Forecast Performance of Threshold Autoregressive Models - A Monte Carlo Study
AbstractThreshold Autoregressive Models (TAR) along with other nonlinear time series models have attracted much attention in recent years in time series analysis. TAR models have been applied to a variety of time series. It has been reported that they have a good in sample fit but like many other non-linear time series models cannot improve out of sample forecast performance. Within a controlled simulation framework, we study the forecast performance under two types of non-linearity: shift in the mean and shift in the volatility of the process. We illustrate that estimation of the lag parameter and threshold value are crucial for forecast performance. Monte Carlo results show that TAR model performs much better than a Random Walk (RW) model; however, it provides no significant improvement over a linear Autoregressive (AR) model. Conclusions on the relative forecast performance of TAR models based on a single data set can be quite different than long run (Monte Carlo) results.
Download InfoTo our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
Bibliographic InfoPaper provided by University of Western Ontario, Department of Economics in its series UWO Department of Economics Working Papers with number 9910.
Date of creation: Nov 1998
Date of revision:
Contact details of provider:
Postal: Department of Economics, Reference Centre, Social Science Centre, University of Western Ontario, London, Ontario, Canada N6A 5C2
Phone: 519-661-2111 Ext.85244
Web page: http://economics.uwo.ca/research/research_papers/department_working_papers.html
Nonlinear Time Series; Threshold Model; Forecast Performance; Monte Carlo;
This paper has been announced in the following NEP Reports:
- NEP-ALL-1999-11-28 (All new papers)
- NEP-ECM-1999-11-28 (Econometrics)
- NEP-ETS-1999-11-28 (Econometric Time Series)
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ().
If references are entirely missing, you can add them using this form.