This argues for a closer link between the modelling of the long-run relations in applied economics and the intertemporal equilibrium notion from economic theory.

This paper This paper develops a new approach to the problem of testing the existence of a long-run level relationship between a dependent variable and a set of regressors, when it is not known with certainty whether the underlying regressors are trend- or first-difference stationary. The...

This paper considers testing the hypothesis that errors in a panel data model are weakly cross sectionally dependent, using the exponent of cross-sectional dependence <img src="http://www.econ.cam.ac.uk/faculty/pesaran/wp12/image3.png" width="11" height="13" />, introduced recently in Bailey, Kapetanios and Pesaran (2012). It is shown that the implicit null of the <em>CD</em> test depends on the...

This paper examines and compares the finite sample performance of the existing tests for sample selection bias, especially under the multi-collinearity problem pointed out by Nawata (1993). The results show that under such multicollinearity problem, (i) the t-test for sample selection bias based...

This paper takes a computationnaly simple LS approach to develop a more efficient estimation procedure, which we call Residual Augmented Least Square (RALS), than OLS when the errors are not normally distributed. The efficiency gain is from manipulating the higher moment conditions implied by...

This paper presents a new approach to estimation and inference in panel data models with unobserved common factors possibly correlated with exogenously given individual-specific regressors and/or the observed common effects. The basic idea behind the proposed estimation procedure is to filter...

This paper considers alternative approaches to the analysis of large panel data models in the presence of error cross section dependence. A popular method for modelling such dependence uses a factor error structure. Such models raise new problems for estimation and inference. This paper compares...