Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| GMM de Arellano-Bond Não Linear para Dados de Painel Dinâmicos× | GMM em Sistema (Arellano-Bover / Blundell-Bond)× | |
|---|---|---|
| Área | Econometria | Econometria |
| Família | Regression model | Regression model |
| Ano de origem≠ | 1991–2000s | 1998 |
| Autor original≠ | Arellano & Bond (1991), extended to nonlinear settings by Wooldridge and others | Arellano & Bover (1995); Blundell & Bond (1998) |
| Tipo≠ | Dynamic panel estimator | Dynamic panel data estimator |
| Fonte seminal≠ | Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297. DOI ↗ | 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 ↗ |
| Outros nomes | nonlinear AB-GMM, dynamic nonlinear panel GMM, nonlinear difference GMM, NL-GMM dynamic panel | Arellano-Bover estimator, Blundell-Bond estimator, dynamic panel GMM, Sistem GMM (Arellano-Bover / Blundell-Bond) |
| Relacionados≠ | 1 | 4 |
| Resumo≠ | Nonlinear Arellano-Bond GMM extends the classic Arellano-Bond difference-GMM framework to panel models where the conditional mean function is nonlinear in parameters or variables. It uses lagged levels of the dependent variable as instruments after first-differencing to remove individual fixed effects, yielding consistent estimates in short dynamic panels with nonlinear specifications such as count, duration, or multiplicative models. | System GMM is a generalized method of moments estimator for dynamic panel models that contain a lagged dependent variable. Introduced by Blundell and Bond (1998), building on Arellano and Bover, it augments the differenced equation of the earlier difference GMM (Arellano-Bond) with the equation in levels to deliver consistent estimates when N is large and T is small. |
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