Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель динамических панельных данных со структурными сдвигами× | Панельная модель коррекции ошибок (Panel VECM)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1991–1998 | 1987–1995 |
| Автор метода≠ | Bai & Perron (break detection); Arellano & Bond (dynamic panel GMM) | Engle & Granger (1987) for VECM; Holtz-Eakin, Newey & Rosen (1988) for panel VAR extension |
| Тип≠ | Dynamic panel model with regime change | Multivariate dynamic panel model |
| Основополагающий источник≠ | Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78. DOI ↗ | Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. DOI ↗ |
| Другие названия | dynamic panel with breaks, panel dynamic model structural change, DPDSB, panel dynamic structural break estimator | Panel VECM, panel vector error correction model, PVECM, panel cointegrating VAR |
| Связанные≠ | 6 | 5 |
| Сводка≠ | The structural break dynamic panel data model extends the standard dynamic panel framework by allowing regression coefficients or the autoregressive parameter to shift at one or more unknown break dates. It combines GMM-based dynamic panel estimation with formal structural change tests, enabling researchers to study how economic relationships evolve across distinct regimes while controlling for unobserved individual heterogeneity and endogeneity of the lagged dependent variable. | Panel VECM combines vector error correction modelling with panel data, simultaneously capturing the long-run cointegrating equilibrium among multiple I(1) variables and their short-run adjustment dynamics across multiple cross-sectional units. It is the standard framework when panel variables share at least one common stochastic trend. |
| ScholarGateНабор данных ↗ |
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