A simple data-dependent filtering method is proposed before applying the Bai–Ng method to estimate the number of common factors in the conventional approximate factor model. The asymptotic justification is provided and the finite-sample performance is examined.

Factor analysis performed on a panel of 23 nominal exchange rates from January 1999 to December 2010 yields three common factors. This paper identifies the euro/dollar, Swiss-franc/dollar and yen/dollar exchange rates as empirical counterparts to these common factors. These empirical factors...

In this paper we derive an asymptotic theory for linear panel regression augmented with estimated common factors. We give conditions under which the estimated factors can be used in place of the latent factors in the regression equation. For the principal components estimate of the factor space...

Independent realization is a commonly used shortcut for deriving forecast properties. It is also an unrealistic assumption in many empirical applications. In this paper we consider the effect of the assumption when deriving the properties of panel data forecasts. To do so, we derive and compare...

In regressions involving integrable functions we examine the limit properties of instrumental variable (IV) estimators that utilise integrable transformations of lagged regressors as instruments. The regressors can be either <italic>I</italic>(0) or nearly integrated (<italic>NI</italic>) processes. We show that this kind of...

Statistics are developed to test for the presence of an asymptotic discontinuity (or infinite density or peakedness) in a probability density at the median. The approach makes use of work by Knight (1998) on <italic>L</italic> ₁ estimation asymptotics in conjunction with nonparametric kernel density estimation...

This paper studies nonlinear cointegration models in which the structural coefficients may evolve smoothly over time. These time-varying coefficient functions are well-suited to many practical applications and can be estimated conveniently by nonparametric kernel methods. It is shown that the...

We obtain uniform consistency results for kernel-weighted sample covariances in a nonstationary multiple regression framework that allows for both fixed design and random design coefficient variation. In the fixed design case these nonparametric sample covariances have different uniform...

This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized regression techniques. We focus on linear models where the slope parameters are heterogeneous across groups but homogenous within a group and the group membership is...