قارن الطرق
راجع الطرق التي اخترتها جنبًا إلى جنب؛ الصفوف المختلفة مميَّزة.
| نموذج الانحدار الذاتي المتجهي المتين (Robust VAR)× | نموذج الانحدار الذاتي المتجه للبيانات المقطعية (Panel VAR)× | فار الكميات (Quantile VAR)× | |
|---|---|---|---|
| المجال | الاقتصاد القياسي | الاقتصاد القياسي | الاقتصاد القياسي |
| العائلة | Regression model | Regression model | Regression model |
| سنة النشأة≠ | 1980s–2000s | 1988 | 2006 |
| صاحب الطريقة≠ | Extensions by Lutkepohl and others building on Sims (1980) VAR framework | Holtz-Eakin, Newey & Rosen | Koenker and Xiao |
| النوع≠ | Multivariate time-series model with robust estimation | Panel vector autoregression | Distribution impulse response |
| المصدر التأسيسي≠ | Goncalves, S., & Kilian, L. (2004). Bootstrapping autoregressions with conditional heteroskedasticity of unknown form. Journal of Econometrics, 123(1), 89-120. DOI ↗ | Holtz-Eakin, D., Newey, W. & Rosen, H. S. (1988). Estimating Vector Autoregressions with Panel Data. Econometrica, 56(6), 1371-1395. DOI ↗ | Koenker, R., & Xiao, Z. (2006). Quantile autoregression. Journal of the American Statistical Association, 101(475), 980-990. DOI ↗ |
| الأسماء البديلة≠ | robust VAR, outlier-robust VAR, heavy-tailed VAR, RVAR | PVAR, panel vector autoregression, Panel VAR (PVAR) | Quantile-based impulse response |
| ذات صلة≠ | 5 | 3 | 3 |
| الملخص≠ | The Robust VAR model extends the classical Vector Autoregression framework by replacing ordinary least squares estimation with robust estimators — such as M-estimators or median-based methods — to reduce the influence of outliers, structural breaks, and heavy-tailed shocks common in financial and macroeconomic time series. | Panel VAR extends the vector autoregression model to panel data, modelling the dynamic interactions among several variables while controlling for cross-unit heterogeneity through fixed effects. It was introduced by Holtz-Eakin, Newey and Rosen in 1988 and produces impulse-response functions and variance decompositions at the panel level. | Quantile VAR estimates impulse responses of multivariate systems conditional on different quantiles of the distribution, revealing how shocks propagate heterogeneously across the conditional distribution. Introduced by Koenker and Xiao (2006) and applied to risk measurement by White et al. (2015), it reveals tail behavior and contagion effects invisible to mean-based VAR analysis. This is essential for risk management and understanding how crises propagate differently than normal times. |
| ScholarGateمجموعة البيانات ↗ |
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