (Revised Edition) Holden–Day

## 5  Parameters

1:     R0 – REAL (KIND=nag_wp)Input
On entry: the cross-correlation between the two series at lag $0$, ${r}_{xy}\left(0\right)$.
Constraint: $-1.0\le {\mathbf{R0}}\le 1.0$.
2:     R(NL) – REAL (KIND=nag_wp) arrayInput
On entry: the cross-correlations between the two series at lags $1$ to $L$, ${r}_{xy}\left(\mathit{l}\right)$, for $\mathit{l}=1,2,\dots ,L$.
Constraint: $-1.0\le {\mathbf{R}}\left(\mathit{i}\right)\le 1.0$, for $\mathit{i}=1,2,\dots ,{\mathbf{NL}}$.
3:     NL – INTEGERInput
On entry: $L$, the number of lagged cross-correlations in the array R.
Constraint: ${\mathbf{NL}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{NNA}}\left(1\right)+{\mathbf{NNA}}\left(2\right)+{\mathbf{NNA}}\left(3\right),1\right)$.
4:     NNA($3$) – INTEGER arrayInput
On entry: the transfer function model orders in the standard form $b,q,p$ (i.e., delay time, number of moving-average MA-like followed by number of autoregressive AR-like parameters).
Constraint: ${\mathbf{NNA}}\left(\mathit{i}\right)\ge 0$, for $\mathit{i}=1,2,3$.
5:     S – REAL (KIND=nag_wp)Input
On entry: the ratio of the standard deviation of the $y$ series to that of the $x$ series, ${s}_{y}/{s}_{x}$.
Constraint: ${\mathbf{S}}>0.0$.
6:     NWDS – INTEGERInput
On entry: the exact number of parameters in the transfer function model.
Constraint: ${\mathbf{NWDS}}={\mathbf{NNA}}\left(2\right)+{\mathbf{NNA}}\left(3\right)+1$.
7:     WA(IWA) – REAL (KIND=nag_wp) arrayWorkspace
8:     IWA – INTEGERInput
On entry: the dimension of the array WA as declared in the (sub)program from which G13BDF is called.
Constraint: ${\mathbf{IWA}}\ge {\mathbf{NNA}}\left(3\right)×\left({\mathbf{NNA}}\left(3\right)+1\right)$.
9:     WDS(NWDS) – REAL (KIND=nag_wp) arrayOutput
On exit: the preliminary estimates of the parameters of the transfer function model in the order of $q+1$ MA-like parameters followed by the $p$ AR-like parameters. If the estimation of either type of parameter fails then these parameters are set to $0.0$.
10:   ISF($2$) – INTEGER arrayOutput
On exit: indicators of the success of the estimation of MA-like and AR-like parameters respectively. A value $0$ indicates that there are no parameters of that type to be estimated. A value of $1$ or $-1$ indicates that there are parameters of that type in the model and the estimation of that type has been successful or unsuccessful respectively. Note that there is always at least one MA-like parameter in the model.
11:   IFAIL – INTEGERInput/Output
On entry: IFAIL must be set to $0$, $-1\text{ or }1$. If you are unfamiliar with this parameter you should refer to Section 3.3 in the Essential Introduction for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value $-1\text{ or }1$ is recommended. If the output of error messages is undesirable, then the value $1$ is recommended. Otherwise, if you are not familiar with this parameter, the recommended value is $0$. When the value $-\mathbf{1}\text{ or }\mathbf{1}$ is used it is essential to test the value of IFAIL on exit.
On exit: ${\mathbf{IFAIL}}={\mathbf{0}}$ unless the routine detects an error or a warning has been flagged (see Section 6).

## 6  Error Indicators and Warnings

If on entry ${\mathbf{IFAIL}}={\mathbf{0}}$ or $-{\mathbf{1}}$, explanatory error messages are output on the current error message unit (as defined by X04AAF).
Errors or warnings detected by the routine:
${\mathbf{IFAIL}}=1$
 On entry, ${\mathbf{NNA}}\left(\mathit{i}\right)<0$, for $\mathit{i}=1,2,3$, or ${\mathbf{NL}}<\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{NNA}}\left(1\right)+{\mathbf{NNA}}\left(2\right)+{\mathbf{NNA}}\left(3\right),1\right)$, or ${\mathbf{R0}}<-1.0$ or ${\mathbf{R0}}>1.0$, or ${\mathbf{R}}\left(i\right)<-1.0$ or ${\mathbf{R}}\left(i\right)>1.0$, for some $i=1,2,\dots ,{\mathbf{NL}}$, or ${\mathbf{S}}\le 0.0$, or ${\mathbf{NWDS}}\ne {\mathbf{NNA}}\left(2\right)+{\mathbf{NNA}}\left(3\right)+1$, or ${\mathbf{IWA}}<{\mathbf{NNA}}\left(3\right)×\left({\mathbf{NNA}}\left(3\right)+1\right)$.
${\mathbf{IFAIL}}=-999$
Internal memory allocation failed.

