Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Mfumo wa VAR wa Kibayesi (BVAR)× | Mfumo wa VAR wa Fourier× | |
|---|---|---|
| Nyanja | Ekonometriki | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1984 | 2010s |
| Mwanzilishi≠ | Doan, Litterman & Sims | Enders & Lee; extended by Nazlioglu and others to VAR systems |
| Aina | Multivariate time-series model | Multivariate time-series model |
| Chanzo asilia≠ | Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100. DOI ↗ | Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574-599. DOI ↗ |
| Majina mbadala | BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model | Fourier VAR, smooth structural break VAR, trigonometric VAR, Fourier-augmented VAR |
| Zinazohusiana≠ | 5 | 6 |
| Muhtasari≠ | The Bayesian Vector Autoregression (BVAR) model extends the classical VAR framework by incorporating prior beliefs about the model coefficients. Priors — most commonly the Minnesota prior — shrink VAR coefficients toward economically sensible values, dramatically reducing overfitting and improving out-of-sample forecast accuracy even when the number of variables is large. | The Fourier VAR model extends the standard Vector Autoregression by replacing fixed deterministic terms with Fourier trigonometric components, allowing the intercept (and optionally the trend) to shift gradually and smoothly over time. This eliminates the need to pre-specify the number, timing, or shape of structural breaks in a multivariate time-series system. |
| ScholarGateSeti ya data ↗ |
|
|