Location of Repository

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

By Michael P. Clements and Jeremy Smith

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

Topics: HB
Publisher: University of Warwick, Department of Economics
Year: 1997
OAI identifier: oai:wrap.warwick.ac.uk:1667

Suggested articles

Preview

Citations

  1. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. doi
  2. (1995). A nonlinear approach to U.S. doi
  3. (1995). A personal overview of non-linear time series analysis from a chaos perspective. doi
  4. (1992). A simple nonparametric test of predictive performance. doi
  5. (1996). A threshold model for the French franc/Deutschmark exchange rate. doi
  6. (1997). A.E.H.(1995). Modelling macroeconomic timeseries: Acomparative analysis of parametric and nonparametric methods. Discussion paper,
  7. (1991). An empirical assessment of non-linearities in models of exchange rate determination. doi
  8. Anon-linear stochastic rational expectations model ofexchange rates.
  9. (1994). Are economic forecasts valuable?. doi
  10. (1994). Asymmetries in the cyclical behaviour of UK labour markets. doi
  11. (1982). Autoregressive conditional heteroscedasticity, with estimates of the variance of United Kingdom inflation. doi
  12. (1995). Comparing predictive accuracy. doi
  13. D.F.(1993). Onthelimitations ofcomparing meansquared forecast errors.
  14. (1981). Data transformation and self-exciting threshold autoregression. doi
  15. (1993). Do recessions permanently affect output. doi
  16. (1994). Duration dependent transitions in a Markov model of U.S. GNP growth. doi
  17. (1990). Evaluating predictions of change. doi
  18. (1989). Forecasting exponential autoregressive models of order 1. doi
  19. (1997). Forecasting the U.S. unemployment rate. Mimeo, doi
  20. (1986). Generalised autoregressive conditional heteroskedasticity. doi
  21. (1996). Impulse response analysis in nonlinear multivariate models. doi
  22. (1996). Inference when a nuisance parameter is not identified under the null hypothesis. doi
  23. (1973). Information theory and an extension of the maximum likelihood principle. doi
  24. (1993). Modelling Nonlinear Economic Relationships.
  25. (1995). Non-linear Time Series. A Dynamical System Approach. doi
  26. (1993). Non-linearities in foreign exchange markets: a different perspective. doi
  27. (1993). Nonlinear dynamic structures. doi
  28. (1990). Nonparametric exchange rate prediction. doi
  29. (1986). On estimating thresholds in autoregressive models. doi
  30. R.C.(1981). Onmarkettimingandinvestment performance. IIstatistical procedures for evaluating forecast skills.
  31. (1994). Some advances in non-linear and adaptive modelling in time-series. doi
  32. (1992). Some recent developments in non-linear time series modelling, testing and forecasting. doi
  33. T.,andAnderson, H.M.(1992). Characterizing nonlinearities inbusinesscyclesusingsmooth transition autoregressive models.
  34. (1994). Testing for non-linear dependence in inter-war exchange rates. doi
  35. (1997). Testing the equality of prediction mean squared errors. doi
  36. (1993). Tests for parameter instability and structural change with unknown change point. doi
  37. (1997). The performance of alternative forecasting methods for SETAR models. doi
  38. (1980). Threshold autoregression, limit cycles and cyclical data. doi
  39. (1983). Threshold Models in Non-Linear Time Series Analysis: doi
  40. (1970). Time Series Analysis, Forecasting and Control. doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.