This paper considers Maximum Likelihood (ML) based estimation and inference procedures for linear dynamic panel data models with fixed effects. The paper first studies the asymptotic properties of MaCurdy's [MaCurdy, T., 1982. The use of time series processes to model the time structure of...

In this paper we reconsider Modified ML estimation of the panel AR(1) model with fixed effects and arbitrary initial conditions, viz. y_{i,t}=ρy_{i,t-1} x_{i,t}′β(1-ρ) μ_{i}(1-ρ) ε_{i,t} with ε_{i,t}|(1(ρ≠1)((y_{i,0}-μ_{i}-T⁻¹ι′X_{i}β), QX_{i}))∼i.i.d.N(0,σ²), i=1,...,N...

This paper proposes new inference methods for panel AR models with arbitrary initial conditions and heteroskedasticity and possibly additional regressors that are robust to the strength of identification. Specifically, we consider several Maximum Likelihood based methods of constructing tests...

In this paper we consider two kinds of generalizations of Lancaster's (Review of Economic Studies, 2002) Modified ML estimator (MMLE) for the panel AR(1) model with fixed effects and arbitrary initial conditions and possibly covariates when the time dimension, T, is fixed. When the...

In this paper we discuss several limited information (LI) and full information (FI) random effects and fixed effects Quasi ML estimators (MLEs) for panel AR(1) models with additional regressors. We also consider related GMM estimators. All estimators are consistent for short (large N, fixed T)...