방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 패널 자기회귀 (Panel AR) 모형× | 동적 패널 데이터 모형× | |
|---|---|---|
| 분야 | 계량경제학 | 계량경제학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1980s-2000s | 1991–1998 |
| 창시자≠ | Hsiao, C.; Arellano, M. | Arellano & Bond (1991); Blundell & Bond (1998) |
| 유형≠ | Autoregressive time-series model for panel data | Dynamic panel regression |
| 원전≠ | Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press. ISBN: 978-0521522717 | Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277–297. DOI ↗ |
| 별칭 | panel autoregressive model, PAR model, AR model for panel data, panel AR(p) | dynamic panel model, lagged dependent variable panel model, Arellano-Bond type dynamic panel, GMM dynamic panel |
| 관련 | 5 | 5 |
| 요약≠ | The Panel AR model extends the classical univariate autoregressive model to panel data, capturing how each unit's own past values predict its current value while controlling for unobserved individual heterogeneity through fixed or random effects. It is foundational for modelling dynamic persistence in micro or macro panel datasets. | The dynamic panel data model extends standard panel regression by including one or more lagged values of the outcome variable as regressors. Because past outcomes directly predict current outcomes, the model captures persistence and adjustment dynamics — but it also introduces a correlation between the lagged dependent variable and the individual fixed effect, rendering OLS and standard fixed-effects estimators inconsistent. GMM-based approaches developed by Arellano-Bond and Blundell-Bond resolve this problem. |
| ScholarGate데이터셋 ↗ |
|
|