The Common Correlated Effects (CCE) approach by Pesaran2006 is a popular method for estimating panel data models with interactive effects. Due to its simplicity, i.e. unobserved common factors are approximated with cross-section averages of the observables, the estimator is highly flexible and...

The present paper develops a new Instrumental Variables (IV) estimator for spatial, dynamic panel data models with interactive effects under large N and T asymptotics. For this class of models, the only approaches available in the literature are based on quasi-maximum likelihood estimation. The...

This note is intended for researchers who want to use the interactive effects model for empirical modeling. We consider how to estimate interactive effects models when some of the factors and factor loading are observable. Observable factors are common regressors which do not vary across...

This paper proposes a novel method to estimate large panel data error-correction models with stationary/non-stationary covariates and spatially dependent errors, which allows for known/unknown group-specific patterns of slope heterogeneity. Analysis is based on composite quasi-likelihood (CQL)...

This paper introduces a new procedure for analyzing the quantile co-movement of a large number of financial time series based on a large-scale panel data model with factor structures. The proposed method attempts to capture the unobservable heterogeneity of each of the financial time series...

Internal market structure analysis infers both brand attributes and consumer preferences for those attributes from preference or choice data. The authors exploit a new method for estimating probit models from panel data to infer market structures that can be displayed in few dimensions, even...