![]() This returns the following: Source | SS df MS Number of obs = 406 The following syntax runs the regression. pct_white ( independent variable): The percent of white voters in a given Congressional districtĪll three variables are measured as percentages ranging from zero to 100.mshare ( independent variable): The percent of social media posts for a Republican candidate.vote_share ( dependent variable): The percent of votes for a Republican candidate.The replication data in Stata format can be downloaded from our github repo. ![]() The data used in this post come from the More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior study from DiGrazia J, McKelvey K, Bollen J, Rojas F (2013), which investigated the relationship between social media mentions of candidates in the 20 US House elections with actual vote results. The details of the underlying calculations can be found in our multiple regression tutorial. The below results will appear.This tutorial shows how to fit a multiple regression model (that is, a linear regression with more than one independent variable) using Stata. In the above command, ‘LTD’ is the variable, and “ xtunitroot llc” is the syntax. Test the unit root for the panel data using the Leuin-lin-Chu test using the below command. It is not possible to perform a stationarity test in the case of panel data using the augmented Dicky Fuller test. To start with panel data regression, ensure the absence of a unit root problem since this data also carries time dimensions. Resultantly, the pooled regression technique is obsolete for this dataset and therefore move towards either fixed or random effects panel data regression. Moreover, the regression analysis of this data may carry some sort of fixed effects. Therefore the panel data set here carries the variables due to the distinction between the companies. It thus confirms the fact that pooled regression is not free from the joint effects of dummies. However, the results suggest p values equal to 0.000 which indicates that the null hypothesis can be rejected. Therefore, the effects of the alternative coming from variations in data due to the distinction of companies do not affect this model. ![]() Here, the null hypothesis suggests that the joint effect of all the dummies is zero. The results will appear.įigure 3: Results of the joint hypothesis of dummies for pooled panel data regression in STATA Create the dummies for each of the companies using this variable. Note that the “compnam” is the panel data variable. To make the dummies for all 30 companies, use the below command: tabulate compnam, gen(companies) In this case, it is the companies from the previous article (Introduction to panel data analysis in STATA). In order to start with pooled regression, first, create dummies for all the cross-sectional units. This article explains how to perform pooled panel data regression in STATA. The underlying assumption in pooled regression is that space and time dimensions do not create any distinction within the observations and there is no set of fixed effects in the data. In this, a usual OLS regression helps to see the effect of independent variables on the dependent variables disregarding the fact that data is both cross-sectional and time series. Saptarshi Basu Roy Choudhury & Priya Chetty on October 30, 2018īefore applying panel data regression, the first step is to disregard the effects of space and time and perform pooled regression instead.
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