Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Teste de Hausman para Quebra Estrutural× | Modelo de Efeitos Aleatórios com Ruptura Estrutural× | |
|---|---|---|
| Área | Econometria | Econometria |
| Família | Regression model | Regression model |
| Ano de origem≠ | 1978 (base); extended through 1990s–2000s | 1998–2000s |
| Autor original≠ | Jerry A. Hausman (base test, 1978); structural break extension developed in panel econometrics literature | Bai & Perron (break detection); Baltagi (panel RE framework) |
| Tipo≠ | Specification test | Panel regression with regime shifts |
| Fonte seminal≠ | Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251–1271. DOI ↗ | Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78. DOI ↗ |
| Outros nomes | Hausman test under structural change, structural change Hausman specification test, break-robust Hausman test, panel specification test with breaks | RE model with structural breaks, break-adjusted random effects, random effects break model, panel RE with regime shifts |
| Relacionados | 5 | 5 |
| Resumo≠ | The Structural Break Hausman Test extends the classical Hausman (1978) specification test to panel or time-series settings where the data-generating process shifts at one or more break points. By detecting structural breaks first and then running the Hausman comparison within each regime, researchers can reliably choose between fixed effects and random effects estimators even when the underlying relationship changes over time. | The structural break random effects model extends standard panel RE estimation by allowing one or more breakpoints at which slope coefficients or error variances shift across time. It combines structural change detection (e.g., Bai-Perron) with the GLS-based random effects estimator, producing regime-specific parameter estimates while retaining the efficiency gains of pooling individual-level variation as random draws from a common distribution. |
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