Efficient Moment Selection from High-Dimensional Moment Conditions

• Activity Number: 472
• Type: Contributed
• Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Business and Economic Statistics Section
• Author(s): Hyunkeun Cho*+ and Annie Qu
• Companies: University of Illinois at Urbana-Champaign and University of Illinois at Urbana-Champaign
• Keywords: Dynamic panel data model ; Generalized method of moments ; High-dimensional moment conditions ; Moment selection ; Quadratic inference function ; Singular matrix

Abstract

For high-dimensional correlated data with a large cluster size, it is feasible to generate many valid moment conditions such as in dynamic panel data models. The generalized method of moments (GMM) approach has the advantages of obtaining an efficient estimator by minimizing the weighed objective function of the moment conditions. However, the GMM estimator could be infeasible when the number of moment conditions exceeds the sample size. We propose an objective criterion which includes a set of important moment conditions in addition to selecting optimal linear combinations of the remaining moment conditions. This is in contrast to existing methods which only select a subset of the valid moment conditions. Monte Carlo simulation studies and real data example show that the proposed method performs better when important moment conditions are included in addition to linear combinations of the remaining moment conditions. It outperforms existing methods in the sense of reducing bias and improving the efficiency of the estimation.