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A Shrinkage Instrumental Variable Estimator for Large Datasets

Andrea Carriero (), G. Kapetanios and Massimiliano Marcellino

No 558, Working Papers from IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University

Abstract: This paper proposes and discusses an instrumental variable estimator that can be of particular relevance when many instruments are available and/or the number of instruments is large relative to the total number of observations. Intuition and recent work (see, e.g., Hahn (2002)) suggest that parsimonious devices used in the construction of the nal instruments may provide e¤ective estimation strategies. Shrinkage is a well known approach that promotes parsimony. We consider a new shrinkage 2SLS estimator. We derive a consistency result for this estimator under general conditions, and via Monte Carlo simulation show that this estimator has good potential for inference in small samples. Keywords: Instrumental Variable Estimation, 2SLS, Shrinkage, Bayesian Regression. JEL classi cation: C13, C23, C51.

Date: 2015
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Related works:
Journal Article: A SHRINKAGE INSTRUMENTAL VARIABLE ESTIMATOR FOR LARGE DATASETS (2015) Downloads
Working Paper: A Shrinkage Instrumental Variable Estimator for Large Datasets (2008) Downloads
Working Paper: A Shrinkage Instrumental Variable Estimator for Large Datasets (2008) Downloads
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