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Hypothesis testing and finite sample properties of generalized method of moments estimators: a Monte Carlo study

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  • Ching-Sheng Mao
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    Abstract

    Econometric methods based on the first-order conditions of intertemporal optimization models have gained increasing popularity in recent years. To a large extent, this development stems from the celebrated Lucas critique, which argued forcibly that traditional econometric models are not structural with respect to changes in the economic environment caused by policy regime shifts. The generalized method of moments (GMM) estimation procedure developed by Hansen (1982) is a leading example of a large research program in estimating parameters of taste and technology that are arguably invariant to shifts in policy rules. This estimation procedure has been used by many researchers to estimate nonlinear rational expectations models and has a major impact on the practice of macroeconomics.

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    Bibliographic Info

    Paper provided by Federal Reserve Bank of Richmond in its series Working Paper with number 90-12.

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    Date of creation: 1990
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    Handle: RePEc:fip:fedrwp:90-12

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    Keywords: Econometric models;

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    1. Den Haan, Wouter J & Marcet, Albert, 1994. "Accuracy in Simulations," Review of Economic Studies, Wiley Blackwell, vol. 61(1), pages 3-17, January.
    2. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July.
    3. Kydland, Finn E & Prescott, Edward C, 1982. "Time to Build and Aggregate Fluctuations," Econometrica, Econometric Society, vol. 50(6), pages 1345-70, November.
    4. King, Robert G. & Plosser, Charles I. & Rebelo, Sergio T., 1988. "Production, growth and business cycles : I. The basic neoclassical model," Journal of Monetary Economics, Elsevier, vol. 21(2-3), pages 195-232.
    5. Tauchen, George, 1986. "Statistical Properties of Generalized Method-of-Moments Estimators of Structural Parameters Obtained from Financial Market Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(4), pages 397-416, October.
    6. Hall, Robert E, 1988. "Intertemporal Substitution in Consumption," Journal of Political Economy, University of Chicago Press, vol. 96(2), pages 339-57, April.
    7. Ching-Sheng Mao, 1989. "Estimating intertemporal elasticity of substitution: the case of log- linear restrictions," Economic Review, Federal Reserve Bank of Richmond, issue Nov, pages 3-14.
    8. Hendry, David F., 1984. "Monte carlo experimentation in econometrics," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 16, pages 937-976 Elsevier.
    9. Eichenbaum, Martin S & Hansen, Lars Peter & Singleton, Kenneth J, 1988. "A Time Series Analysis of Representative Agent Models of Consumption and Leisure Choice under Uncertainty," The Quarterly Journal of Economics, MIT Press, vol. 103(1), pages 51-78, February.
    10. Singleton, Kenneth J., 1985. "Testing specifications of economic agents' intertemporal optimum problems in the presence of alternative models," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 391-413.
    11. Brock, William A. & Mirman, Leonard J., 1972. "Optimal economic growth and uncertainty: The discounted case," Journal of Economic Theory, Elsevier, vol. 4(3), pages 479-513, June.
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    Cited by:
    1. Weber, Christian E., 2002. "Intertemporal non-separability and "rule of thumb" consumption," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 293-308, March.
    2. Franke, Reiner & Westerhoff, Frank, 2011. "Structural stochastic volatility in asset pricing dynamics: Estimation and model contest," BERG Working Paper Series 78, Bamberg University, Bamberg Economic Research Group.
    3. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.
    4. Franke, Reiner, 2009. "Applying the method of simulated moments to estimate a small agent-based asset pricing model," Journal of Empirical Finance, Elsevier, vol. 16(5), pages 804-815, December.

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