विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| लुम्सडेन-पैपेल यूनिट-रूट परीक्षण जिसमें दो संरचनात्मक ब्रेक हैं× | बाई-पेरॉन मल्टीपल स्ट्रक्चरल ब्रेक टेस्ट× | |
|---|---|---|
| क्षेत्र | अर्थमिति | अर्थमिति |
| परिवार | Hypothesis test | Hypothesis test |
| उद्भव वर्ष≠ | 1997 | 1998 |
| प्रवर्तक≠ | Robin Lumsdaine & David Papell | Jushan Bai & Pierre Perron |
| प्रकार≠ | Sequential two-break unit-root test | Sequential hypothesis test for multiple structural breaks |
| मौलिक स्रोत≠ | Lumsdaine, R. L., & Papell, D. H. (1997). Multiple trend breaks and the unit-root hypothesis. Review of Economics and Statistics, 79(2), 212–218. DOI ↗ | Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78. DOI ↗ |
| उपनाम | LP Test, Two-Break Unit-Root Test, Double Structural Break Unit-Root Test, Lumsdaine-Papell İki Kırılmalı Birim Kök Testi | Bai-Perron Multiple Break Test, Multiple Structural Change Test, Sequential Structural Break Test, Çoklu Yapısal Kırılma Testi |
| संबंधित≠ | 3 | 2 |
| सारांश≠ | The Lumsdaine-Papell test, introduced by Robin Lumsdaine and David Papell in 1997, extends the Zivot-Andrews single-break unit-root test to allow for two simultaneous structural breaks in the intercept and/or linear trend of a time series. It is widely used in macroeconomics and finance when data are suspected to have experienced two major regime shifts — such as policy changes, financial crises, or wars — and the researcher needs to determine whether the series is nonetheless integrated of order one. | 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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