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Small Sample Properties of Generalized Method of Moments Based Wald Tests Author info | Abstract | Publisher info | Download info | Related research | Statistics Craig Burnside
Martin Eichenbaum
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This paper assesses the small sample properties of Generalized Method of Moments (GMM) based Wald statistics. The analysis is conducted assuming that the data generating process corresponds to (i) a simple vector white noise process and (ii) an equilibrium business cycle model. Our key findings are that the small sample size of the Wald tests exceeds their asymptotic size, and that their size increases uniformly with the dimensionality of joint hypotheses. For tests involving even moderate numbers of moment restrictions, the small sample size of the tests greatly exceeds their asymptotic size. Relying on asymptotic distribution theory leads one to reject joint hypothesis tests far too often. We argue that the source of the problem is the difficulty of estimating the spectral density matrix of the GMM residuals, which is needed to conduct inference in a GMM environment. Imposing restrictions implied by the underlying economic model being investigated or the null hypothesis being tested on this spectral density matrix can lead to substantial improvements in the small sample properties of the Wald tests.
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Date of creation: May 1994Date of revision:
Handle: RePEc:nbr:nberte:0155Note: EFGContact details of provider: Postal: National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A. Phone: 617-868-3900 Email: Web page: http://www.nber.org More information through EDIRC
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Find related papers by JEL classification: C5 - Mathematical and Quantitative Methods - - Econometric Modeling C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
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references Cited by : (explanations , Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile , click on "citations" and make appropriate adjustments.)
Lawrence J. Christiano & Wouter Den Haan, 1995.
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Staff Report
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NBER Technical Working Papers
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Working Paper Series, Macroeconomic Issues
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