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Simulation based estimation of threshold moving average models with contemporaneous shock asymmetry

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  • Taştan, Hüseyin

Abstract

Persistence of shocks to macroeconomic time series may differ depending on the sign or on whether a threshold value is crossed. For example, positive shocks to gross domestic product may be more persistent than negative shocks. Threshold (or asymmetric) moving average (TMA) models, by explicitly taking into account threshold behavior, can help discriminate whether there exists persistence asymmetry. Recently, building on the works of Wecker (1981, JASA, 76(373)) and De Gooijer (1998, JTSA, 19(1)) among others, Guay and Scaillet (2003, JBES, 21(1)) proposed TMA model in which both contemporaneous and lagged asymmetric effects are present and provided indirect inference framework for estimation and testing. This paper builds on their work and examines the properties of efficient method of moments (EMM) estimation of TMA class of models using Monte Carlo simulation experiments. The model is also applied to analyze the persistence properties of shocks in Turkish business cycles.

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  • Taştan, Hüseyin, 2011. "Simulation based estimation of threshold moving average models with contemporaneous shock asymmetry," MPRA Paper 34302, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:34302
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    References listed on IDEAS

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    1. Beaudry, Paul & Koop, Gary, 1993. "Do recessions permanently change output?," Journal of Monetary Economics, Elsevier, vol. 31(2), pages 149-163, April.
    2. Sichel, Daniel E, 1993. "Business Cycle Asymmetry: A Deeper Look," Economic Inquiry, Western Economic Association International, vol. 31(2), pages 224-236, April.
    3. Jan G. De Gooijer & Kurt Brännäs, 2004. "Asymmetries in conditional mean and variance: modelling stock returns by asMA-asQGARCH," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(3), pages 155-171.
    4. Babsiri, Mohamed El & Zakoian, Jean-Michel, 2001. "Contemporaneous asymmetry in GARCH processes," Journal of Econometrics, Elsevier, vol. 101(2), pages 257-294, April.
    5. Gallant, A. Ronald & Tauchen, George, 1996. "Which Moments to Match?," Econometric Theory, Cambridge University Press, vol. 12(4), pages 657-681, October.
    6. Huseyin Tastan & Nuri Yildirim, 2008. "Business cycle asymmetries in Turkey: an application of Markov-switching autoregressions," International Economic Journal, Taylor & Francis Journals, vol. 22(3), pages 315-333.
    7. Michaelides, Alexander & Ng, Serena, 2000. "Estimating the rational expectations model of speculative storage: A Monte Carlo comparison of three simulation estimators," Journal of Econometrics, Elsevier, vol. 96(2), pages 231-266, June.
    8. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
    9. Elwood, S. Kirk, 1998. "Is the persistence of shocks to output asymmetric?," Journal of Monetary Economics, Elsevier, vol. 41(2), pages 411-426, April.
    10. 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.
    11. Alain Guay & Olivier Scaillet, 1999. "Indirect Inference, Nuisance Parameter and Threshold Moving Average," Cahiers de recherche CREFE / CREFE Working Papers 95, CREFE, Université du Québec à Montréal.
    12. 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.
    13. Gonzalo, Jesus & Martinez, Oscar, 2006. "Large shocks vs. small shocks. (Or does size matter? May be so.)," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 311-347.
    14. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    15. Hess, Gregory D. & Iwata, Shigeru, 1997. "Asymmetric persistence in GDP? A deeper look at depth," Journal of Monetary Economics, Elsevier, vol. 40(3), pages 535-554, December.
    16. 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 63-84, Suppl. De.
    17. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    18. Kurt Brännäs & Henry Ohlsson, 1999. "Asymmetric Time Series and Temporal Aggregation," The Review of Economics and Statistics, MIT Press, vol. 81(2), pages 341-344, May.
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    More about this item

    Keywords

    Threshold moving average models; contemporaneous asymmetry; persistence of shocks; Efficient Method of Moments;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics

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