## 7  Accuracy

Equations used in the computations may become unstable, in which case results are reset to zero with array ISF values set accordingly.

If ${\mathbf{NNA}}\left(3\right)>0$,a local workspace array of fixed length is allocated internally by G13BDF. The total size of this array amounts to ${\mathbf{NNA}}\left(3\right)$ integer elements and ${\mathbf{NNA}}\left(3\right)×\left({\mathbf{NNA}}\left(3\right)+1\right)$ real elements.
The time taken by G13BDF is roughly proportional to ${{\mathbf{NWDS}}}^{3}$.

## 9  Example

This example reads the cross-correlations between two series at lags $0$ to $6$. It then reads a $\left(3,2,1\right)$ transfer function model and calculates and prints the preliminary estimates of the parameters of the model.

### 9.1  Program Text

Program Text (g13bdfe.f90)

### 9.2  Program Data

Program Data (g13bdfe.d)

### 9.3  Program Results

Program Results (g13bdfe.r)


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## Compute the characteristic polynomial of a companion matrix

Let $p(x) = \sum_{i=0}^n c_i x^i$ be a monic polynomial (i.e. $c_n = 1$) and let $A = [a_{i,j}]$ where $a_{i,j} = -c_{i-1}$ if $j = n$, 1 if $i = j+1$, and 0 otherwise. Show that $c_A(x) = p(x)$.

Recall that $c_A(x) = \mathsf{det}(xI - A) = \mathsf{det}([x\delta_{i,j} - a_{i,j}])$, where $\delta_{i,j} = 1$ if $i = j$ and 0 otherwise.

Using the Cofactor Expansion Formula along the $n$th column (derived here), we have $\mathsf{det}(xI-A) = \sum_{i=1}^n (-1)^{i+n} (x \delta_{i,n} - a_{i,n}) \mathsf{det}(M_{i,n})$, where $M_{i,n}$ denotes the $(i,n)$ matrix minor of $xI - A$.

Evidently, we have that $(M_{k,n})_{i,j} = x$ if $i = j < k$, $-1$ if $i = j \geq k$, $-1$ if $i = j+1$ and $i < k$, $x$ if $i = j-1$ and $i \geq k$, and 0 otherwise. So $M_{k,n} = \begin{bmatrix} L & 0 \\ 0 & U \end{bmatrix}$, where $L$ is lower triangular of dimension $k-1$ and diagonal entries equal to $x$ and $U$ is upper triangular of dimension $n-k$ and diagonal entries equal to $-1$. So we have $\mathsf{det}(M_{k,n}) = (-1)^{n-k}x^{k-1}$.

So we have $c_A(x) = \sum_{i=1}^n (-1)^{i+n} (x \delta_{i,n} - a_{i,n}) (-1)^{n-i} x^{i-1}$ $= \sum_{i=1}^n (-1)^{2n} (x\delta_{i,n} - a_{i,n}) x^{i-1}$ $= x^n + \sum_{i=1}^n (-a_{i,n})x^{i-1}$ $= x^n + \sum_{i=1}^n c_{i-1}x^{i-1}$ $= x^n + \sum_{i=0}^{n-1} c_ix^i$ $= p(x)$ as desired.

• Marc van Leeuwen  On November 4, 2011 at 5:11 am

This proof is incorrect, since $M_{k,n}$ is clearly not upper triangular for $k>1$. Instead it is block-triangular (i.e., of 2×2 block form with the lower left block null and touching the diagonal) with the top-left block, square of size $k-1$, being LOWER triangular with $x$’s on the diagonal, and the bottom right block of siwe $n-k$ being upper triangular with $-1$’s on the diagonal.

• nbloomf  On November 4, 2011 at 8:17 am

Thanks! I think it is fixed now.


