Compara mètodes
Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| Model d'ARIMA (Autoregressive Integrated Moving Average)× | Vector Autoregressiu Bayesà (BVAR)× | Model de Sèries Temporals Estructurals (Model Estructural Bàsic)× | |
|---|---|---|---|
| Camp | Econometria | Econometria | Econometria |
| Família | Regression model | Regression model | Regression model |
| Any d'origen≠ | 2015 | 1986 | 1990 |
| Autor original≠ | Box & Jenkins (Box-Jenkins methodology) | Litterman (1986); Bańbura, Giannone & Reichlin (2010) | Andrew C. Harvey |
| Tipus≠ | Univariate time-series model | Bayesian multivariate time-series model | State-space (unobserved components) time series model |
| Font seminal≠ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 | Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38. DOI ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737 |
| Àlies≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR) | BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM) |
| Relacionats≠ | 5 | 5 | 4 |
| Resum≠ | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). | Bayesian VAR adds Minnesota or other prior distributions to a vector autoregressive model to control over-parameterisation. Introduced by Litterman (1986) and extended to high dimensions by Bańbura, Giannone and Reichlin (2010), it outperforms classical VAR on short series and high-dimensional macroeconomic forecasts. | The Structural Time Series Model, in its Basic Structural Model (BSM) form, is Andrew Harvey's state-space approach that decomposes a series into separate stochastic trend, seasonal, cyclical, and irregular components. Developed in Harvey's 1990 treatment, it is prized for interpretability and component decomposition where ARIMA only delivers a black-box fit. |
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