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A Monte Carlo study of the forecasting performance of empirical SETAR models

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Clements, Michael P. and Smith, Jeremy (Jeremy P.) (1997) A Monte Carlo study of the forecasting performance of empirical SETAR models. Working Paper. Coventry: University of Warwick, Department of Economics. (Warwick economic research papers.

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Abstract

In this paper we investigate the multi-period forecast performance of a number of empirical selfexciting threshold autoregressive (SETAR) models that have been proposed in the literature formodelling exchange rates and GNP, amongst other variables. We take each of the empirical SETAR models in turn as the DGP to ensure that the ‘non-linearity’ characterises the future, and compare the forecast performance of SETAR and linear autoregressive models on a number of quantitative and qualitative criteria. Our results indicate that non-linear models have an edge in certain states of nature but not in others, and that this can be highlighted by evaluating forecasts conditional upon the regime.

Item Type: Working or Discussion Paper (Working Paper)
Subjects: H Social Sciences > HB Economic Theory
Divisions: Faculty of Social Sciences > Economics
Library of Congress Subject Headings (LCSH): Economic forecasting, Threshold logic, Simulation games, Monte Carlo method
Series Name: Warwick economic research papers
Publisher: University of Warwick, Department of Economics
Place of Publication: Coventry
Date: 6 December 1997
Number: No.464
Number of Pages: 22
Status: Not Peer Reviewed
Access rights to Published version: Open Access
Funder: Economic and Social Research Council (Great Britain) (ESRC)
Grant number: L116251015 (ESRC)
References: Acemoglu, D., and Scott, A. (1994). Asymmetries in the cyclical behaviour of UK labour markets. Economic Journal, 104, 1303–1323. Akaike, A. (1973). Information theory and an extension of the maximum likelihood principle. In Petrov, B. N., and Saki, F. L. (eds.), Second International Symposium of Information Theory. Budapest. Al-Qassam, M. S., and Lane, J. A. (1989). Forecasting exponential autoregressive models of order 1. Journal of Time Series Analysis, 10, 95–113. Andrews, D. W. K. (1993). Tests for parameter instability and structural change with unknown change point. Econometrica, 61, 821–856. Beaudry, P., and Koop, G. (1993). Do recessions permanently affect output. Journal of Monetary Economics, 31, 149–163. Bollerslev, T. (1986). Generalised autoregressive conditional heteroskedasticity. Journal of Econometrics, 51, 307–327. Box, G. E. P., and Jenkins, G.M. (1970). Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day. Chan, K. S., and Tong, H. (1986). On estimating thresholds in autoregressive models. Journal of Time Series Analysis, 7, 179–190. Chappell, D., Padmore, J., Mistry, P., and Ellis, C. (1996). A threshold model for the French franc/Deutschmark exchange rate. Journal of Forecasting, 15, 155–164. Clements,M. P., and Hendry, D. F. (1993). On the limitations of comparing mean squared forecast errors. Journal of Forecasting, 12, 617–637. With discussion. Clements, M. P., and Smith, J. (1997). The performance of alternative forecasting methods for SETAR models. International Journal of Forecasting. Forthcoming. 15 Cleveland, W. S., Devlin, S. J., and Grosse, E. (1988). Regression by local fitting: Methods, properties and computational algorithms. Journal of Econometrics, 37, 87–114. De Gooijer, J. G., and De Bruin, P. (1997). On SETAR forecasting. Statistics and Probability Letters. Forthcoming. De Gooijer, J. G., and Kumar, K. (1992). Some recent developments in non-linear time series modelling, testing and forecasting. International Journal of Forecasting, 8, 135–156. Diebold, F. X., and Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business and Economic Statistics, 13, 253–263. Diebold, F. X., and Nason, J. A. (1990). Nonparametric exchange rate prediction. Journal of International Economics, 28, 315–332. Durland, J. M., and McCurdy, T. H. (1994). Duration dependent transitions in a Markov model of U.S. GNP growth. Journal of Business and Economic Statistics, 12, 279–288. