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Monte Carlo Methodology and the Finite Sample Properties of Instrumental Variables Statistics for Testing Nested and Non-nested Hypotheses

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  • Ericsson, Neil R

Abstract

Using Monte Carlo methodology, this paper investigates the effect of dynamics and simultaneity on the finite sample properties of instrumental variables statistics for testing nested and nonnested hypotheses. Simple numerical-analytical formulae (response surfaces) are obtained which closely approximate the statistics' unknown size and power functions for a dynamic simultaneous-equations model. The analysis illustrates the value and limitations of asymptotic theory in interpreting finite sample properties. Two practical results arise. The F form and the Wald statistic is favored over its chi-squared form, and "large-sigma" and small "effective" sample size strongly affect the test of over-identifying restrictions and the Cox-type test. Copyright 1991 by The Econometric Society.

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Bibliographic Info

Article provided by Econometric Society in its journal Econometrica.

Volume (Year): 59 (1991)
Issue (Month): 5 (September)
Pages: 1249-77

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Handle: RePEc:ecm:emetrp:v:59:y:1991:i:5:p:1249-77

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Cited by:
  1. McAleer, Michael, 1995. "The significance of testing empirical non-nested models," Journal of Econometrics, Elsevier, vol. 67(1), pages 149-171, May.
  2. Lawrence J. Christiano & Wouter Den Haan, 1995. "Small sample properties of GMM for business cycle analysis," Staff Report 199, Federal Reserve Bank of Minneapolis.
  3. Julia Campos & Neil R. Ericsson & David F. Hendry, 1993. "Cointegration tests in the presence of structural breaks," International Finance Discussion Papers 440, Board of Governors of the Federal Reserve System (U.S.).
  4. Taylor, Mark P. & Sarno, Lucio, 1998. "The behavior of real exchange rates during the post-Bretton Woods period," Journal of International Economics, Elsevier, vol. 46(2), pages 281-312, December.
  5. Lawford, Steve & Stamatogiannis, Michalis P., 2009. "The finite-sample effects of VAR dimensions on OLS bias, OLS variance, and minimum MSE estimators," Journal of Econometrics, Elsevier, vol. 148(2), pages 124-130, February.
  6. Neil R. Ericsson & James G. MacKinnon, 2002. "Distributions of error correction tests for cointegration," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 285-318, 06.
  7. Francis X. Diebold & Celia Chen, 1993. "Testing structural stability with endogenous break point: a size comparison of analytic and bootstrap procedures," Working Papers 93-11, Federal Reserve Bank of Philadelphia.
  8. F. Javier Trivez & Beatriz Catalan, 2009. "Detecting level shifts in ARMA-GARCH (1,1) Models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(6), pages 679-697.
  9. Hamilton, James D., 1996. "Specification testing in Markov-switching time-series models," Journal of Econometrics, Elsevier, vol. 70(1), pages 127-157, January.

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