Bias-corrected estimation of linear dynamic panel data models

Sebastian Kripfganz and Jörg Breitung ()

London Stata Conference 2022 from Stata Users Group

Abstract: In the presence of unobserved group-specific heterogeneity, the conventional fixed-effects and random-effects estimators for linear panel data models are biased when the model contains a lagged dependent variable and the number of time periods is small. We present a computationally simple bias-corrected estimator with attractive finite-sample properties, which is implemented in our new xtdpdbc Stata package. The estimator relies neither on instrumental variables nor on specific assumptions about the initial observations. Because it is a method-of-moments estimator, standard errors are readily available from asymptotic theory. Higher-order lags of the dependent variable can be accommodated as well. A useful test for the correct model specification is the Arellano–Bond test for residual 3 autocorrelation. The random-effects versus fixed-effects assumption can be tested using a Hansen overidentification test or a generalized Hausman test. The user can also specify a hybrid model, in which only a subset of the exogenous regressors satisfies a random-effects assumption.

Date: 2022-09-10
New Economics Papers: this item is included in nep-dcm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://repec.org/lsug2022/uk2022_kripfganz.pdf

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:boc:lsug22:05

Access Statistics for this paper

More papers in London Stata Conference 2022 from Stata Users Group Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F Baum ().