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# Input capacitor for LED driver circuit

I'm planning to power the circuit from a micro usb charger rated 1.8A. The peak current for the circuit is 1.5A but if the charger is not providing enough current I can use PWM with e.g. a 50% duty cycle, then the average current should be 0.75A (half of 1.5A).

I suppose to average out the current so that to the supply it looks like a constant 0.75A I need a capacitor. Provided the PWM is at approx 1.5KHz with 320uS ON and 320uS OFF and the voltage is 5V what type and value of a capacitor should I use on the input (preferably a smaller size)?

• I don't understand why a 1.8 A rated charger can't supply a peak current of 1.5 A. But it's probably because I can't parse your wording. Regarding a capacitor, $C = I \tfrac{\Delta t}{\Delta V}$. $\Delta t$ is your OFF time, $\Delta V$ is the allowable voltage droop, and $I$ is the average current you expect drawn during this time. – jonk Nov 10 '16 at 17:47
• Thanks! It says 1.8A, but I'm not sure if it can actually deliver that. – axk Nov 10 '16 at 18:08
• Okay. So that makes more sense. I'll provide a very short discussion as an answer, then. I'd still like to know why the 50% PWM, though. Is it ONLY because of your worries about the power source? – jonk Nov 10 '16 at 18:09
• Strictly speaking I need a power supply that can provide the required current but if the one I have cannot handle the load and in absence of a more powerful supply using the LED drivers dimming through PWM can reduce the average current, that was my reasoning. – axk Nov 10 '16 at 18:14
• How are you going to PWM the circuit? It's one thing to just plop down a capacitor. Do you have a mechanism for that aspect, already? – jonk Nov 10 '16 at 18:18

Some entering notes:

• You have a power supply that you doubt can supply full current. Since you are want to continue using this supply (for purposes of discussion), you've decided to PWM your LED circuit at a duty cycle that is less than 100%.
• You want to select a capacitor that will provide adequate current.
• You have an IC on the LED side which you can PWM, already.

Rough behavioral model might look like:

simulate this circuit – Schematic created using CircuitLab

You are worried that $R_s$ is large enough that the voltage from the USB power source will droop too much under full load. You'd like to add a capacitor to supply additional current to your LED controller IC and PWM so as to lower the average current draw to something the USB power supply can handle.

The equation for the capacitor, in general, is:

$$C = I_C \frac{\Delta t}{\Delta V_C}$$

In this case, your USB power source is continually supplying current to the IC and to $C_1$. But your controller is, we assume, pulling more current than the USB source can provide during the ON times.

A minor problem is that you don't know $R_s$, but are just worried about it. In reality, the USB power source will be supplying current (some) all the time, even while the LED controller is ON. Just not enough.

So let's assume it can supply half the current needed during ON times. Then during this time the current from the capacitor will also be half. You've mentioned that you want to be certain of $1.5\:\textrm{A}$. So this gives us a value for $I_C$ when the LED controller is ON: $I_C=750\:\textrm{mA}$. With a PWM frequency of $1.5\:\textrm{kHz}$ and a duty cycle of 50%, the ON period will be $\tfrac{1}{3}\:\textrm{ms}$. So we have an approximation for the period where the capacitor is supplying current and its voltage starts drooping: $\Delta t=\tfrac{1}{3}\:\textrm{ms}$. The only remaining problem is the allowable droop of the voltage at the LED controller IC. Let's assume that this should be no worse than $200\:\textrm{mV}$. (You can choose other acceptable values, though.)

Then the capacitor should be:

$$C = 750\:\textrm{mA} \frac{\frac{1}{3}\:\textrm{ms}}{200\:\textrm{mV}}= 1250\:\mu\textrm{F}$$

Make it the next size bigger, I suppose.

• It seems that if PWM frequency is increased 10 times, then the required capacitor will be 10 times smaller. E.g. 15 kHz PWM will need a 125 μF cap. Is that correct? – Gene Pavlovsky Dec 17 '20 at 23:36
• @GenePavlovsky Yes. If the PWM frequency is higher, then the capacitor will discharge for less time between recharges and, for a given allowable "voltage droop," smaller capacitors can be used. Your calculation looks about right to me, given no change in the other parameters. But this is more than 4 yrs ago so I may be given this short shift. – jonk Dec 18 '20 at 3:25


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# nLab prismatic cohomology

cohomology

### Theorems

#### $(\infty,1)$-Topos Theory

(∞,1)-topos theory

## Constructions

structures in a cohesive (∞,1)-topos

# Contents

## Idea

A type of cohomology attached to prisms, which are $\delta$-rings equipped with an ideal satisfying some conditions. (The pair $(A, I)$ is a prism when $I$ is an ideal of a $\delta$-ring $A$ defining a Cartier divisor on its spectrum $Spec(A)$ such that $A$ is derived $(p,I)$-complete, and $p \in I + \phi(I)A$.)

