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Computationally attractive stability tests for the efficient method of moments


This paper develops structural stability tests based on the Efficient Method of Moments for the case of a known breakpoint. Computationally attractive post-sample estimators and test-statistics for structural stability are proposed, which are modifications of the Lagrange Multiplier, Likelihood Ratio, Wald and Hansen tests for structural stability. The modifications retain the asymptotic optimality properties against certain local alternatives of those based on efficient computationally intensive estimators for the post-sample data. Evaluation of these tests is performed in the context of stochastic volatility models. For these types of models and datasets, readily available structural stability tests are important as these models are used in the pricing of options where the arrival of new data constantly raises the issue of whether the estimates are in need of updating. A Monte Carlo study gives encouraging results for the computationally attractive tests. An application is made to stochastic volatility models for daily returns of the S&P500 index ranging from 1981 to 1993. The tests do not reject the null hypothesis of structural stability for the final model.

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Article provided by Royal Economic Society in its journal The Econometrics Journal.

Volume (Year): 1 (1998)
Issue (Month): ConferenceIssue ()
Pages: C203-C227

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Handle: RePEc:ect:emjrnl:v:1:y:1998:i:conferenceissue:p:c203-c227
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  1. Hull, John C & White, Alan D, 1987. " The Pricing of Options on Assets with Stochastic Volatilities," Journal of Finance, American Finance Association, vol. 42(2), pages 281-300, June.
  2. Ghysels, Eric & Hall, Alastair, 1990. "A Test for Structural Stability of Euler Conditions Parameters Estimated via the Generalized Method of Moments Estimator," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 31(2), pages 355-64, May.
  3. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  4. Andrews, Donald W K & Fair, Ray C, 1988. "Inference in Nonlinear Econometric Models with Structural Change," Review of Economic Studies, Wiley Blackwell, vol. 55(4), pages 615-39, October.
  5. Gallant, A. Ronald & Tauchen, George, 1996. "Which Moments to Match?," Econometric Theory, Cambridge University Press, vol. 12(04), pages 657-681, October.
  6. Ghysels, Eric & Hall, Alastair, 1990. "Are consumption-based intertemporal capital asset pricing models structural?," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 121-139.
  7. Andersen, Torben G & Sorensen, Bent E, 1996. "GMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 328-52, July.
  8. Fenton, Victor M & Gallant, A Ronald, 1996. "Erratum [Convergence Rates of SNP Density Estimators]," Econometrica, Econometric Society, vol. 64(6), pages 1493, November.
  9. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
  10. Gallant, A Ronald & Nychka, Douglas W, 1987. "Semi-nonparametric Maximum Likelihood Estimation," Econometrica, Econometric Society, vol. 55(2), pages 363-90, March.
  11. Tauchen, George & Zhang, Harold & Liu, Ming, 1996. "Volume, volatility, and leverage: A dynamic analysis," Journal of Econometrics, Elsevier, vol. 74(1), pages 177-208, September.
  12. Ming Liu & Harold Zhang, 1996. "Specification Tests in the Efficient Method of Moments Framework with Application to the Stochastic Volatility Models," GSIA Working Papers 34, Carnegie Mellon University, Tepper School of Business.
  13. Hoffman, D. & Pagan, A., 1988. "Post-Sample Prediction Tests For Generalized Method Of Moment Estimators," RCER Working Papers 129, University of Rochester - Center for Economic Research (RCER).
  14. Danielsson, Jon, 1994. "Stochastic volatility in asset prices estimation with simulated maximum likelihood," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 375-400.
  15. 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.
  16. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 1994. "Bayesian Analysis of Stochastic Volatility Models: Comments: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 413-17, October.
  17. Peter C.B. Phillips, 1982. "ERA's: A New Approach to Small Sample Theory," Cowles Foundation Discussion Papers 645, Cowles Foundation for Research in Economics, Yale University.
  18. K. Newey, Whitney, 1985. "Generalized method of moments specification testing," Journal of Econometrics, Elsevier, vol. 29(3), pages 229-256, September.
  19. Victor Fenton & Gallant, A. Ronald, 1996. "Qualitative and Asymptotic Performance of SNP Density Estimators," Working Papers 96-17, Duke University, Department of Economics.
  20. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 2002. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 69-87, January.
  21. Fenton, Victor M & Gallant, A Ronald, 1996. "Convergence Rates of SNP Density Estimators," Econometrica, Econometric Society, vol. 64(3), pages 719-27, May.
  22. Nelson, Daniel B & Foster, Dean P, 1994. "Asymptotic Filtering Theory for Univariate ARCH Models," Econometrica, Econometric Society, vol. 62(1), pages 1-41, January.
  23. Nelson, Daniel B & Cao, Charles Q, 1992. "Inequality Constraints in the Univariate GARCH Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 229-35, April.
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