We develop a hierarchical Bayesian approach for inference in random coefficient dynamic panel data models. Our approach allows for the initial values of each unit’s process to be correlated with the unit-specific coefficients. We impose a stationarity assumption for each unit’s process by assuming that the unit-specific autoregressive coefficient is drawn from a logitnormal distribution. Our me...

Estimation of the dynamic error components model is considered using two alternative linear estimators that are designed to improve the properties of the standard firstdifferenced GMM estimator. Both estimators require restrictions on the initial conditions process. Asymptotic efficiency comparisons and Monte Carlo simulations for the simple AR(1) model demonstrate the dramatic improvement in p...

Recent developments in nonlinear panel data analysis allow identifying and estimating general dynamic systems. In this review we describe some results and techniques for nonparametric identification and flexible estimation in the presence of time-invariant and time-varying latent variables. This opens the possibility to estimate nonlinear reduced forms in a large class of structural dynamic mod...

The conventional Sargan (1958) / Hansen (1982) test of overidentifying restrictions and the Tilting Parameter test of Imbens, Spady and Johnson (1998) are compared in the context of the AR(1) dynamic panel data model using Monte Carlo experiments. Interestingly, the size properties of the former are found to be superior in this setting. Nevertheless, the Sargan / Hansen test is found to have no...

We thank the editor M. Hashem Pesaran and three anonymous referees for their useful comments. 1 Summary We propose a new method for estimating dynamic panel data models with selection. The method uses backward substitution for the lagged dependent variable, which leads to an estimating equation that requires correcting for contemporaneous selection only. The estimator is valid under relatively ...

We propose conditions under which parameters of fixed-effect dynamic models are identified with unequally spaced panel data. Under predeterminedness, weak stationarity, and empirically testable rank conditions, AR(1) parameters are identified given the availability of ‘‘two pairs of two consecutive time gaps’’, which generalizes ‘‘two pairs of two consecutive time periods’’. This result extends...

We develop a generalized dynamic factor model for panel data with the goal of estimating an unobserved performance index. While similar models have been developed in the literature of dynamic factor analysis, our contribution is threefold. First, contrary to simple dynamic factor analysis where multiple attributes of the same cross sectional unit are measured at each time period, our model also...

Motivated by the first differencing method for linear panel data models, we propose a class of iterative local polynomial estimators for nonparametric dynamic panel data models with or without exogeous regressors. The estimators utilize the additive structure of the first-differenced model, the fact that the two additive components have the same functional form, and the unknown function of inte...

Previous research on dynamic panel estimation has focused on panels that, unlike a typical panel of macroeconomic data, have small time dimensions and large individual dimensions. We use a Monte Carlo approach to investigate the performance of several different methods designed to reduce the bias of the estimated coefficients for the longer, narrower panels commonly found for macro data. We fin...

This paper studies estimation and inference in a quantile regression dynamic panel model with fixed effects. Panel data fixed effects estimators are typically biased in the presence of lagged dependent variables as regressors. To reduce the dynamic bias in the quantile regression fixed effects estimator I suggest the use of the instrumental variables quantile regression method of Chernozhukov a...