Roughly, it is a unified construction of various $p$-adic cohomology theories, including étale cohomology, de Rham cohomology and crystalline cohomology, as well as the so far conjectural $q$-de Rham cohomology of Peter Scholze.


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# Help in Binary logistic regression

I am doing an analysis of my big data on diabetic patients (n= 168615). The dependent variable is HbA1c blood test. I want to run binary regression on two groups of the dependent variable (HbA1c ≤ 7% coded Zero OR HbA1c > 7% coded One). The predictors are: weekly average maximum weather temperature (continuous) and the categories of the weekly average maximum weather temperature (High temperature coded 1, moderate temperature coded 2, and low temperature coded 3).

NOW: when I run the analysis for each predictor separately, I found statistically significant. But when I run the regression that predictor section contains both (continuous and categorical variables) some of them were not statistically significant. The attached photos are for what I have done: 1- Regression of weekly average maximum weather temperature (continuous) predictor alone. 2- Regression of the categories of weekly average maximum weather temperature predictor alone. 3- Regression of combined both (continuous and categorical variables).

So, please if anyone can help me in this analysis to examine if the model fit or not, which the right one I have done, and how to interpret the results. I would appreciate for any little help.

• What is the purpose of this analysis? Are you just trying to predict the outcome from temperature? Are you trying to estimate a specific parameter? Are you trying to build a causal model? Are you trying to describe a relationship? We need this information to be able to give you a good answer. – Noah Jun 13 at 0:20
• Thank you some much for replying. I need to see if there is an effect of weather temperature on HbA1c level (relationship) and then to be able to predict. – ph-abdallah Jun 13 at 0:23

First, a quick remark: plese stop using the buzz-word "big data" with $$n= 168615$$ I don't know if you expect to have trillions of patients in the future, but for now, this does not look like a big data problem.
Now to the actual question: statistical significance is nothing more than an arbitrary threshold put at $$\alpha=0.05$$, but there is nothing special about that $$5\%$$, so I wouldn't worry too much. What I would make sure of is that the performance of the multi-feature model is better.


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# Is this a valid proof of $f(S \cap T) \subseteq f(S) \cap f(T)$?

$f(S \cap T) \subseteq f(S) \cap f(T)$

Suppose there is a $x$ that is in $S$, but not in $T$, then there is a value $y$ such that $f(x) = x$, that is in $f(S)$, but not in $f(S \cap T)$. Suppose there is a $x$ that is in $T$, but not in $S$, then there is a value $y$ such that $f(x) = x$ that is in $f(T)$, but is not in $f(S \cap T)$.

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No. If $x$ is in $S$ but not $T$, and $y$ is in $T$ but not in $S$, it's possible there are $z$ and $w$ in $S\cap T$ such that $f(z)=f(y)$ and $f(w)=f(x)$. – Gerry Myerson Nov 4 '12 at 2:37
You might want to begin your proof with this: Let $y \in f(S \cap T)$ so $y = f(x)$ for some $x \in$ ... – wj32 Nov 4 '12 at 2:46
ahh im too confused now – Gladstone Asder Nov 4 '12 at 2:49
Please, try to make the title of your question more informative. E.g., Why does $a<b$ imply $a+c<b+c$? is much more useful for other users than A question about inequality. From How can I ask a good question?: Make your title as descriptive as possible. In many cases one can actually phrase the title as the question, at least in such a way so as to be comprehensible to an expert reader. – Martin Sleziak Nov 4 '12 at 6:50

No. In general, to show that $A\subset B$, it is not sufficient to show that there is $b\in B$ so that $b \notin B$. Take $A=\{1,2\}$ and $B=\{2,3\}$. Then $3 \notin A$ but $B\not \subset A$. You need to show that for every $a\in A$, $a\in B$ to show that $A\subset B$. I hope that helps.

-

What if there's an element that is in the $S \cap T$ that also maps to $y$? In other words, what if $f$ isn't injective? I honestly stopped reading there.

