Bayesian Factor Model Shrinkage for Linear IV Regression With Many Instruments

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

3 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)278-287
Journal / PublicationJournal of Business and Economic Statistics
Volume36
Issue number2
Online published28 Apr 2017
Publication statusPublished - Apr 2018
Externally publishedYes

Abstract

A Bayesian approach for the many instruments problem in linear instrumental variable models is presented. The new approach has two components. First, a slice sampler is developed, which leverages a decomposition of the likelihood function that is a Bayesian analogue to two-stage least squares. The new sampler permits nonconjugate shrinkage priors to be implemented easily and efficiently. The new computational approach permits a Bayesian analysis of problems that were previously infeasible due to computational demands that scaled poorly in the number of regressors. Second, a new predictor-dependent shrinkage prior is developed specifically for the many instruments setting. The prior is constructed based on a factor model decomposition of the matrix of observed instruments, allowing many instruments to be incorporated into the analysis in a robust way. Features of the new method are illustrated via a simulation study and three empirical examples.

Research Area(s)

  • Bayesian econometrics, Horseshoe prior, Instrumental variables, Slice sampler