Bootstrap bias-correction procedure in estimating long-run relationships from dynamic panels, with an application to money demand in the euro area

In dynamic panel data models, which are particularly well-suited to cross-country analysis, the mean group estimator (J. Econ. 1995 68 79-113) is under certain quite strong conditions consistent, but theoretical and empirical evidence indicates that it can be biased when the number of time observations is small. Possible explanations are sample-size bias and omitted variables or measurement errors that are correlated with the regressors. I find support for both hypotheses using a Monte Carlo experiment, which analyzes cointegrated systems. A possible solution for the MG estimator bias is a bootstrap bias-correction procedure, but Analysis of Panels and Limited Dependent Variables: A Volume in Honour of G.S. Maddala 1999, Cambridge chapter 12 297-321 shows that it performs well only when the true coefficient of the lagged dependent variable is small. In this paper, I test three different bootstrap procedures and obtain an appreciable reduction in the MG estimator bias, especially when the suggestions of J. Econ. (1997) 80 297-318 are applied. Finally, I use bootstrap bias-corrected estimators to investigate the long-run properties of money demand in the euro area.