IDEAS home Printed from https://ideas.repec.org/p/ecl/riceco/2001-09.html
   My bibliography  Save this paper

Efficient Semiparametric Estimation of Expectations in Dynamic Nonlinear Systems

Author

Listed:
  • 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

    Download full text from publisher

    File URL: http://www.ruf.rice.edu/~econ/papers/2001papers/09Jeon.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Newey, Whitney K, 1991. "Uniform Convergence in Probability and Stochastic Equicontinuity," Econometrica, Econometric Society, vol. 59(4), pages 1161-1167, July.
    2. 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.
    3. Bryan W. Brown & Whitney K. Newey, 1998. "Efficient Semiparametric Estimation of Expectations," Econometrica, Econometric Society, vol. 66(2), pages 453-464, March.
    4. 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.
    5. White, Halbert & Domowitz, Ian, 1984. "Nonlinear Regression with Dependent Observations," Econometrica, Econometric Society, vol. 52(1), pages 143-161, January.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Back, Kerry & Brown, David P, 1993. "Implied Probabilities in GMM Estimators," Econometrica, Econometric Society, vol. 61(4), pages 971-975, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

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

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. van Garderen, Kees Jan & Lee, Kevin & Pesaran, M. Hashem, 2000. "Cross-sectional aggregation of non-linear models," Journal of Econometrics, Elsevier, vol. 95(2), pages 285-331, April.
    2. Almeida, Caio & Garcia, René, 2012. "Assessing misspecified asset pricing models with empirical likelihood estimators," Journal of Econometrics, Elsevier, vol. 170(2), pages 519-537.
    3. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521520911.
    4. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, January.
    5. de Jong, Robert M., 1998. "Uniform laws of large numbers and stochastic Lipschitz-continuity," Journal of Econometrics, Elsevier, vol. 86(2), pages 243-268, June.
    6. Guggenberger, Patrik & Ramalho, Joaquim J.S. & Smith, Richard J., 2012. "GEL statistics under weak identification," Journal of Econometrics, Elsevier, vol. 170(2), pages 331-349.
    7. Whang, Yoon-Jae & Andrews, Donald W. K., 1993. "Tests of specification for parametric and semiparametric models," Journal of Econometrics, Elsevier, vol. 57(1-3), pages 277-318.
    8. Paulo M. D. C. Parente & Richard J. Smith, 2021. "Quasi‐maximum likelihood and the kernel block bootstrap for nonlinear dynamic models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(4), pages 377-405, July.
    9. Graham Elliott & Ivana Komunjer & Allan Timmermann, 2008. "Biases in Macroeconomic Forecasts: Irrationality or Asymmetric Loss?," Journal of the European Economic Association, MIT Press, vol. 6(1), pages 122-157, March.
    10. Whitney K. Newey & Frank Windmeijer, 2005. "GMM with many weak moment conditions," CeMMAP working papers CWP18/05, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Joachim Inkmann, 2000. "Finite Sample Properties of One-Step, Two-Step and Bootstrap Empirical Likelihood Approaches to Efficient GMM Estimation," Econometric Society World Congress 2000 Contributed Papers 0332, Econometric Society.
    12. Frank Kleibergen, 2004. "Expansions of GMM statistics that indicate their properties under weak and/or many instruments and the bootstrap," Econometric Society 2004 North American Summer Meetings 408, Econometric Society.
    13. Richard H. Clarida & Lucio Sarno & Mark P. Taylor & Giorgio Valente, 2006. "The Role of Asymmetries and Regime Shifts in the Term Structure of Interest Rates," The Journal of Business, University of Chicago Press, vol. 79(3), pages 1193-1224, May.
    14. Shane M. Sherlund, 2004. "Quasi Empirical Likelihood Estimation of Moment Condition Models," Econometric Society 2004 North American Summer Meetings 507, Econometric Society.
    15. Clarida, Richard H. & Sarno, Lucio & Taylor, Mark P. & Valente, Giorgio, 2003. "The out-of-sample success of term structure models as exchange rate predictors: a step beyond," Journal of International Economics, Elsevier, vol. 60(1), pages 61-83, May.
    16. Giuseppe Ragusa, 2011. "Minimum Divergence, Generalized Empirical Likelihoods, and Higher Order Expansions," Econometric Reviews, Taylor & Francis Journals, vol. 30(4), pages 406-456, August.
    17. Jose A. Lopez & Christian Walter, 1997. "Is implied correlation worth calculating? Evidence from foreign exchange options and historical data," Research Paper 9730, Federal Reserve Bank of New York.
    18. Russell Davidson & Victoria Zinde‐Walsh, 2017. "Advances in specification testing," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 50(5), pages 1595-1631, December.
    19. Marcelo Moreira & Geert Ridder, 2019. "Efficiency loss of asymptotically efficient tests in an instrumental variables regression," CeMMAP working papers CWP03/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    20. Mikio Ito & Akihiko Noda, 2012. "The GEL estimates resolve the risk-free rate puzzle in Japan," Applied Financial Economics, Taylor & Francis Journals, vol. 22(5), pages 365-374, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ecl:riceco:2001-09. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/dericus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.