Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-27T02:12:18.706Z Has data issue: false hasContentIssue false

EFFICIENT METHOD OF MOMENTS IN MISSPECIFIED I.I.D. MODELS

Published online by Cambridge University Press:  08 June 2004

Víctor Aguirre-Torres
Affiliation:
Instituto Tecnológico Autónomo de México (ITAM)
Manuel Domínguez Toribio
Affiliation:
Instituto Tecnológico Autónomo de México (ITAM)

Abstract

The paper presents the asymptotic theory of the efficient method of moments when the model of interest is not correctly specified. The paper assumes a sequence of independent and identically distributed observations and a global misspecification. It is found that the limiting distribution of the estimator is still asymptotically normal, but it suffers a strong impact in the covariance matrix. A consistent estimator of this covariance matrix is provided. The large sample distribution on the estimated moment function is also obtained. These results are used to discuss the situation when the moment conditions hold but the model is misspecified. It also is shown that the overidentifying restrictions test has asymptotic power one whenever the limit moment function is different from zero. It is also proved that the bootstrap distributions converge almost surely to the previously mentioned distributions and hence they could be used as an alternative to draw inferences under misspecification. Interestingly, it is also shown that bootstrap can be reliably applied even if the number of bootstrap replications is very small.The authors express their gratitude to Professor A.R. Gallant for his support and for helpful discussions about this topic. They also thank Professor Donald Andrews for his revision and for the suggestion to read the work by Hall and Inoue (2003). They thank Hall and Inoue for sending them their technical report. Finally the authors thank two anonymous referees for their helpful comments, which led to a significant improvement of the paper. The views expressed in this paper are our sole responsibility. Víctor Aguirre-Torres acknowledges partial support from the Asociación Mexicana de la Cultura A.C. Manuel A. Domínguez acknowledges funding from CONACYT, research project J38076-D, and partial support from Asociación Mexicana de la Cultura A.C.

Type
Research Article
Copyright
© 2004 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Andrews, D. (2002) Higher order improvements of a computationally attractive k-step bootstrap for extremum estimators. Econometrica 70, 119162.Google Scholar
Arcones, M. & E. Giné (1992) On the bootstrap of M-estimators and other statistical functionals. In R. Lepage & L. Billard (eds.), Exploring the Limits of Bootstrap, pp. 1349. Wiley.
Bickel, P. & D. Freedman (1981) Some asymptotic theory for the bootstrap. Annals of Statistics 9, 11961217.Google Scholar
Davidson, R. & J. MacKinnon (1999) Bootstrap testing in nonlinear models. International Econometric Review 40, 487508.Google Scholar
Dhrymes, P.J (1984) Mathematics for Econometrics, 2nd ed. Springer-Verlag.
Domínguez, M.A. & V. Aguirre-Torres (2003) Broadening the Scope of the Bootstrap in Complex Problems. Working paper DE-C03.8, Department of Statistics, ITAM.
Gallant, A.R. (1987) Nonlinear Statistical Models. Wiley.
Gallant, A.R., D. Hsieh, & G. Tauchen (1997) Estimation of volatility models with diagnostics. Journal of Econometrics 81, 159192.Google Scholar
Gallant, A.R. & J.R. Long (1997) Estimating stochastic differential equations efficiently by minimum chi-squared. Biometrika 84, 125141.Google Scholar
Gallant, A.R. & D.W. Nychka (1987) Seminonparametric maximum likelihood estimation. Econometrica 55, 363390.Google Scholar
Gallant, A.R. & G. Tauchen (1996) Which moments to match? Econometric Theory 12, 657681.Google Scholar
Gallant, A.R. & G. Tauchen (1999a) The relative efficiency of method of moments estimators. Journal of Econometrics 92, 149172.Google Scholar
Gallant, A.R. & G. Tauchen (1999b) EMM: A program for efficient method of moments estimation. Version 1.4, anonymous ftp at ftp.econ.duke.edu in directory pub/get/emm.
Geweke, J. (1981) The approximate slopes of econometric tests. Econometrica 49, 14271442.Google Scholar
Hall, A.R. (2000) Covariance matrix estimation and the power of the overidentifying restrictions test. Econometrica 68, 15171527.Google Scholar
Hall, A.R., & Inoue, A. (2003) The large sample behaviour of the generalized method of moments estimator in misspecified models. Journal of Econometrics 114, 361394.Google Scholar
Newey, W.K. (1985) Generalized method of moments specification testing. Journal of Econometrics 29, 229256.Google Scholar
Serfling, R.J. (1980) Approximation Theorems of Mathematical Statistics. Wiley.
Tauchen, G. (1997) New minimum chi-square methods in empirical finance. In D. Kreps & K. Wallis (eds.), Advances in Econometrics, pp. 279317. Cambridge University Press.
White, H. (1982) Maximum likelihood of misspecified models. Econometrica 50, 125.Google Scholar