To * http://www.stata.com/support/statalist/faq > (See Carlo's advice on showing Stata output; it is much easier to receive advice on this board. 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 two) as the number of entities n increases. my problem is this: I get NA where I should get some values in the computation of robust standard errors.. Thank you for considering my question. An Introduction to Modern Econometrics Using Stata | http://www.stata-press.com/books/imeus.html On his web page he states: "The routines currently written into Stata allow you to cluster by only one variable (e.g. Share. See the discussion of clustering in Baum/Schaffer/Stillman papers, Stata Journal 3(1) [free] and 7(4), available in preprint form on my website. CONSISTENT COVARIANCE MATRIX ESTIMATION WITH SPATIALLY DEPENDENT PANEL DATA. Review of Financial Studies 22:435-80. You are not logged in. 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. * http://www.stata.com/support/statalist/faq An Introduction to Stata Programming | http://www.stata-press.com/books/isp.html cluster(varname1 varname2) provides 2-way cluster-robust SEs and statistics as proposed by Cameron, Gelbach and Daniel Hoechle. But e (i,t) can be autocorrelated. When I followed up on Kit's -xtivreg2_ suggestion, I found the following in the help for ivreg2: > The Stata Journal 2007 7: 3, 281-312 Share. I recently read these two articles about robust standard errors in panel datasets and can't figure out which SE I should use and in case of the clustered method how to apply this to Stata. * http://www.stata.com/help.cgi?search "Two-way cluster-robust" means the SEs and statistics are robust to * For searches and help try: Microeconometrics using stata (Vol. 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. The standard errors determine how accurate is your estimation. > I found various methods to apply the regression in Stata and hope you can help me to choose the right one, if any. * Robust Standard Errors in R. Stata makes the calculation of robust standard errors easy via the vce(robust) option. I have written a Stata ado file to implement this estimation procedure." Robust standard errors for panel regressions with cross–sectional dependence. Estimating robust standard errors in Stata Author James Hardin, StataCorp The new versions are better (less biased). Petersen (2007) reported a survey of 207 panel data papers published in the Journal of Finance,theJournal of Financial Economics,andtheReview of Financial Studies between 2001 and … Study the time-invariant features within each panel, the relationships across panels, and how outcomes of interest change over time. > newey depvar varlist, lag('T-1') force However, the standard errors generated assume the errors are iid. > package first) xtscc depvar varlist, fe If I'm correct, -ivreg2- came out in 2008, so maybe Petersen wrote his paper before -ivreg2-, but his website doesn't mention -ivreg2-. > * regression using Driscoll-Kraay SEs (need to install the xtscc From > Petersen, M. A. Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches. Kit Baum wrote: "None of what you have found deals with clustering." 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. > xtreg depvar varlist, vce(robust) > > * regression using Newey-West SEs typical application would be panel data where one "category" is the panel and the other "category" is time; the None of what you have found deals with clustering. vce(oim) uses the observed information matrix (OIM); see[R] ml. and they indicate that it is essential that for panel data, OLS standard errors be corrected for clustering on the individual. "statalist@hsphsun2.harvard.edu"
* http://www.stata.com/help.cgi?search Estimating robust standard errors in Stata 4.0 resulted in . Here I'm specifically trying to figure out how to obtain the robust standard errors (shown in square brackets) in column (2). To: statalist@hsphsun2.harvard.edu Robust Inference for Regression with Clustered Data: ... Regression model … Clustering allows you to deal with arbitrary heteroskedasticity across panels and aribtrary correlation within panels. Robust Standard Errors for Panel Regressions with Cross-Sectional Dependence. Correcting for Cross-Sectional and Time-Series Dependence in Accounting Research. This approach allows for correlations among different firms in the same year and different years in the same firm, for example. It seems that way since you said the standard errors are "robust to heteroskedasticity and autocorrelation." Hence, obtaining the correct SE, is critical. > ivregress gmm depvar varlist, vce(hac nwest opt) perfect In xtreg, stata automatically clusters on your panel variable when you type robust (in fact, it also does this when you don't). Two-way clustering also allows you to consider common effects hitting all firms at the same point in time. The help file above indicates that -ivreg2- does deal with both, so I'm not sure what I am missing. Subject: Re: st: Robust Standard Errors in Paneldatasets RE: st: Robust Standard Errors in Paneldatasets 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. - ivreg2- has a small sample correction option, so when would that be appropriate as opposed to including a time indicator variable? 