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...

This paper provides a computationally practical simulation estimation for the dynamic panel Tobit model with large categories of dependence structures. The simulation estimators are conducted through correlated random effects approach. The log-likelihood function is simulated and maximized through procedures based on a recursive algorithm formulated by GHK and Gibbs sampling simulators. The ini...

We propose outlier-robust estimators for linear dynamic fixed effects panel data models where the number of observations,N , is large and the number of time periods, T , is small. In the simple setting of estimating the AR(1) coefficient from stationary Gaussian panel data, the estimator is (a linear transformation of) the median ratio of adjacent first-differenced data pairs. Its influence fun...

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...

The fixed effects estimator of panel models can be severely biased because of the well-known incidental parameter problems. It is shown that such bias can be reduced as T grows with n. We consider asymptotics where n and T grow at the same rate as an approximation that allows us to compare bias properties. Under these asymptotics the bias corrected estimators are centered at the truth, whereas ...

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 ...

This paper unifies the growth and profit strands in the industrial organization literature, by testing both the Law of Proportionate Effect and the Persistence of Profit hypothesis within a two-equation model capturing the bi-directional relationship between growth and profit. The model is estimated over a large panel data set comprising firms in five European countries. While there is an inver...

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...