Usually when showing that a set is a subset of another, you want to take a direct approach. I'm not saying that a contradiction wouldn't work, and I believe that in more complicated situations it would, but for simple properties like this, you should try a direct approach first:

I'll be informal (or at least not use symbols), so you can seen if you understand the proof and write it yourself.

You pick an element in the set on the left, and you see that its preimage sits in $S\cap T$. This just follows from the definition of the preimage. So the preimage sits in both $S$ and $T$. Now apply $f$ to the elements in the preimage, what do you get?

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i just had to add such that f(x) = x, right? – Gladstone Asder Nov 4 '12 at 2:58
No, that doesn't make sense to me. You need to start with an element in $f(S \cap T)$, not with an element in $S$ or $T$. Try to see if you can prove it with a straightforward approach (i.e. direct proof and not a contradiction). Once you understand it, play around with the proof and see if you can prove it with a contradiction. I honestly haven't tried doing it using a contradiction, but if I were to do so, I don't think it'd work the way you started it. – eliya gwetta Nov 4 '12 at 3:02

How about $S = \{1,\ 2,\ 3\}$ and $T = \{2,\ 3,\ 4\}$ with $f$ defined as $$f(1) = 1,\ f(2) = 1,\ f(3) = 2,\ f(4) = 2$$ Then $1\notin T$ but every value in $f(S) = \{1,\ 2\}$ is in $f(S\cap T) = \{1,\ 2\}$. Similarly with $4\notin S$.

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# I need help with this chem question

1. Apr 7, 2004

### poopflinger15

Gaseous oxides of nitrogen rarely occur as pure substances, but as equilibrium mixtures of them. One of these equilibrium mixtures is nitrogen dioxide and dinitrogen tetroxide. If the density of such a mixture is 2.3 g/l at 74 degrees Celsius and 1.3 atm, calculate the partial pressures of the gasses and Kp for the dissociation of dinitogen tetroxide to nitrogen dioxide.

2. Apr 8, 2004

### himanshu121

Let the volume be 1L Then total mass of the system will be

$$n_1*M + 2n_2*M = 2.3 g$$
Where
n1 : no of mol of NO2
n1 : no of mol of N2O4
M : Molecular mass of NO2

Also PV=(n1+n2)RT

So u have two unknown and two equations u can find n1 & n2 from it And Hence Kp or partial pressure

3. Apr 8, 2004

### GCT

Using PV=nRT, since you know P and T, you can solve for the solution in the form of moles per liter. For the rest of the problem we will assume a volume of one liter (it does not matter either way since it is only a matter of proportion). Since you have the grams pertaining to this situation you essentially have the "average molar mass of this situation." That is 2.3 grams/the total number of moles (which you are to figure out).

[molar mass of the nitrogen compound (grams/moles) X N1 (number of nitrogen compound)] + [molar mass of the dinitrogen compound (grams/moles) X N2 (number of the dinitrogen compound)]

all of this divided by 2

Solve for the proportion N1/N2, that is proportional to the moles.

Notice however, that the Kp equation for the reaction pertaining to the addition of the nitrogen to form the dinitrogen should have an exponent of 2 on the pressure of the nitrogen compound; we know that according to the stochiometric equation that there are two moles of the nitrogen compound for every dinitrogen compound.:

Kp = [partial pressure of dinitrogen compound p]/[partial pressure of nitrogen compound]^2

Note also that you will need to convert moles into pressure because of the presence of an exponent (with the assumption of 1 L as the volume, volume is insignificant information in this problem).

--------------
Online help with college and high school chemistry
http://groups.msn.com/GeneralChemistryHomework [Broken]

Last edited by a moderator: May 1, 2017
4. Apr 8, 2004

### thunderfvck

There's nothing quite like a lungful of NO2 in the morning. It really gives your day a kick.


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# Non-Standard Matches and Charitable Giving

 Title: Non-Standard Matches and Charitable Giving Author: Sanders, Michael; Smith, Sarah; Norton, Michael Irwin Note: Order does not necessarily reflect citation order of authors. Citation: Sanders, Michael, Sarah Smith, and Michael I. Norton. "Non-Standard Matches and Charitable Giving." Harvard Business School Working Paper, No. 13–094, May 2013. Full Text & Related Files: 13-094.pdf (102.7Kb; PDF) Abstract: Many organisations, including corporations and governments, wish to encourage charitable giving, and offer incentives for their employees, customers and citizens to do so. The most common of these incentives is a match rate, where the organisation agrees to pay, for example, $1 for every$1 donated. However, these incentives may not be efficient. In this short article we suggest alternative ways of matching that existing theory and data suggest might be more effective at encouraging donations. These include non-linear matching, social (and team) matching, and lottery matching – each of which novel schemes could be tested empirically against a standard match incentive. Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:10646421 Downloads of this work:


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# Central limit theorem with unknown variance

In my experiment I compute the average latency of operations per second. I would like to define N, i.e: how many times do I need to run my experiment to compute a close-to-real average latency?

