Taştan, Hüseyin (2011): Simulation based estimation of threshold moving average models with contemporaneous shock asymmetry.
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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.
|Item Type:||MPRA Paper|
|Original Title:||Simulation based estimation of threshold moving average models with contemporaneous shock asymmetry|
|Keywords:||Threshold moving average models, contemporaneous asymmetry, persistence of shocks, Efficient Method of Moments|
|Subjects:||C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General
C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes
C - Mathematical and Quantitative Methods > C0 - General > C01 - Econometrics
|Depositing User:||Huseyin Tastan|
|Date Deposited:||25. Oct 2011 20:29|
|Last Modified:||28. Sep 2015 02:45|
Beaudry, P. & Koop, G. (1993), ‘Do recessions permanently change output?’, Journal of Monetary Economics 31, 149–163.
Brannas, K. & DeGooijer, J. (2004), ‘Asmmetries in conditional mean and variance: Modeling stock returns by asma(q)-asqgarch’, Journal of Forecasting 23, 155–171.
Brannas, K. & DeGooijer, J. G. (1994), ‘Autoregressive-asymmetric moving average models for business cycle data’, Journal of Forecasting 13, 529–544.
Brannas, K. & Ohlsson, H. (1999), ‘Asymmetric time series and temporal aggregation’, Revief of Economics and Statistics 81(2).
DeGooijer, J. (1998), ‘On threshold moving-average models’, Journal of Time Series Analysis 19(1), 1–18.
El-Babsiri, M. & Zakoian, J.-M. (2001), ‘Contemporaneous asymmetry in garch processes’, Journal of Econometrics 101(2), 257–294.
Elwood, S. (1998), ‘Is the persistence of shocks to output asymmetric?’, Journal of Monetary Economics 41, 411–426.
Fackler, P. & Tastan, H. (2009), A framework for indirect inference. Unpublished manuscript, available at: http://www.yildiz.edu.tr/ tastan/SGMM.pdf.
Gallant, A. & Tauchen, G. (1996), ‘Which moments to match’, Econometric Theory 12, 657–681.
Gonzalo, J. & Martinez, O. (2006), ‘Large shocks vs. small shocks. (or does size matter? may be so.)’, Journal of Econometrics 135, 311–347.
Gourieroux, C., Monfort, A. & Renault, E. (1993), ‘Indirect inference’, Journal of Applied Econometrics 8, S85–S118.
Guay, A. & Scaillet, O. (2003), ‘Indirect inference, nuisance parameter, and threshold moving average models’,
Journal of Business and Economic Statistics 21(1), 122–132. Hamilton, J. (1989), ‘A new approach to the economic analysis of nonstationary time series and the business cycle’, Econometrica 57(2), 357–84.
Hess, G. & Iwata, S. (1997), ‘Asymmetric persistence in gdp? a deeper look at depth’, Journal of Monetary Economics 40, 535–554.
Lee, B. & Ingram, B. F. (1991), ‘Simulation estimation of time series models’, Journal of Econometrics 47, 197–205.
McFadden, D. (1989), ‘A method of simulated moments for estimation of discrete response models without numerical integration’, Econometrica 57(5), 995–1026.
Michaelides, A. & Ng, S. (2000), ‘Estimating the rational expectations model of speculative storage: A monte carlo comparison of three simulation estimators’, Journal of Econometrics 96, 231–266.
Neftci, S. (1982), ‘Optimal prediction of cyclical downturns’, Journal of Economic Dynamics and Control 4, 225–41.
Newey, W. K. & West, K. D. (1987), ‘A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix’, Econometrica, 55(3), 703–708.
Sichel, D. (1993), ‘Business cycle asymmetry: A deeper look’, Economic Inquiry, 31(2), 224–36.
Smith, A. A. (1993), ‘Estimating nonlinear time series models using simulated vector autoregressions’, Journal of Applied Econometrics 8, S63–S84.
Tastan, H. & Yildirim, N. (2008), ‘Business cycle asymmetries in turkey: An application of markov-switching autoregressions’, International Economic Journal 22(3), 315–333.
Terasvirta, T. (1994), ‘Specification, estimation and and evaluation of smooth transition autoregressive models’, Journal of the American Statistical Association 89(425), 208–218.
Tsay, R. (1989), ‘Testing and modeling threshold autoregressive processes’, Journal of the American Statistical Association 84(405), 231–240.
Wecker, W. (1981), ‘Asymmetric time series’, Journal of the American Statistical Association 76(373), 16–21.
Yildirim, N. & Tastan, H. (2007), ‘Measuring business cycles in emerging market economies: Turkish case’, Yapi Kredi Economic Review 18(2), 27–50.
Zakoian, J.-M. (1994), ‘Threshold heteroskedastic models’, Journal of Economic Dynamics and Control 18(5), 931–955.