Abstract
We develop a Feasible Generalized Least Squares estimator of the date of a structural break in level and/or trend. The estimator is based on a consistent estimate of a T-dimensional inverse autocovariance matrix. A cubic polynomial transformation of break date estimates can be approximated by a nonstandard yet nuisance parameter free distribution asymptotically. The new limiting distribution captures the asymmetry and bimodality in finite samples and is applicable for inference with a single, known, set of critical values. We consider the confidence intervals/sets for break dates based on both Wald-type tests and by inverting multiple likelihood ratio (LR) tests. A simulation study shows that the proposed estimator increases the empirical concentration probability in a small neighborhood of the true break date and potentially reduces the mean squared errors. The LR-based confidence intervals/sets have good coverage while maintaining informative length even with highly persistent errors and small break sizes.
Original language | English |
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Pages (from-to) | 195-219 |
Number of pages | 25 |
Journal | Econometric Reviews |
Volume | 42 |
Issue number | 2 |
Early online date | 1 Feb 2023 |
DOIs | |
Publication status | Published - 7 Feb 2023 |
Keywords
- Level break
- trend break
- feasible generalized least squares
- inverted likelihood ratio test
- confidence set
- TIME-SERIES
- CHANGE-POINT
- HETEROSKEDASTICITY