Efficient Inference on Fractionally Integrated Panel Data Models with Fixed Effects

Abstract

A dynamic panel data model is considered containing individual components and a fractional stochastic time trend.? We propose four different ways of coping with the individual effects so as to estimate the fractional parameter. Like models with autoregressive dynamics, ours nests a unit root, but unlike the nonstandard asymptotics in the autoregressive case, estimates of the fractional parameter can be asymptotically normal. Establishing useful central limits is made difficult by bias caused by the individual effects, or by the consequences of eliminating them.? Implications for hypothesis testing and interval estimation are discussed. A Monte Carlo study of finite-sample performance is included.