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Moment Restriction-based Econometric Methods: An Overview

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Author Info

  • Naoto Kunitomo

    (Faculty of Economics, University of Tokyo)

  • Michael McAleer

    (Erasmus University Rotterdam, Tinbergen Institute, The Netherlands, and Institute of Economic Research, Kyoto University)

  • Yoshihiko Nishiyama

    (Institute of Economic Research, Kyoto University)

Abstract

Moment restriction-based econometric modelling is a broad class which includes the parametric, semiparametric and nonparametric approaches. Moments and conditional moments themselves are nonparametric quantities. If a model is specified in part up to some finite dimensional parameters, this will provide semiparametric estimates or tests. If we use the score to construct moment restrictions to estimate finite dimensional parameters, this yields maximum likelihood (ML) estimates. Semiparametric or nonparametric settings based on moment restrictions have been the main concern in the literature, and comprise the most important and interesting topics. The purpose of this special issue on "Moment Restriction-based Econometric Methods" is to highlight some areas in which novel econometric methods have contributed significantly to the analysis of moment restrictions, specifically asymptotic theory for nonparametric regression with spatial data, a control variate method for stationary processes, method of moments estimation and identifiability of semiparametric nonlinear errors-in-variables models, properties of the CUE estimator and a modification with moments, finite sample properties of alternative estimators of coefficients in a structural equation with many instruments, instrumental variable estimation in the presence of many moment conditions, estimation of conditional moment restrictions without assuming parameter identifiability in the implied unconditional moments, moment-based estimation of smooth transition regression models with endogenous variables, a consistent nonparametric test for nonlinear causality, and linear programming-based estimators in simple linear regression.

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Bibliographic Info

Paper provided by Kyoto University, Institute of Economic Research in its series KIER Working Papers with number 734.

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Length: 16pages
Date of creation: Oct 2010
Date of revision:
Handle: RePEc:kyo:wpaper:734

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Keywords: Moment restrictions; Parametric; semiparametric and nonparametric methods; Estimation; Testing; Robustness; Model misspecification.;

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References

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  1. Yatchew, Adonis John, 1992. "Nonparametric Regression Tests Based on Least Squares," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 8(04), pages 435-451, December.
  2. Bierens, Herman J, 1990. "A Consistent Conditional Moment Test of Functional Form," Econometrica, Econometric Society, Econometric Society, vol. 58(6), pages 1443-58, November.
  3. Waldyr Dutra Areosa & Michael McAleer & Marcelo C. Medeiros, 2009. "Moment-Based Estimation of Smooth Transition Regression Models with Endogenous Variables," CIRJE F-Series, CIRJE, Faculty of Economics, University of Tokyo CIRJE-F-671, CIRJE, Faculty of Economics, University of Tokyo.
  4. Okui, Ryo, 2011. "Instrumental variable estimation in the presence of many moment conditions," Journal of Econometrics, Elsevier, Elsevier, vol. 165(1), pages 70-86.
  5. Preve, Daniel & Medeiros, Marcelo C., 2011. "Linear programming-based estimators in simple linear regression," Journal of Econometrics, Elsevier, Elsevier, vol. 165(1), pages 128-136.
  6. Kohtarro Hitomi & Yoshinori Kawasaki & Ryo Okui & Yoshihiko Nishiyama, 2005. "A Consistent Nonparametric Test for Causality," KIER Working Papers, Kyoto University, Institute of Economic Research 602, Kyoto University, Institute of Economic Research.
  7. Whang, Yoon-Jae & Andrews, Donald W. K., 1993. "Tests of specification for parametric and semiparametric models," Journal of Econometrics, Elsevier, Elsevier, vol. 57(1-3), pages 277-318.
  8. T. W. Anderson & Naoto Kunitomo & Yukitoshi Matsushita, 2008. "On Finite Sample Properties of Alternative Estimators of Coefficients in a Structural Equation with Many Instruments," CIRJE F-Series, CIRJE, Faculty of Economics, University of Tokyo CIRJE-F-577, CIRJE, Faculty of Economics, University of Tokyo.
  9. Morimune, Kimio, 1983. "Approximate Distributions of k-Class Estimators When the Degree of Overidentifiability Is Large Compared with the Sample Size," Econometrica, Econometric Society, Econometric Society, vol. 51(3), pages 821-41, May.
  10. Hausman, Jerry & Lewis, Randall & Menzel, Konrad & Newey, Whitney, 2011. "Properties of the CUE estimator and a modification with moments," Journal of Econometrics, Elsevier, Elsevier, vol. 165(1), pages 45-57.
  11. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, Econometric Society, vol. 50(4), pages 1029-54, July.
  12. Fan, Yanqin & Li, Qi, 1996. "Consistent Model Specification Tests: Omitted Variables and Semiparametric Functional Forms," Econometrica, Econometric Society, Econometric Society, vol. 64(4), pages 865-90, July.
  13. Wang, Liqun & Hsiao, Cheng, 2011. "Method of moments estimation and identifiability of semiparametric nonlinear errors-in-variables models," Journal of Econometrics, Elsevier, Elsevier, vol. 165(1), pages 30-44.
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