A comparison analysis of dynamic panel-data estimators in the presence of endogenous regressors

Data used in applied econometrics are typically nonexperimental in nature. This makes the assumption of exogeneity of regressors untenable and poses a serious identification issue in the estimation of economic structural relationships. As far as the source of endogeneity is confined to unobserved heterogenity between groups (for example, time-invariant managerial ability in firm-level labor demand equations), the availability of panel data can identify the parameters of interest. If endogeneity, instead, is more pervasive, stemming also from unobserved within-group variation (for example, a transitory technology shock hitting at the same time both the labor demand of the firm and the wage paid), then standard panel data estimators are biased and instrumental variable or generalized method of moments estimators provide valid alternative techniques. This paper extends the analysis in Bruno (2005) focusing on dynamic panel-data (DPD) models with endogenous regressors. Various Monte Carlo experiments are carried out through my Stata code xtarsim to assess the relative finite-sample performances of some popular DPD estimators, such as Arellano and Bond (xtabond, xtabond2), Blundell and Bond (xtabond2), Anderson and Hsiao (ivreg, ivreg2, xtivreg, xtivreg2), and LSDVC (xtlsdvc). New versions of the commands xtarsim and xtlsdvc are also presented.