2010. (1998). In Petersen, Mitchell A. Josh: I assume that you are using a version of Stata where the "robust" option is the same as "cluster(id)," where "id" is the cross section identifier. [Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index] A I'm trying to figure out the commands necessary to replicate the following table in Stata. Kit Date Robust standard errors for panel regressions with cross-sectional dependence Daniel Hoechle Department of Finance University of Basel Basel, Switzerland daniel.hoechle@unibas.ch Abstract. 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". I am about to do some multiple regressions with Panel Data so I am using the plm package.. Now I want to have the same results with plm in R as when I use the lm function and Stata when I perform a heteroscedasticity robust and entity fixed regression. * For searches and help try: Now, pooled OLS leaves u (i) in the error term, which is an obvious source of autocorrelation. > xtreg depvar varlist, fe robust The -ivreg2- help states, "Users should be aware of the asymptotic requirements for the consistency of the chosen VCE," so when T is short, is the best option the parametric option? Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches. * contemporaneous cross-panel correlation (clustering on time). > * regression using White SEs This paper references Petersen's Stata code. 4.1.1 Regression with Robust Standard Errors. The importance of using cluster-robust variance estimators (i.e., “clustered standard errors”) in panel models is now widely recognized. Cluster-robust standard errors and hypothesis tests in panel data models James E. Pustejovsky 2020-11-03 . > > Driscoll, J., & Kraay, A. The different robust estimators for the standard errors of panel models used in applied econometric practice can all be written and computed as combinations of the same simple building blocks. one dimension such as firm or time). Having said that, you are asking a theoretical question.). The e-mail addresses that you supply to use this service will not be used for any other purpose without your consent. 2008. The reason for robust standard errors in panel data is because the idiosyncratic errors can have heteroskedasticity or autocorrelation, or both. Social Media; Email; Share Access; Share this article via social media. Downloadable! Heteroskedasticity-Robust Standard Errors for Fixed Effects Panel Data Regression May, 2006 This revision: July, 2007 James H. Stock Department of Economics, Harvard University and the NBER Mark W. Watson1 Department of Economics and Woodrow Wilson School, Princeton University and the NBER ABSTRACT The conventional heteroskedasticity-robust (HR) variance matrix estimator for cross … Amy Dunbar arbitrary within-group correlation in two distinct non-nested categories defined by varname1 and varname2. This table is taken from Chapter 11, p. 357 of Econometric Analysis of Cross Section and Panel Data, Second Edition by Jeffrey M Wooldridge. I have used the modified Wald test to test for the presence of heteroskedasticity p values are low and hence the data does suffer from heteroskedasticity. Also see Gow, I., G. Ormazabal, and D. Taylor. How to implement heteroscedasticity-robust standard errors on regressions in Stata using the robust option and how to calculate them manually. The second data set is the Mitchell Petersen’s test data for two-way clustering. Hence, I wonder which regression type and which standard errors are most applicable as they should correct for heteroscedasticity and autocorrelation. Tue, 26 Oct 2010 13:24:06 +0000 In Stata's notation, the composite error term is u (i) + e (i,t). The second part deals with cluster-robust standard errors. Review of Economics & Statistics, 80(4), 549-560. newey and ivregress fail to take the panel nature of the data into account (in fact the ivregress command you give will not run on multiple panels, and the newey with undocumented -force- option is likely to think your data are one long time series). The Accounting Review 85 (2):483. > > > Petersen, M. A. Review of Financial Studies 22:435-80. In contrary to other statistical software, such as R for instance, it is rather simple to calculate robust standard errors in STATA. > You can browse but not post. vce(opg) uses the sum of the outer product of the gradient (OPG) vectors; see[R] ml. xtreg without the fe option is random effects, which is a.s. inappropriate for finance panels. I have a panel of 49 observations, 7 countries, 7 years, running Panel fixed effects and IV fixed effects on Stata. "xtgls" allows for non-iid errors. Papers by Thompson (2006) and by Cameron, Gelbach and Miller (2006) suggest a way to account for multiple dimensions at the same time. > * normal panel regression Googling around I 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. They are crucial in determining how many stars your table gets robust to you regression command is much easier receive. Using the Huber-White sandwich estimators web site ( http: //www.kellogg.northwestern.edu/faculty/petersen/htm/papers/se/se_programming.htm ) is... Basel Basel, Switzerland daniel.hoechle @ unibas.ch: Abstract term, which is obvious... For two-way clustering also allows you to deal with both, so 'm... 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