help xtsccversion: 1.3 also see:xtscc postestimation-------------------------------------------------------------------------------

Title

xtscc- Regression with Driscoll-Kraay standard errors

Syntax

xtsccdepvar[indepvars] [if] [in] [weight] [,options]

optionsDescription ------------------------------------------------------------------------- Modellag(#)set maximum lag order of autocorrelation; default is m(T)=floor[4(T/100)^(2/9)]feperform fixed effects (within) regressionpooledperform pooled OLS/WLS regression; defaultnoconstantsuppress regression constant in pooled OLS/WLS regressionsasereturn (asymptotic) Driscoll-Kraay SE without small sample adjustmentReporting

level(#)set confidence level; default islevel(95)------------------------------------------------------------------------- You musttssetyour data before usingxtscc.by,statsby, andximay be used withxtscc; see prefix.aweights are allowed unless optionfeis specified; see weight. See xtscc postestimation for features available after estimation.

Description

xtsccproduces Driscoll and Kraay (1998) standard errors for coefficients estimated by pooled OLS/WLS or fixed-effects (within) regression.depvaris the dependent variable andvarlistis an (optional) list of explanatory variables.The error structure is assumed to be heteroskedastic, autocorrelated up to some lag, and possibly correlated between the groups (panels). Driscoll-Kraay standard errors are robust to very general forms of cross-sectional ("spatial") and temporal dependence when the time dimension becomes large. This nonparametric technique of estimating standard errors does not place any restrictions on the limiting behavior of the number of panels. Consequently, the size of the cross-sectional dimension in finite samples does not constitute a constraint on feasibility - even if the number of panels is much larger than T. However, note that the estimator is based on large T asymptotics. Therefore, one should be somewhat cautious with applying this estimator to panel datasets with a large number of groups but a small number of observations over time.

This implementation of Driscoll and Kraay's covariance estimator works for both, balanced and unbalanced panels, respectively. Furthermore, it is capable to handle missing values.

Options+-------+ ----+ Model +------------------------------------------------------------

lag(#)specifies the maximum lag to be considered in the autocorrelation structure. If you do not specify this option, a lag length of m(T)=floor[4(T/100)^(2/9)] is chosen.

feperforms fixed-effects (within) regression with Driscoll-Kraay standard errors. These standard errors are robust to very general forms of cross-sectional ("spatial") and temporal dependence (provided that T is sufficiently large). See above. If the residuals are assumed to be heteroscedastic only: usextreg, fe robust.

pooledperforms pooled OLS/WLS regression with Driscoll-Kraay standard errors. These standard errors are robust to very general forms of cross-sectional ("spatial") and temporal dependence (provided that T is sufficiently large). See above. If the residuals are assumed to be heteroscedastic only: usereg, robust cluster(). If the residuals are assumed to be heteroscedastic and autocorrelated only (i.e. there is no cross-sectional correlation): usenewey, lag() force.

noconstant; see [R] estimation options.

asereturns asymptotic Driscoll-Kraay standard errors. Standard errors that are computed this way might be slightly overoptimistic as they abstract away from a small sample adjustment.

+-----------+ ----+ Reporting +--------------------------------------------------------

level(#); see estimation options.

Examples. sysuse grunfeld

Pooled OLS estimation:. reg invest mvalue kstock, robust cluster(company) . est store robust

. newey invest mvalue kstock, lag(4) force . est store newey

. xtscc invest mvalue kstock, lag(4) . est store dris_kraay

. est table *, b se t

Fixed-effects (within) regression:. est clear . xtreg invest mvalue kstock, fe robust . est store fe_robust

. xtscc invest mvalue kstock, fe lag(4) . est store fe_dris_kraay

. est table *, b se t

Reference- Driscoll, John C. and Aart C. Kraay, 1998, Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data,

Review of Economics andStatistics80, 549-560.

Notes- The main procedure of

xtsccis implemented in Mata and largely follows Driscoll and Kraay's GAUSS program which is available from http://www.johncdriscoll.net/. - Thextsccuses functions from Ben Jann'smorematapackage.

AcknowledgementsI would like to thank David M. Drukker and Bill Gould from StataCorp for their useful comments and suggestions.

AuthorDaniel Hoechle, University of Basel, daniel.hoechle@unibas.ch

Also seeManual:

[R] regress,[TS] newey,[XT] xtregOnline: xtscc postestimation;

tsset,regress,newey,xtreg,_robust