Also see Gow, I., G. Ormazabal, and D. Taylor. * http://www.stata.com/support/statalist/faq An Introduction to Modern Econometrics Using Stata | http://www.stata-press.com/books/imeus.html Share. ”Robust” standard errors is a technique to obtain unbiased standard errors of OLS coefficients under heteroscedasticity. Robust standard errors for panel regressions with cross–sectional dependence. Review of Economics & Statistics, 80(4), 549-560. I have written a Stata ado file to implement this estimation procedure." The Accounting Review 85 (2):483. If I'm correct, -ivreg2- came out in 2008, so maybe Petersen wrote his paper before -ivreg2-, but his website doesn't mention -ivreg2-. > * normal panel regression I observe important differences between clustered and non-clustered standard errors. Unclustered data. Miller (2006) and Thompson (2009). And like in any business, in economics, the stars matter a lot. Sent: Tuesday, October 26, 2010 7:56 AM Cluster-robust standard errors and hypothesis tests in panel data models James E. Pustejovsky 2020-11-03 . > Review of Financial Studies 22:435-80. Robust Standard Errors for Panel Regressions with Cross-Sectional Dependence. I have a panel of 49 observations, 7 countries, 7 years, running Panel fixed effects and IV fixed effects on Stata. Study the time-invariant features within each panel, the relationships across panels, and how outcomes of interest change over time. All you need to is add the option robust to you regression command. Estimating robust standard errors in Stata Author James Hardin, StataCorp The new versions are better (less biased). "xtgls return monday january, p(c) c(p)" allows for heteroschedasticity and cross-sectional correlation across panels (countries in my example), in addition to panel-specific AR1 autocorrelation within each panel. But e (i,t) can be autocorrelated. Kit Baum wrote: "None of what you have found deals with clustering." The Stata regress command includes a robust option for estimating the standard errors using the Huber-White sandwich estimators. The conventional heteroskedasticity‐robust (HR) variance matrix estimator for cross‐sectional regression (with or without a degrees‐of‐freedom adjustment), applied to the fixed‐effects estimator for panel data with serially uncorrelated errors, is inconsistent if the number of time periods T is fixed (and greater than 2) as the number of entities n increases. Kit Baum | Boston College Economics & DIW Berlin | http://ideas.repec.org/e/pba1.html You can browse but not post. If you want to compute a Hausman test statistic that works also with cluster-robust standard errors you can follow the procedure outlined in Wooldridge (2010) "Econometric Analysis of Cross-Section and Panel Data". vce(oim) uses the observed information matrix (OIM); see[R] ml. This table is taken from Chapter 11, p. 357 of Econometric Analysis of Cross Section and Panel Data, Second Edition by Jeffrey M Wooldridge. The second data set is the Mitchell Petersen’s test data for two-way clustering. Now, pooled OLS leaves u (i) in the error term, which is an obvious source of autocorrelation. I have a panel data set in R (time and cross section) and would like to compute standard errors that are clustered by two dimensions, because my residuals are correlated both ways. The rst part of this note deals with estimation of xed-e ects model using the Fatality data. > Hi, I am new to Stata and try to measure herd behavior as deviations in the return dispersion of a large panel dataset. > xtreg depvar varlist, vce(robust) In the new implementation of the robust estimate of variance, Stata is now scaling the estimated variance matrix in order to make it less biased. > I found various methods to apply the regression in Stata and hope you can help me to choose the right one, if any. typical application would be panel data where one "category" is the panel and the other "category" is time; the Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches. Robust regression is an alternative to least squares regression when data is contaminated with outliers or influential observations and it can also be used for the purpose of detecting influential observations. * http://www.stata.com/help.cgi?search resulting SEs are robust to arbitrary within-panel autocorrelation (clustering on panel id) and to arbitrary 2008. In a simple panel data analysis with data on 64 firms over 8 years, I use cluster-robust standard errors (at the firm level) to evaluate significance of coefficients. > Amy Dunbar

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