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| Bayesian Vector Autoregression (BVAR)× | Regresi Kuasa Dua Terkecil Biasa (OLS)× | VAR Ambang dan VAR Peralihan Licin (TVAR / STVAR)× | |
|---|---|---|---|
| Bidang | Ekonometrik | Ekonometrik | Ekonometrik |
| Keluarga | Regression model | Regression model | Regression model |
| Tahun asal≠ | 1986 | 2019 | 1998 |
| Pengasas≠ | Litterman (1986); Bańbura, Giannone & Reichlin (2010) | Wooldridge (textbook treatment); classical least squares | Tsay (multivariate threshold modelling) |
| Jenis≠ | Bayesian multivariate time-series model | Linear regression | Nonlinear multivariate time-series model |
| Sumber perintis≠ | Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Tsay, R. S. (1998). Testing and Modeling Multivariate Threshold Models. Journal of the American Statistical Association, 93(443), 1188-1202. DOI ↗ |
| Alias≠ | BVAR, Bayesian vector autoregression, Minnesota prior VAR, Bayesian VAR (BVAR) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | TVAR, STVAR, regime-switching VAR, threshold VAR |
| Berkaitan | 5 | 5 | 5 |
| Ringkasan≠ | 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). | 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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