I figured that I could apply the CLT here, because If I repeat the same experiment 1000 times and plot a histogram, I get a normal distribution curve. Is the central theorem useful in my case? In the definitions I found, to be able to compute an estimated mean with a certain error, one needs to know the variance beforehand, and I don't know it.

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This is more ore less textbook stuff. The key word here is t-statistic. Central limit theorem is certainly useful, if your latency has finite variance. If it does not, then it becomes trickier. –  mpiktas Nov 22 '12 at 13:28
You do need to know how close is close enough in regards to "real average latency" to even approach this question. Usually what we do in these cases is estimate the population variance using the sample estimate of the population variance (usually just called sample variance). –  rpierce Jul 24 '13 at 0:00

Perhaps you can bound your variance. Suppose, for example, that you know your data must be in the range $[a,b]$. Then Popoviciu's inequality bounds your variance by $\sigma^2 \le (1/4)(b-a)^2$. Using the upper bound in the formulas you found will be a bit of overkill, but it should satisfy your requirements.

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CTL is all about independent and identically distributed (i.i.d.) random variables, with finite mean and variance. I edit the answer just to add that you don't have to know your parameter , but be sure that this parameter is finite and identical along your runs.

In order to estimate the parameter mean with unknownk variance you can build an interval using the t-student as

$$\bar X - t_{n-1,1-\alpha} \frac{S}{\sqrt{n}}$$

where $t$ is the value of a t-student with $n-1$ degrees of freedom with $1-a$ confidence level.

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Do you have any pointer on how to calculate the number of experiment to have 95 accuracy without knowing the variance? thanks –  Djellel Eddine Nov 22 '12 at 13:16
-1, Answers should be answers, this is more like an unfinished comment. Please elaborate more. –  mpiktas Nov 22 '12 at 13:31


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# Find Eigenvalue/Eigenvector

1. Apr 22, 2013

### dkotschessaa

1. The problem statement, all variables and given/known data

If invertible matrices A and B have eigenvalues α and β resp. wrt to a common eigenvector x, what is the eigenvalue of

4A3B-4-17AB2 + ∏I

(We'll call this equation (1))

2. Relevant equations

Ax = αx
Bx = βx

3. The attempt at a solution

I think I'm ok for the actual eigenvalue. Basically we "exchange" α for A and β for B in the equation above, since Ax = αx implies A2x = α2x and so on and so forth. ∏I is just an identity matrix multiplied by ∏, so it's eigenvalue is ∏ (with multiplicity two).

So I get 4α24 - 17αβ2 + ∏

But I can't seem to figure out how to approach getting the corresponding Eigenvector. I know the characteristic equation

det(A - λI) = 0

may tell me something, but if I plug in (1) for A and the eigenvalue above for λ I just get a complete mess.

-Dave K

2. Apr 22, 2013

### Dick

You can't really find the eigenvector unless you have actual values for the A and B matrices. Do you?

3. Apr 22, 2013

### I like Serena

Hi Dave!

Let's call C = 4A3B-4-17AB2 + ∏I
Then, as you said, Cx = (4α24 - 17αβ2 + ∏)x

Let's call λ=4α24 - 17αβ2 + ∏.
Then Cxx.

What do you think the corresponding eigenvector is?

4. Apr 23, 2013

### dkotschessaa

I did try that but I'm not sure how to "solve" for x. As the other poster pointed out, I don't even know what the values of C are... I don't even know what dimension so I can't use some identity matrix and use λ (the mess) as a scalar... Not sure what I'm supposed to do.

5. Apr 23, 2013

### Dick

You can't solve for x any more than you can solve for α and β. x is the eigenvector. I think you are done.

6. Apr 23, 2013

### I like Serena

The vector x is given as the common eigenvector of A and B.
None of them is specified other than that they are given to exist.
C has the same common eigenvector x as A and B.

7. Apr 24, 2013

### dkotschessaa

Yeah, we went over this in class, and the eigenvector is just x. Ok then. Thanks everyone.