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Small sample properties of forecasts from autoregressive models under structural breaks
Open accessAbstract
Autoregressive models are used routinely in forecasting and often lead to better performance than more complicated models. However, empirical evidence is also suggesting that the autoregressive representations of many macroeconomic and financial time series are likely to be subject to structural breaks. This paper develops a theoretical framework for the analysis of small-sample properties of forecasts from general autoregressive models under a structural break. Our approach is quite general and allows for unit roots both pre- and post-break. We derive finite-sample results for the mean squared forecast error of one-step-ahead forecasts, both conditionally and unconditionally and present numerical results for different types of break specifications. Implications of breaks for the determination of the optimal window size are also discussed.
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Cites
Computation and analysis of multiple structural change models
Bai, J., & Perron, P. (2003) · Journal of Applied Econometrics · doi:10.1002/jae.659 · Cited by 5,771
Tests for parameter instability and structural change with unknown change point.
Andrews, D. W. K. (1993) · Econometrica · doi:10.2307/2951764 · Cited by 4,334
Cited by
General diagnostic tests for cross-sectional dependence in panels
Pesaran, M. H. (2021) · Empirical Economics · doi:10.1007/s00181-020-01875-7 · Cited by 2,707
Self-citationSelection of estimation window in the presence of breaks
Pesaran, M. H., & Timmermann, A. (2007) · Journal of Econometrics · doi:10.1016/j.jeconom.2006.03.010 · Cited by 468
Self-citationKirikkaleli, D., Güngör, H., & Adebayo, T. S. (2022) · Business Strategy and the Environment · doi:10.1002/bse.2945 · Cited by 369
Raihan, A., Muhtasim, D. A., Farhana, S., Pavel, M. I., Faruk, O., Rahman, M., & Mahmood, A. (2022) · Energy and Climate Change · doi:10.1016/j.egycc.2022.100080 · Cited by 363
Geopolitical risk and oil volatility: A new insight
Liu, J., Ma, F., Tang, Y., & Zhang, Y. (2019) · Energy Economics · doi:10.1016/j.eneco.2019.104548 · Cited by 300
Metadata
- Authors
- Pesaran, M. H., & Timmermann, A.
- Venue
- Journal of Econometrics
- Publisher
- Elsevier BV
- Published
- 2005-11
- Volume
- 129
- Issue
- 1-2
- Pages
- 183-217
- ISSN
- 0304-4076 (ISSN-L)
- OpenAlex
- W2167685304
- Cited by
- 390
- References
- 2
- FWCI
- 9.43
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
