Time series data are often well modeled by using the device of an autoregressive root that is local to unity. Unfortunately, the localizing parameter (c) is not consistently estimable using existing time series econometric techniques and the lack of a consistent estimator complicates inference....

It is shown that the maximum likelihood estimator of a local to unity parameter can be consistently estimated with panel data when the cross section observations are independent. Consistency applies when there are no deterministic trends or when there is a homogenous deterministic trend in the...

A new model of near integration is formulated in which the local to unity parameter is identifiable and consistently estimable with time series data. The properties of the model are investigated.

This paper develops a regression limit theory for nonstationary panel data with large numbers of cross section (n) and time series (T) observations. The paper explores the existence of long-run average relations between integrated panel vectors when there is no individual time series...

This paper overviews some recent developments in nonstationary panel data analysis. Underlying recent theory are asymptotics for multi-indexed processes in which both indexes may pass to infinity. We review some of the new limit theory that has been developed, show how it can be applied and give...

This paper develops procedures for the estimation of a common localizing parameter using panel data. Pooling information across individuals in a panel aids the identification and estimation of the localising parameter and leads to consistent estimation in somple panel models. However, in the...

This paper overviews some recent developments in nonstationary panel data asymptotics, concentrating on the nonstationary panel case and gives a new result for models with individual effects. Underlying recent theory are asymptotics for multi-indexed processes in which both indexes may pass to...