Abstract
Time-series - cross-section (TSCS) data are characterized by having repeated observations over time on some set of units, such as states or nations. TSCS data typically display both contemporaneous correlation across units and unit level heteroskedasity making inference from standard errors produced by ordinary least squares incorrect. Panel-corrected standard errors (PCSE) account for these these deviations from spherical errors and allow for better inference from linear models estimated from TSCS data. In this paper, we discuss an implementation of them in the R system for statistical computing. The key computational issue is how to handle unbalanced data.
Highlights
Time-series–cross-section (TSCS) data are characterized by having repeated observations over time on some set of units, such as states or nations
When fitting linear models to TSCS data, it is common to use this non-spherical error structure to improve inference and estimation efficiency by a feasible generalized least squares (FGLS) estimator suggested by Parks (1967) and made popular by Kmenta (1986)
Beck and Katz (1995) suggested estimating linear models of TSCS data by ordinary least squares (OLS)1 and they proposed a sandwich type estimator of the covariance matrix of the estimated parameters, which they called panel-corrected standard errors (PCSE), that is robust to the possibility of
Summary
Time-series–cross-section (TSCS) data are characterized by having repeated observations over time on some set of units, such as states or nations. Beck and Katz (1995) suggested estimating linear models of TSCS data by ordinary least squares (OLS) and they proposed a sandwich type estimator of the covariance matrix of the estimated parameters, which they called panel-corrected standard errors (PCSE), that is robust to the possibility of. R packages that estimate various models for panel data include plm (Croissant and Millo 2008) and systemfit (Henningsen and Hamann 2007), that implement different types of robust standard errors. Some of these are only robust to unit heteroskedasity and possible serial correlation.
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