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Efficient Semiparametric Estimation of Expectations in Dynamic Nonlinear Systems

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  • Jeon, Byung M.

    (Rice U)

  • Brown, Bryan

Abstract

Semiparametric estimation of the expectations of a general class of dynamic functions is considered. Such expectation functionals that are of interest for dynamic models are one- and multi-period ahead forecasting functions, distribution functions, and covariance matrices. The semiparametric efficiency bound for this problem is established and an estimator, which attains the bound is developed. The explicit form of the semiparmetric efficient expectation estimator is worked out for several explicit assumptions regarding the degree of dependence between the predetermined variables and the disturbances of the model. Under the assumption of independence, the one- and multi-period ahead residual-based predictors proposed by Brown and Mariano (1989) are shown to be semiparametric efficient. Under unconditional mean zero assumption, we propose an improved heteroskedastic autocorrelation consistent estimator.

Suggested Citation

  • Jeon, Byung M. & Brown, Bryan, 2001. "Efficient Semiparametric Estimation of Expectations in Dynamic Nonlinear Systems," Working Papers 2001-09, Rice University, Department of Economics.
    • Handle: RePEc:ecl:riceco:2001-09
    as

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    File URL: http://www.ruf.rice.edu/~econ/papers/2001papers/09Jeon.pdf
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    References listed on IDEAS

    as
    1. Brown, Bryan W. & Mariano, Roberto S., 1989. "Predictors in Dynamic Nonlinear Models: Large-Sample Behavior," Econometric Theory, Cambridge University Press, vol. 5(3), pages 430-452, December.
    2. Bryan W. Brown & Whitney K. Newey, 1998. "Efficient Semiparametric Estimation of Expectations," Econometrica, Econometric Society, vol. 66(2), pages 453-464, March.
    3. Andrews, Donald W K, 1987. "Consistency in Nonlinear Econometric Models: A Generic Uniform Law of Large Numbers [On Unification of the Asymptotic Theory of Nonlinear Econometric Models]," Econometrica, Econometric Society, vol. 55(6), pages 1465-1471, November.
    4. White, Halbert & Domowitz, Ian, 1984. "Nonlinear Regression with Dependent Observations," Econometrica, Econometric Society, vol. 52(1), pages 143-161, January.
    5. Brown, Bryan W & Maital, Shlomo, 1981. "What Do Economists Know? An Empirical Study of Experts' Expectations," Econometrica, Econometric Society, vol. 49(2), pages 491-504, March.
    6. Bryan W. Brown & Douglas J. Hodgson, 2007. "Semiparametric efficiency bounds in dynamic non-linear systems under elliptical symmetry," Econometrics Journal, Royal Economic Society, vol. 10(1), pages 35-48, March.
    7. Brown, Bryan W & Mariano, Roberto S, 1984. "Residual-Based Procedures for Prediction and Estimation in a Nonlinear Simultaneous System," Econometrica, Econometric Society, vol. 52(2), pages 321-343, March.
    8. Whitney Newey & Kenneth West, 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.
    9. Newey, Whitney K, 1991. "Uniform Convergence in Probability and Stochastic Equicontinuity," Econometrica, Econometric Society, vol. 59(4), pages 1161-1167, July.
    10. Back, Kerry & Brown, David P, 1993. "Implied Probabilities in GMM Estimators," Econometrica, Econometric Society, vol. 61(4), pages 971-975, July.
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    Cited by:

    1. Park, Byeong U. & Sickles, Robin C. & Simar, Leopold, 2003. "Corrigendum to "Semiparametric-efficient estimation of AR(1) panel data models": [J. Econom. 117 (2003) 279-309]," Journal of Econometrics, Elsevier, vol. 117(2), pages 311-311, December.

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