Krahasoni metodat
Shqyrtoni metodat e zgjedhura krah për krah; rreshtat që ndryshojnë janë të theksuar.
| Testi Quandt-Andrews për Ndryshime Strukturore të Panjohura× | Testi Bai-Perron për Ndarje Strukturore Shumefishe× | |
|---|---|---|
| Fusha | Ekonometri | Ekonometri |
| Familja | Hypothesis test | Hypothesis test |
| Viti i origjinës≠ | 1993 | 1998 |
| Krijuesi≠ | Donald Andrews | Jushan Bai & Pierre Perron |
| Lloji≠ | Supremum test for structural change | Sequential hypothesis test for multiple structural breaks |
| Burimi themelues≠ | Andrews, D. W. K. (1993). Tests for parameter instability and structural change with unknown change point. Econometrica, 61(4), 821–856. DOI ↗ | Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78. DOI ↗ |
| Emërtime të tjera | sup-Wald Test, Andrews Breakpoint Test, Unknown Structural Break Test, Quandt Likelihood Ratio Test | Bai-Perron Multiple Break Test, Multiple Structural Change Test, Sequential Structural Break Test, Çoklu Yapısal Kırılma Testi |
| Të lidhura≠ | 3 | 2 |
| Përmbledhja≠ | The Quandt-Andrews test, formalized by Andrews (1993), detects structural breaks in regression parameters when the breakpoint date is unknown a priori. It sweeps all candidate break dates within a trimmed interior of the sample, computes a Wald (or LM/LR) statistic at each candidate, and reports the supremum of those statistics. Applied economists and time-series analysts use it to test whether coefficients remain stable across a full estimation window without needing to specify when the break occurred. | The Bai-Perron test, introduced by Jushan Bai and Pierre Perron in their landmark 1998 Econometrica paper, is a least-squares-based procedure for detecting, estimating, and testing the number of structural breaks in a linear regression model estimated on time-series data. Unlike single-break tests, it simultaneously identifies multiple change-points in a sample, providing economists and empirical researchers with a rigorous, data-driven way to locate parameter instability across time. |
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