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| Model stanja prostora (Kalmanov filter)× | Bejzijevski autoregresioni vektor (BVAR)× | Markovljev model promene režima (MS-AR / MS-VAR)× | |
|---|---|---|---|
| Oblast | Ekonometrija | Ekonometrija | Ekonometrija |
| Porodica | Regression model | Regression model | Regression model |
| Godina nastanka≠ | 1990 | 1986 | 1989 |
| Tvorac≠ | Harvey; Durbin & Koopman (state space treatment); Kalman filter | Litterman (1986); Bańbura, Giannone & Reichlin (2010) | Hamilton (1989); Kim & Nelson (1999) |
| Tip≠ | State space time series model | Bayesian multivariate time-series model | Regime-switching time series model |
| Temeljni izvor≠ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ | 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 ↗ |
| Drugi nazivi≠ | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) | BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR) | regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR |
| Srodne≠ | 4 | 5 | 5 |
| Sažetak≠ | A state space model is a general time series framework that describes a series through unobserved (latent) state variables linked by a measurement equation and a transition equation, with the states estimated in real time by the Kalman filter. Developed in the state space tradition of Harvey (1990) and Durbin & Koopman (2012), it nests ARIMA and exponential smoothing as special cases. | 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. |
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