Abstract
For a parametric econometric model with possibly latent variables, the simulation tool and Monte Carlo integration provide a versatile minimum distance estimation principle. The general approach is dubbed simulation-based indirect inference. It can take advantage of any instrumental piece of information that identifies the structural parameters. Examples include the simulated method of moments and its simulated-score-matching version. Monte Carlo integration also allows numerical assessment of the criterion to maximize for M-estimation. Asymptotic efficiency is reached by the simulated maximum likelihood or a simulated score technique. Since the simulator is provided by the structural model, the classical trade-off between efficiency and robustness to misspecification must be revisited.
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Bibliography
Andrews, D. 1993. Exactly median unbiased estimation of first order autoregressive/ unit root models. Econometrica 61: 139–165.
Danielsson, J., and J.F. Richard. 1993. Accelerated Gaussian importance sampler with application to dynamic latent variables models. Journal of Applied Econometrics 8: S153–S173.
Dridi, R., A. Guay, and E. Renault. 2007. Indirect inference and calibration of dynamic stochastic general equilibrium models. Journal of Econometrics 136: 397–430.
Duffie, D., and K. Singleton. 1993. Simulated moments estimation of Markov models of asset prices. Econometrica 61: 929–952.
Engle, R.F., and G.G.J. Lee. 1996. Estimating diffusion models of stochastic volatility. In Modeling stock market volatility: Bridging the gap to continuous time, ed. P.E. Rossi. New York: Academic Press.
Gallant, A.R., and G. Tauchen. 1996. Which moments to match? Econometric Theory 12: 657–681.
Gourieroux, C., and A. Monfort. 1995. Testing, encompassing, and simulating dynamic econometric models. Econometric Theory 11: 195–228.
Gourieroux, C., and A. Monfort. 1996. Simulation-based econometric methods. In Core lectures. Oxford: Oxford University Press.
Gourieroux, C., A. Monfort, and A. Trognon. 1984. Pseudo-maximum likelihood methods theory. Econometrica 52: 681–700.
Gourieroux, C., A. Monfort, and E. Renault. 1993. Indirect inference. Journal of Applied Econometrics 8: S85–118.
Gourieroux, C., E. Renault, and N. Touzi. 2000. Calibration by simulation for small sample bias correction. In Simulation-based inference in econometrics, ed. R. Mariano, T. Schuerman, and M.J. Weeks. Cambridge: Cambridge University Press.
Hajivassiliou, V.A. 1993. Simulation estimation methods for limited dependent variable models. In Handbook of statistics, ed. G.S. Maddala, C.R. Rao, and H. Vinod, Vol. 11. Amsterdam: North-Holland.
Hall, P. 1992. The bootstrap and Edgeworth expansion. New York: Springer.
Hansen, L.P. 1982. Large sample properties of generalized method of moments. Econometrica 50: 1029–1054.
Horowitz, J.L. 1997. Bootstrap methods in econometrics: theory and numerical performance. In Advances in econometrics, seventh world congress, ed. D. Kreps and K. Wallis. Cambridge: Cambridge University Press.
Ingram, B.F., and B.S. Lee. 1991. Estimation by simulation of time series models. Journal of Econometrics 47: 197–207.
Laffont, J.J., H. Ossard, and Q. Vuong. 1995. Econometrics of first-price auction. Econometrica 63: 953–980.
Laroque, G., and B. Salanie. 1993. Simulation based estimation of models with lagged latent variables. Journal of Applied Econometrics 8: S119–S133.
Li, T. 2006. Indirect inference in structural econometric models. Journal of Econometrics 157: 120–128.forthcoming.
Monfort, A. 1996. A reappraisal of misspecified econometric models. Econometric Theory 12: 597–619.
Monfort, A., and H.K. Van Dijk. 1995. Simulation-based econometrics. In Econometric inference using simulation techniques, ed. H.K. Van Dijk, A. Monfort, and B.W. Brown. New York: Wiley.
Pan, J. 2002. The jump-risk premia implicit in options: Evidence from an integrated time-series study. Journal of Financial Economics 63: 3–50.
Pastorello, S., E. Renault, and N. Touzi. 2000. Statistical inference for random-variance option pricing. Journal of Business and Economic Statistics 18: 358–367.
Pastorello, S., V. Patilea, and E. Renault. 2003. Iterative and recursive estimation in structural non-adaptive models. Invited Lecture with discussion. Journal of Business, Economics and Statistics 21: 449–509.
Renault, E. 1997. Econometric models of option pricing errors. In Advances in econometrics, seventh world congress, ed. D. Kreps and K. Wallis. Cambridge: Cambridge University Press.
Shephard, N. 1993. Fitting nonlinear time series with applications to stochastic variance models. Journal of Applied Econometrics 8: S134–S152.
Smith, A. 1993. Estimating nonlinear time series models using simulated vector autoregressions. Journal of Applied Econometrics 8: S63–S84.
White, H. 1982. Maximum likelihood estimation of mis-specified models. Econometrica 50: 1–25.
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Renault, E. (2018). Simulation-Based Estimation. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95189-5_2532
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DOI: https://doi.org/10.1057/978-1-349-95189-5_2532
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