Forecasting with the damped trend model using the structural approach
| Autor: | Giacomo Sbrana, Andrea Silvestrini |
|---|---|
| Přispěvatelé: | Neoma Business School (NEOMA) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Economics and Econometrics
021103 operations research Series (mathematics) Computer science Covariance matrix 05 social sciences Monte Carlo method 0211 other engineering and technologies State vector 02 engineering and technology Kalman filter Management Science and Operations Research General Business Management and Accounting Industrial and Manufacturing Engineering Algebraic Riccati equation 0502 economics and business Applied mathematics [INFO]Computer Science [cs] Time series 050203 business & management Smoothing |
| Zdroj: | International Journal of Production Economics International Journal of Production Economics, Elsevier, 2020, 226, pp.107654-. ⟨10.1016/j.ijpe.2020.107654⟩ |
| ISSN: | 0925-5273 |
| DOI: | 10.1016/j.ijpe.2020.107654⟩ |
| Popis: | The damped trend model is a strong benchmark for time series forecasting. This model is usually estimated by adopting the innovations approach rather than the structural one, since the latter is more complex, requiring the use of the Kalman filter. In this paper, we introduce a simple method for estimating the damped trend using the structural approach. The proposed method relies on the analytical solution to the algebraic Riccati equation for the covariance matrix of the state vector’s estimation error. The solution fully simplifies both the Kalman filter recursions and the likelihood evaluation. The likelihood evaluation using the proposed method actually becomes very similar to that of the innovations approach. Moreover, the solution facilitates the smoothing of the state vector, which is crucial for signal extraction. A Monte Carlo simulation shows that both innovations and structural approaches have a similar out-of-sample forecasting performance. This is also confirmed empirically by working with the annual time series from the M3-competition database and with quarterly time series on total credit to the non-financial sector relative to GDP published by the Bank for International Settlements. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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