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity, with estimates of the variance of United Kingdom inflation. Econometrica, 50, 987–1007. Gallant, A. R., Rossi, P. E., and Tauchen, G. (1993). Nonlinear dynamic structures. Econometrica, 61, 871–907. Ghaddar, D. K., and Tong, H. (1981). Data transformation and self-exciting threshold autoregression. Applied Statistics, 30, 238–248. Granger, C. W. J., and Ter¨asvirta, T. (1993). Modelling Nonlinear Economic Relationships. Oxford: Oxford University Press. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57, 357–384. Hansen, B. E. (1996). Inference when a nuisance parameter is not identified under the null hypothesis. Econometrica, 64, 413–430. Harvey, D., Leybourne, S., and Newbold, P. (1997). Testing the equality of prediction mean squared errors. International Journal of Forecasting. Forthcoming. Henriksson, R.D., andMerton, R.C. (1981). Onmarket timing and investment performance. II statistical procedures for evaluating forecast skills. Journal of Business, 54, 513–533. Hsieh, D. A. (1989). A non-linear stochastic rational expectations model of exchange rates. Unpublished manuscript, Fuqua School of Business, Duke University. Koop, G., Pesaran, M. H., and Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74, 119–147. Kr¨ager, H., and Kugler, P. (1993). Non-linearities in foreign exchange markets: a different perspective. Journal of International Money and Finance, 12, 195–208. Meese, R. A., and Rose, A. K. (1991). An empirical assessment of non-linearities in models of exchange rate determination. Review of Economic Studies, 58, 603–619. Montgomery, A. L., Zarnowitz, V., Tsay, R. S., and Tiao, G. C. (1997). Forecasting the U.S. unemployment rate. Mimeo, Wharton School, University of Pennsylvania, Philadelphia. Peel, D. A., and Speight, A. E. H. (1994). Testing for non-linear dependence in inter-war exchange rates. Weltwirtschaftliches Archiv, 130, 391–417. Peel, D.A., and Speight, A. E.H. (1995). Modelling macroeconomic time series: Acomparative analysis of parametric and nonparametric methods. Discussion paper, University of Wales, Aberystwyth. 16 Pesaran, M. H., and Potter, S. M. (1997). A floor and ceiling model of US Output. Journal of Economic Dynamics and Control, 21, 661–695. Pesaran, M., and Timmermann, A. (1992). A simple nonparametric test of predictive performance. Journal of Business and Econometic Statistics, 10, 461–465. Potter, S. (1995). A nonlinear approach to U.S. GNP. Journal of Applied Econometrics, 10, 109–125. Schnader, M. H., and Stekler, H. O. (1990). Evaluating predictions of change. Journal of Business, 63, 99–107. Stekler, H. O. (1994). Are economic forecasts valuable?. Journal of Forecasting, 13, 495–505. Ter¨asvirta, T., andAnderson, H. M. (1992). Characterizing nonlinearities in business cycles using smooth transition autoregressive models. Journal of Applied Econometrics, 7, 119–139. Tiao, G. C., and Tsay, R. S. (1994). Some advances in non-linear and adaptive modelling in time-series. Journal of Forecasting, 13, 109–131. Tong, H. (1978). On a threshold model. In Chen, C. H. (ed.), Pattern Recognition and Signal Processing, pp. 101–141. Amsterdam: Sijhoff and Noordoff. Tong, H. (1983). Threshold Models in Non-Linear Time Series Analysis: Springer-Verlag, New York. Tong, H. (1995a). Non-linear Time Series. A Dynamical System Approach. Oxford: Clarendon Press. First published 1990. Tong, H. (1995b). A personal overview of non-linear time series analysis from a chaos perspective. Scandinavian Journal of Statistics, 22, 399–445. Tong, H., and Lim, K. S. (1980). Threshold autoregression, limit cycles and cyclical data. Journal of The Royal Statistical Society, B 42, 245–292.
URI: http://wrap.warwick.ac.uk/id/eprint/1667

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