Sebastian Kripfganz

Senior Lecturer (advanced assistant professor) in Econometrics,
University of Exeter Business School, Department of Economics

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© Sebastian Kripfganz
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Title and Abstract
Bias-corrected estimation of linear dynamic panel data models
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.
Suggested Citation
Kripfganz, S., and J. Breitung (2022). Bias-corrected estimation of linear dynamic panel data models. Proceedings of the 2022 London Stata Conference.
Related Work
Breitung, J., S. Kripfganz, and K. Hayakawa (2022). Bias-corrected method of moments estimators for dynamic panel data models. Econometrics and Statistics 24, 116-132.
Kripfganz, S. (2019). Generalized method of moments estimation of linear dynamic panel data models. Proceedings of the 2019 London Stata Conference.
Kripfganz, S., and C. Schwarz (2019). Estimation of linear dynamic panel data models with time-invariant regressors. Journal of Applied Econometrics 34 (4), 526-546.
Kripfganz, S. (2016). Quasi-maximum likelihood estimation of linear dynamic short-T panel-data models. Stata Journal 16 (4), 1013-1038.
Sebastian Kripfganz
University of Exeter
Jörg Breitung
University of Cologne
Proceedings of the 2022 London Stata Conference
Stata Program