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The Objective Function Of Simulation Estimators Near The Boundary Of The Unstable Region Of The Parameter Space

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  • George Tauchen

Abstract

The paper examines the role of stability constraints in estimation by dynamic simulation. In particular, it analyzes the behavior of the objective function on either side of the boundary of the stability region of the parameter space. The main finding is that stability constraints may be ignored because the simulation-based objective function contains a built-in penalty to enforce stability. A key caveat, however, is that the dynamic stability of the auxiliary model that defines the moment conditions must be checked and enforced. An attempt to fit via simulation to moments defined by a dynamically unstable auxiliary model can be expected to lead to an ill-behaved objective function. © 1998 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology

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  • George Tauchen, 1998. "The Objective Function Of Simulation Estimators Near The Boundary Of The Unstable Region Of The Parameter Space," The Review of Economics and Statistics, MIT Press, vol. 80(3), pages 389-398, August.
    • Handle: RePEc:tpr:restat:v:80:y:1998:i:3:p:389-398
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    1. Lee, Bong-Soo & Ingram, Beth Fisher, 1991. "Simulation estimation of time-series models," Journal of Econometrics, Elsevier, vol. 47(2-3), pages 197-205, February.
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    4. 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 328-352, July.
    5. Foster, F Douglas & Viswanathan, S, 1995. "Can Speculative Trading Explain the Volume-Volatility Relation?," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(4), pages 379-396, October.
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    9. Gallant, A. Ronald & Tauchen, George, 1997. "Estimation Of Continuous-Time Models For Stock Returns And Interest Rates," Macroeconomic Dynamics, Cambridge University Press, vol. 1(1), pages 135-168, January.
    10. Gallant, A. Ronald & Hsieh, David & Tauchen, George, 1997. "Estimation of stochastic volatility models with diagnostics," Journal of Econometrics, Elsevier, vol. 81(1), pages 159-192, November.
    11. Laroque, Guy & Salanie, Bernard, 1989. "Estimation of Multi-market Fix-Price Models: An Application of Pseudo Maximum Likelihood Methods," Econometrica, Econometric Society, vol. 57(4), pages 831-860, July.
    12. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    13. 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.
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    2. Gallant, A. Ronald & Tauchen, George, 2002. "Simulated Score Methods and Indirect Inference for Continuous-time Models," Working Papers 02-09, Duke University, Department of Economics.
    3. Dobrescu, Loretti I. & Kotlikoff, Laurence J. & Motta, Alberto, 2012. "Why aren't developed countries saving?," European Economic Review, Elsevier, vol. 56(6), pages 1261-1275.
    4. Nikolay Gospodinov & Serena Ng, 2015. "Minimum Distance Estimation of Possibly Noninvertible Moving Average Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 403-417, July.
    5. Yacine Ait-Sahalia, 1998. "Maximum Likelihood Estimation of Discretely Sampled Diffusions: A Closed-Form Approach," NBER Technical Working Papers 0222, National Bureau of Economic Research, Inc.
    6. Ronald Gallant, A. & Tauchen, George, 1999. "The relative efficiency of method of moments estimators1," Journal of Econometrics, Elsevier, vol. 92(1), pages 149-172, September.

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