Linear Instrumental Variables Model Averaging Estimation

• Activity Number: 689
• Type: Contributed
• Date/Time: Thursday, August 8, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Business and Economic Statistics Section
• Author(s): Luis Filipe Martins*+ and Vasco Gabriel
• Companies: ISCTE-LUI and University of Surrey
• Keywords: Instrumental Variables ; Model Selection ; Model Averaging ; Model Screening ; Returns to Education

Abstract

Model averaging (MA) estimators in the linear instrumental variables regression framework are considered. We propose obtaining weights for averaging across individual estimates by direct smoothing of selection criteria arising from the estimation stage. This is particularly relevant in applications in which there is a large number of candidate instruments and, therefore, a considerable number of instrument sets arising from different combinations of the available instruments. The asymptotic properties of the estimator are derived under homoskedastic and heteroskedastic errors. A simple Monte Carlo study contrasts the performance of MA procedures with existing instrument selection procedures, showing that MA estimators compare very favourably in many relevant setups. Finally, this method is illustrated with an empirical application to returns to education.