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New Minimum Chi-Square Methods in Empirical Finance

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  • Tauchen, George E.

Abstract

This paper reviews recently developed simulation-based minimum chi-square estimators for structural models. Particular attention is paid to selection of the auxiliary model that defines the GMM-type criterion used in the minimum chi-square estimation. Considerations of statistical efficiency and behavior under misspecification make a strong case for using a very flexible, nonparametric approach to select the auxiliary model. To avoid a numerically ill-behaved GMM criterion function, the dynamic stability of the auxiliary model must also be verified, though, interestly, the dynamic stability of the structural model itself is automatically enforced and need not be imposed in estimation. The empirical application involves estimation of a single-fator diffusion model for the 30-day Eurodollar interest rate.

Suggested Citation

  • Tauchen, George E., 1995. "New Minimum Chi-Square Methods in Empirical Finance," Working Papers 95-42, Duke University, Department of Economics.
    • Handle: RePEc:duk:dukeec:95-42
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    Cited by:

    1. Pieter J. van der Sluis, 1997. "Post-Sample Prediction Tests for the Efficient Method of Moments," Tinbergen Institute Discussion Papers 97-054/4, Tinbergen Institute.
    2. Ghysels, E. & Harvey, A. & Renault, E., 1995. "Stochastic Volatility," Papers 95.400, Toulouse - GREMAQ.
    3. Pieter J. Van Der Sluis, 1998. "Computationally attractive stability tests for the efficient method of moments," Econometrics Journal, Royal Economic Society, vol. 1(Conferenc), pages 203-227.
    4. Andersen, Torben G. & Lund, Jesper, 1997. "Estimating continuous-time stochastic volatility models of the short-term interest rate," Journal of Econometrics, Elsevier, vol. 77(2), pages 343-377, April.
    5. Rómulo Chumacero & Jorge Quiroz, 1996. "La Tasa Natural de Crecimiento de la Economía Chilena: 1985-1996," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 33(100), pages 453-472.
    6. Martin, Vance L. & Wilkins, Nigel P., 1999. "Indirect estimation of ARFIMA and VARFIMA models," Journal of Econometrics, Elsevier, vol. 93(1), pages 149-175, November.
    7. Des Mc Manus & David Watt, 1999. "Estimating One-Factor Models of Short-Term Interest Rates," Staff Working Papers 99-18, Bank of Canada.
    8. Kristensen, Dennis, 2004. "Estimation in two classes of semiparametric diffusion models," LSE Research Online Documents on Economics 24739, London School of Economics and Political Science, LSE Library.
    9. Seungmoon Choi, 2015. "Maximum Likelihood Estimation of Continuous-Time Diffusion Models for Korean Short-Term Interest Rates," Economic Analysis (Quarterly), Economic Research Institute, Bank of Korea, vol. 21(4), pages 28-58, December.
    10. Durham, Garland B., 2003. "Likelihood-based specification analysis of continuous-time models of the short-term interest rate," Journal of Financial Economics, Elsevier, vol. 70(3), pages 463-487, December.

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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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