Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Bajeziāņu vektorautoregresijas modelis (BVAR)× | Markov režīmu pārslēgšanās modelis (MS-AR / MS-VAR)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1986 | 1989 |
| Autors≠ | Litterman (1986); Bańbura, Giannone & Reichlin (2010) | Hamilton (1989); Kim & Nelson (1999) |
| Tips≠ | Bayesian multivariate time-series model | Regime-switching time series model |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi≠ | BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR) | regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. |
| ScholarGateDatu kopa ↗ |
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