This paper considers estimation of factor-augmented panel data regression models with homogenous slope coefficients. One of the most popular approaches towards this end is the pooled common correlated effects CCE estimator of Pesaran 2006. For this estimator to be consistent at the usual sqrt-NT...

We consider an exact factor model and derive a Lagrange multiplier-type test for unit roots in the idiosyncratic components. The asymptotic distribution of the statistic is derived under the misspecification that the differenced factors are white noise. We prove that the asymptotic distribution...

In this paper, we demonstrate that cultural borders in international finance resurge during financial crises. To investigate the role of cultural borders during both tranquil and crisis periods, we employ a unique data set that focuses on Eurozone cross-border depositing in a gravity-model...

This article examines the link between restrictions on the number of physicians and general practitioners' (GPs) earnings. Using a representative panel of 6016 French self-employed GPs over the years 1983-2004, we estimate an earnings function to identify experience, time and cohort effects. The...

This paper utilizes longitudinal data on varicella (chickenpox) immunizations in order to estimate the causal effects of state-level school-entry and daycare-entry immunization mandates within the United States. We find significant causal effects of mandates upon vaccination rates among...

The usual starting point for understanding changes in income-related health inequality (IRHI) over time has been regression-based decomposition procedures for the health concentration index. However the reliance on repeated cross-sectional analysis for this purpose prevents both the appropriate...

This paper develops an accounting framework to consider the effect of deaths on the longitudinal analysis of income-related health inequalities. Ignoring deaths or using Inverse Probability Weights (IPWs) to re-weight the sample for mortality-related attrition can produce misleading results....

Generalized linear mixed models (GLMMs) are commonly used to analyze longitudinal categorical data. In these models, we typically assume that the random effects covariance matrix is constant across the subject and is restricted because of its high dimensionality and its positive definiteness....

We propose a data-driven procedure for modeling covariance matrices in linear mixed-effects models with minimal distributional assumption on the random effects. It is based on elimination of the random effects using a transformation of the response variable. The approach makes it possible for...

We show how the dynamic logit model for binary panel data may be approximated by a quadratic exponential model. Under the approximating model, simple sufficient statistics exist for the subject-specific parameters introduced to capture the unobserved heterogeneity between subjects. The latter...