Alessandro Casini, Pierre Perron
arXiv 1 Apr 2018 · Econometrics · publishedJournal of Econometrics (2020) · 6 citations (OpenAlex)
arXiv:1804.00232 · PDF · DOI · OpenAlex · Extracted main text
Building upon the continuous record asymptotic framework recently introduced by Casini and Perron (2018a) for inference in structural change models, we propose a Laplace-based (Quasi-Bayes) procedure for the construction of the estimate and confidence set for the date of a structural change. It is defined by an integration rather than an optimization-based method. A transformation of the least-squares criterion function is evaluated in order to derive a proper distribution, referred to as the Quasi-posterior. For a given choice of a loss function, the Laplace-type estimator is the minimizer of the expected risk with the expectation taken under the Quasi-posterior. Besides providing an alternative estimate that is more precise|lower mean absolute error (MAE) and lower root-mean squared error (RMSE)|than the usual least-squares one, the Quasi-posterior distribution can be used to construct asymptotically valid inference using the concept of Highest Density Region. The resulting Laplace-based inferential procedure is shown to have lower MAE and RMSE, and the confidence sets strike the best balance between empirical coverage rates and average lengths of the confidence sets relative to traditional long-span methods, whether the break size is small or large.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Elliott, G., Müller, U.K (2007) Confidence sets for the date of a single break in linear time series regressions | 1.000 | 11 | 3 | 100% |
| 2 | Bai, J (1997) Estimation of a change-point in multiple regression models | 1.000 | 8 | 4 | 100% |
| 3 | Casini, A., Perron, P (2020) a self | 1.000 | 5 | 3 | 100% |
| 4 | Bai, J., Perron, P (1998) Estimating and testing linear models with multiple structural changes self | 0.928 | 4 | 3 | 100% |
| 5 | Chang, S.Y., Perron, P (2018) A comparison of alternative methods to construct confidence intervals for the estimate of a break date in linear regression models self | 0.928 | 4 | 3 | 100% |
| 6 | Chernozhukov, V., Hong, H (2003) An MCMC approach to classical estimation | 0.874 | 7 | 2 | 100% |
| 7 | Casini, A., Perron, P (2019) Structural breaks in time series self | 0.843 | 3 | 3 | 100% |
| 8 | Ibragimov, A., Has'minski, R.Z (1981) Statistical estimation: asymptotic theory | 0.843 | 3 | 3 | 100% |
| 9 | Casini, A., Perron, P (2020) b self | 0.644 | 4 | 1 | 100% |
| 10 | Casini, A (2018) Tests for forecast instability and forecast failure under a continuous record asymptotic framework self | 0.644 | 2 | 2 | 100% |
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