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| نموذج الانحدار الذاتي المتجه للبيانات المقطعية (Panel VAR)× | فار الكميات (Quantile VAR)× | نموذج الانحدار الذاتي المتجهي (VAR)× | |
|---|---|---|---|
| المجال | الاقتصاد القياسي | الاقتصاد القياسي | الاقتصاد القياسي |
| العائلة | Regression model | Regression model | Regression model |
| سنة النشأة≠ | 1988 | 2006 | 2005 |
| صاحب الطريقة≠ | Holtz-Eakin, Newey & Rosen | Koenker and Xiao | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| النوع≠ | Panel vector autoregression | Distribution impulse response | Multivariate time-series model |
| المصدر التأسيسي≠ | 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 ↗ | Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗ |
| الأسماء البديلة≠ | PVAR, panel vector autoregression, Panel VAR (PVAR) | Quantile-based impulse response | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon |
| ذات صلة≠ | 3 | 3 | 4 |
| الملخص≠ | 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. | Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005). |
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