Central limit theorems are developed for instrumental variables estimates of linear and semiparametric partly linear regression models for spatial data. General forms of spatial dependence and heterogeneity in explanatory variables and unobservable disturbances are permitted. We discuss...

This paper deals with certain estimation problems involving the covariance matrix in large dimensions. Due to the breakdown of finite-dimensional asymptotic theory when the dimension is not negligible with respect to the sample size, it is necessary to resort to an alternative framework known as...

We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping...

Limited Mobility Bias explains why positive assortative matching is not observed in the empirical literature. Using German social security records, we estimate the correlation between worker and firm contributions to wage equations and find that it is unambiguously positive.

In this paper, we consider a cointegrated panel data model with non-stationary common factors, which, because of its appeal in many economic applications, has received much attention in the recent literature. By deriving a Granger-type representation theorem, we obtain several equivalent model...

Practitioners are generally well aware of the fact that most standard approaches for estimation and inference in panel data regressions are based on assuming that the cross-sectional units are independent of each other, an assumption that is surely mistaken in applications, especially in...

In this note we extend the method proposed in Bun and Carree (2006) to the more general PVARX(1) model and show that the iterative procedure is not consistent for fixed T. Subsequently we provide corrected version of the bias correction procedure which is fixed T consistent and robust to both...

We examine finite sample properties of estimators for approximate factor models when N is small. Contrary to the “rule-of-thumb”, we find that the principal component analysis estimator and the quasi-maximum likelihood estimator perform well even when N is small.

In an influential paper, Pesaran [Pesaran, M.H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica 74, 967–1012] proposes a very simple estimator of factor-augmented regressions that has since then become very popular. In this note...