Abstract
We study a new class of nonlinear autoregressive models for vector time series, where the current vector depends on single-indexes defined on the past lags and the effects of different lags have an additive form. A sufficient condition is provided for stationarity of such models. We also study estimation of the proposed model using P-splines, hypothesis testing, asymptotics, selection of the order of the autoregression and of the smoothing parameters and nonlinear forecasting. We perform simulation experiments to evaluate our model in various settings. We illustrate our methodology on a climate data set and show that our model provides more accurate yearly forecasts of the El Niño phenomenon, the unusual warming of water in the Pacific Ocean.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 369-388 |
| Number of pages | 20 |
| Journal | Scandinavian Journal of Statistics |
| Volume | 36 |
| Issue number | 3 |
| DOIs | |
| State | Published - Sep 2009 |
| Externally published | Yes |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: Genton's research was supported in part by a National Science Foundation CMG grant ATM-0620624 and by Award no. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). The authors thank the editor, the associate editor and two referees for constructive suggestions that have improved the content and presentation of this article. The authors also thank Salil Mahajan and Ramalingam Saravanan from the Department of Atmospheric Sciences at Texas A&M University for providing the climate data set.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
Keywords
- Autoregressive
- Climate
- Multivariate
- Nonlinear
- Penalized spline
- Prediction
- Time series
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
