A Practitioner's Guide to Robust Covariance Matrix Estimation

Wouter J. Den Haan and Andrew Levin (andrew.t.levin@dartmouth.edu)
Authors registered in the RePEc Author Service: Wouter Denhaan (wjdenhaan@gmail.com)

No 197, NBER Technical Working Papers from National Bureau of Economic Research, Inc

Abstract: This paper develops asymptotic distribution theory for generalized method of moments (GMM) estimators and test statistics when some of the parameters are well identified, but others are poorly identified because of weak instruments. The asymptotic theory entails applying empirical process theory to obtain a limiting representation of the (concentrated) objective function as a stochastic process. The general results are specialized to two leading cases, linear instrumental variables regression and GMM estimation of Euler equations obtained from the consumption-based capital asset pricing model with power utility. Numerical results of the latter model confirm that finite sample distributions can deviate substantially from normality, and indicate that these deviations are captured by the weak instruments asymptotic approximations.

JEL-codes: C12 C14 (search for similar items in EconPapers)
Date: 1996-06
Note: EFG
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (48)

Published as Handbook of Statistics 15. edited by G.S. Maddala and C.R. Rao, 1997

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Related works:
Software Item: VARHAC Covariance Matrix Estimator (FORTRAN) (1996)
Software Item: VARHAC Covariance Matrix Estimator (GAUSS) (1996)
Software Item: VARHAC Covariance Matrix Estimator (RATS) (1996)
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