This paper proposes new GMM estimators for the panel AR(1) model when the ratio of the variance of the individual effects to the variance of the idiosyncratic errors is large. First, we present a necessary condition for large N, fixed T consistency of any Fixed Effects or Random Effects...

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)...

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 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...

In this paper we show that the Quasi ML estimation method yields consistent Random and Fixed Effects estimators for the autoregression parameter ρ in the panel AR(1) model with arbitrary initial conditions and possibly time-series heteroskedasticity even when the error components are drawn from...