We demonstrate that panel unit root tests can have high power when a small fraction of the series are stationary and may lack power when a large fraction is stationary. The acceptance or rejection of the null is thus not sufficient evidence to conclude that all series have a unit root or that...

This paper considers maximum likelihood estimation and inference in the two-way random effects model with serial correlation. We derive a straightforward maximum likelihood estimator when the time-specific component follow an AR(1) or MA(1) process. The estimator is easily generalized to...

This paper is concerned with maximum likelihood based inference in random effects models with serial correlation. Allowing for individual effects we introduce serial correlation of general form in the time effects as well as the idiosyncratic errors. A straightforward maximum likelihood...

This paper proposes several resampling algorithms suitable for error component models and evaluates them in the context of bootstrap testing. In short, all the algorithms work well and lead to tests with correct or close to correct size. There is thus little or no reason not to use the bootstrap...

This paper considers the large sample behavior of the maximum likelihood estimator of random effects models with serial correlation in the form of AR(1) for the idiosyncratic or time-specific error component. Consistent estimation and asymptotic normality as N and/or T grows large is established...

The detection of structural change and determination of lag lengths are long-standing issues in time series analysis. This paper demonstrates how these can be successfully married in a Bayesian analysis. By taking account of the inherent uncertainty about the lag length when deciding on the...