Factor Based Identification-Robust Inference in IV Regressions

50 Pages Posted: 2 Feb 2015

See all articles by George Kapetanios

George Kapetanios

King's College, London

Lynda Khalaf

Carleton University

Massimiliano Giuseppe Marcellino

Bocconi University - Department of Economics; Centre for Economic Policy Research (CEPR)

Date Written: February 2015

Abstract

Robust methods for IV inference have received considerable attention recently. Their analysis has raised a variety of problematic issues such as size/power trade-offs resulting from weak or many instruments. We show that information-reduction methods provide a useful and practical solution to this and related problems. Formally, we propose factor-based modifications to three popular weak-instrument-robust statistics, and illustrate their validity asymptotically and in finite samples. Results are derived using asymptotic settings that are commonly used in both the factor and weak instrument literatures. For the Anderson-Rubin statistic, we also provide analytical finite sample results that do not require any underlying factor structure. An illustrative Monte Carlo study reveals the following. Factor based tests control size regardless of instruments and factor quality. All factor based tests are systematically more powerful than standard counterparts. With informative instruments and in contrast with standard tests: (i) power of factor-based tests is not affected by k even when large, and (ii) weak factor structure does not cost power. An empirical study on a New Keynesian macroeconomic model suggests that our factor-based methods can bridge a number of gaps between structural and statistical modeling.

Keywords: factor model, identification-robust inference, IV regression, new Keynesian model, principle components, weak instruments

Suggested Citation

Kapetanios, George and Khalaf, Lynda and Marcellino, Massimiliano, Factor Based Identification-Robust Inference in IV Regressions (February 2015). CEPR Discussion Paper No. DP10390, Available at SSRN: https://ssrn.com/abstract=2559331

George Kapetanios (Contact Author)

King's College, London ( email )

30 Aldwych
London, WC2B 4BG
United Kingdom
+44 20 78484951 (Phone)

Lynda Khalaf

Carleton University ( email )

1125 colonel By Drive
Ottawa, Ontario K1S 5B6
Canada

Massimiliano Marcellino

Bocconi University - Department of Economics ( email )

Via Gobbi 5
Milan, 20136
Italy

Centre for Economic Policy Research (CEPR) ( email )

London
United Kingdom

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