Time series forecasting under structural breaks
DOI:
https://doi.org/10.22394/1993-7601-2024-76-120-139Keywords:
временные ряды, структурные сдвиги, прогнозирование, оптимальный прогноз, взвешенная оценкаAbstract
In this paper, we overview the forecasting methods in the presence of structural breaks. Methods for selecting a forecast window that includes the break date, weighted average methods of pre- and post-break estimators, and averaging-based methods are discussed. The considered methods are compared in terms of predictive power using Russian macroeconomic time series. The results demonstrate the superiority of forecasts that take into account the presence of break.Agencies
The article was prepared as part of the research work carried out under the state assignment of the Russian Presidential Academy of National Economy and Public Administration (RANEPA). The author expresses gratitude to Andrey Polbin for productive discussions and valuable comments, which contributed to a significant improvement of the article.Downloads
References
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Bai J., Perron P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66 (1), 47–78. DOI: 10.2307/2998540.
Boot T., Pick A. (2020). Does modeling a structural break improve forecast accuracy? Journal of Econometrics, 215 (1), 35–59. DOI: 10.1016/j.jeconom.2019.07.007.
Cai Z. (2007). Trending time-varying coefficient time series models with serially correlated errors. Journal of Econometrics, 136, 163–188. DOI: 10.1016/j.jeconom.2005.08.004.
Chudik A., Pesaran M. H., Sharifvaghefi M. (2023). Variable selection in high dimensional linear regressions with parameter instability. arXiv: 2312.15494. https://arxiv.org/abs/2312.15494.
Eo Y., Morley J. (2015). Likelihood-ratio-based confidence sets for the timing of structural breaks. Quantitative Economics, 6 (2), 463–497. DOI: 10.3982/QE186.
Hirano K., Wright J. H. (2022). Analyzing cross-validation for forecasting with structural instability. Journal of Econometrics, 226 (1), 139–154. DOI: 10.1016/j.jeconom.2020.10.009.
Inoue A., Jin L., Rossi B. (2017). Rolling window selection for out-of-sample forecasting with time-varying parameters. Journal of Econometrics, 196 (1), 55–67. DOI: 10.1016/j.jeconom.2016.03.006.
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Pesaran M. H., Pick A. (2011). Forecast combination across estimation windows. Journal of Business and Economic Statistics, 29 (2), 307–318. DOI: 10.1198/jbes.2010.09018.
Pesaran M. H., Timmermann A. (2007). Selection of estimation window in the presence of breaks. Journal of Econometrics, 137 (1), 134–161. DOI: 10.1016/j.jeconom.2006.03.010.
Pesaran M. H., Pick A., Pranovich M. (2013). Optimal forecasts in the presence of structural breaks. Journal of Econometrics, 177 (2), 134–152. DOI: 10.1016/j.jeconom.2013.04.002.
