help xtsccversion: 1.4
also see: xtscc postestimation

Title

xtscc - Regression with Driscoll-Kraay standard errors

Syntax

xtscc
depvar
[indepvars]
[if] [in]
[weight]
[, options]

Model
lag(#) set maximum lag order of autocorrelation; default is m(T)=floor[4(T/100)^(2/9)]

fe perform fixed effects (within) regression

re perform GLS random effects regression

pooled perform pooled OLS/WLS regression; default

noconstant suppress regression constant in pooled OLS/WLS regressions

ase return (asymptotic) Driscoll-Kraay SE without small sample adjustment

Reporting
level(#) set confidence level; default is level(95)

You must tsset your data before using xtscc.

by, statsby, and xi may be used with
xtscc; see prefix.

indepvars may contain factor variables; see fvvarlist.

depvar and indepvars may contain time-series operators; see tsvarlist.

aweights are allowed.

See xtscc postestimation for features available after estimation.

Description

xtscc produces Driscoll and Kraay (1998) standard errors for coefficients
estimated by pooled OLS/WLS, fixed-effects (within), or GLS random effects
regression. depvar is the dependent variable and varlist is 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.

fe performs fixed-effects (within) regression with Driscoll-Kraay standard errors.

re performs GLS random-effects (RE) regression with Driscoll-Kraay standard errors.

pooled performs pooled OLS/WLS regression with Driscoll-Kraay standard errors.

noconstant; see [R] estimation options.

ase returns asymptotic Driscoll-Kraay standard errors. Standard errors that
are computed this way might be slightly overoptimistic as they abstract 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

GLS random-effects regression:

. est clear

. xtreg invest mvalue kstock, re robust

. est store re_robust

. xtscc invest mvalue kstock, re lag(4)

. est store re_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 and Statistics
80, 549-560.

Notes

- The main procedure of xtscc is implemented in Mata and largely follows
Driscoll and Kraay's GAUSS program which is available from
http://www.johncdriscoll.net/.

- The xtscc program uses functions from Ben Jann's moremata package.

- Weighted estimation in the case of fixed effects and (feasible) GLS random effects estimation
is based on a within transform similar to that of official Stata's -areg- command. Note
that -xtreg, fe- currently cannot estimate fixed effects regressions with
time-varying weights, and -xtreg, re- does not allow for weights at all.

Acknowledgements

I would like to thank Sergio Correia, David M. Drukker, Bill Gould, and Gustavo Sanchez
for their useful comments and suggestions.

Author

Daniel Hoechle, FHNW School of Business, daniel.hoechle@fhnw.ch

Also see

Manual: [R] regress, [TS] newey, [XT] xtreg, [R] areg

Online: xtscc postestimation;

tsset, regress, newey, xtreg, areg, _robust