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The Objective Function of Simulation Estimators Near the Boundary of the Unstable Region of the Parameter Space

  • Tauchen, George

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.

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Paper provided by Duke University, Department of Economics in its series Working Papers with number 97-14.

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Date of creation: 1997
Date of revision:
Publication status: Published in REVIEW OF ECONOMICS AND STATISTICS, Vol. 80, 1998, pages 389-398
Handle: RePEc:duk:dukeec:97-14
Contact details of provider: Postal: Department of Economics Duke University 213 Social Sciences Building Box 90097 Durham, NC 27708-0097
Phone: (919) 660-1800
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Web page: http://econ.duke.edu/

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  1. Bansal, Ravi & Gallant, A. Ronald & Hussey, Robert & Tauchen, George, 1995. "Nonparametric estimation of structural models for high-frequency currency market data," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 251-287.
  2. 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 379-96, October.
  3. 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.
  4. Torben G. Andersen & Bent E. Sorensen, 1995. "GMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study," Discussion Papers 95-19, University of Copenhagen. Department of Economics.
  5. Darrell Duffie & Kenneth J. Singleton, 1990. "Simulated Moments Estimation of Markov Models of Asset Prices," NBER Technical Working Papers 0087, National Bureau of Economic Research, Inc.
  6. Donald W.K. Andrews, 1988. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Cowles Foundation Discussion Papers 877R, Cowles Foundation for Research in Economics, Yale University, revised Jul 1989.
  7. Gallant, A. Ronald & Hsieh, David & Tauchen, George, 1995. "Estimation of Stochastic Volatility Models with Diagnostics," Working Papers 95-36, Duke University, Department of Economics.
  8. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
  9. Abadir, Karim M., 1993. "On the Asymptotic Power of Unit Root Tests," Econometric Theory, Cambridge University Press, vol. 9(02), pages 189-221, April.
  10. Gallant, A. Ronald & Tauchen, George, 1997. "Estimation Of Continuous-Time Models For Stock Returns And Interest Rates," Macroeconomic Dynamics, Cambridge University Press, vol. 1(01), pages 135-168, January.
  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 831-60, July.
  12. Smith, A A, Jr, 1993. "Estimating Nonlinear Time-Series Models Using Simulated Vector Autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages S63-84, Suppl. De.
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