Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Bajeziāņu vektorautoregresijas modelis (BVAR)× | Kopneses un gludās pārejas VAR (TVAR / STVAR)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1986 | 1998 |
| Autors≠ | Litterman (1986); Bańbura, Giannone & Reichlin (2010) | Tsay (multivariate threshold modelling) |
| Tips≠ | Bayesian multivariate time-series model | Nonlinear multivariate 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 ↗ | Tsay, R. S. (1998). Testing and Modeling Multivariate Threshold Models. Journal of the American Statistical Association, 93(443), 1188-1202. DOI ↗ |
| Citi nosaukumi≠ | BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR) | TVAR, STVAR, regime-switching VAR, threshold 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. | Threshold VAR and Smooth-Transition VAR are nonlinear multivariate time-series models in which the coefficients of a vector autoregression switch between regimes according to a threshold variable. Building on Tsay's 1998 treatment of multivariate threshold models, they capture different dynamic structures across phases such as the business cycle, financial crises, or policy differences. |
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