Bootstrap Methods for Inference with Cluster Sample IV Models

46 Pages Posted: 6 Mar 2015

See all articles by Keith Finlay

Keith Finlay

Tulane University - Department of Economics

Leandro M. Magnusson

University of Western Australia

Date Written: April 4, 2014

Abstract

Microeconomic data often have within-cluster dependence. This dependence affects standard error estimation and inference in regression models, including the instrumental variables model. Standard corrections assume that the number of clusters is large, but when this is not the case, Wald and weak-instrument-robust tests can be severely over-sized. We examine the use of bootstrap methods to construct appropriate critical values for these tests when the number of clusters is small. We find that variants of the wild bootstrap perform well and reduce absolute size bias significantly, independent of instrument strength or cluster size. We also provide guidance in the choice among possible weak-instrument-robust tests when data have cluster dependence. These results are applicable to fixed-effects panel data models.

Keywords: Two-stage least squares, instrumental variables, hypothesis testing, weak instruments, clustered errors, wild bootstrap

JEL Classification: C12, C15, C31

Suggested Citation

Finlay, Keith and Magnusson, Leandro M., Bootstrap Methods for Inference with Cluster Sample IV Models (April 4, 2014). Available at SSRN: https://ssrn.com/abstract=2574521 or http://dx.doi.org/10.2139/ssrn.2574521

Keith Finlay

Tulane University - Department of Economics ( email )

New Orleans, LA 70118
United States

Leandro M. Magnusson (Contact Author)

University of Western Australia ( email )

35 Stirling Highway, M251
Crawley, WA 6009
Australia
+61 8 6488 2924 (Phone)
+61 8 6488 1016 (Fax)

HOME PAGE: http://www.uwa.edu.au/people/Leandro.Magnusson

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