Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Bayesiläinen vektoritodennäköisyysmalli (BVAR)× | Markovin tilaa vaihtava malli (MS-AR / MS-VAR)× | Rakenteellinen aikasarjamalli (Perusrakennemalli)× | |
|---|---|---|---|
| Tieteenala | Ekonometria | Ekonometria | Ekonometria |
| Menetelmäperhe | Regression model | Regression model | Regression model |
| Syntyvuosi≠ | 1986 | 1989 | 1990 |
| Kehittäjä≠ | Litterman (1986); Bańbura, Giannone & Reichlin (2010) | Hamilton (1989); Kim & Nelson (1999) | Andrew C. Harvey |
| Tyyppi≠ | Bayesian multivariate time-series model | Regime-switching time series model | State-space (unobserved components) time series model |
| Alkuperäislähde≠ | Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38. DOI ↗ | Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384. DOI ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737 |
| Rinnakkaisnimet≠ | BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR) | regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR | BSM, basic structural model, unobserved components model, Yapısal Zaman Serisi Modeli (BSM) |
| Liittyvät≠ | 5 | 5 | 4 |
| Tiivistelmä≠ | 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 Markov regime-switching model lets the parameters of a time series change probabilistically across hidden regimes governed by a Markov chain. Introduced by Hamilton (1989) and developed further by Kim and Nelson (1999), it automatically detects business-cycle phases such as expansions and contractions. | 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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