Break date estimation for models with deterministic structural change
In this article, we consider estimating the timing of a break in level and/or trend when the order of integration and autocorrelation properties of the data are unknown. For stationary innovations, break point estimation is commonly performed by minimizing the sum of squared residuals across all can...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Published: |
Wiley
2014
|
| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/32664/ |
| _version_ | 1848794462335008768 |
|---|---|
| author | Harvey, David I. Leybourne, Stephen J. |
| author_facet | Harvey, David I. Leybourne, Stephen J. |
| author_sort | Harvey, David I. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | In this article, we consider estimating the timing of a break in level and/or trend when the order of integration and autocorrelation properties of the data are unknown. For stationary innovations, break point estimation is commonly performed by minimizing the sum of squared residuals across all candidate break points, using a regression of the levels of the series on the assumed deterministic components. For unit root processes, the obvious modification is to use a first differenced version of the regression, while a further alternative in a stationary autoregressive setting is to consider a GLS-type quasi-differenced regression. Given uncertainty over which of these approaches to adopt in practice, we develop a hybrid break fraction estimator that selects from the levels-based estimator, the first-difference-based estimator, and a range of quasi-difference-based estimators, according to which achieves the global minimum sum of squared residuals. We establish the asymptotic properties of the estimators considered, and compare their performance in practically relevant sample sizes using simulation. We find that the new hybrid estimator has desirable asymptotic properties and performs very well in finite samples, providing a reliable approach to break date estimation without requiring decisions to be made regarding the autocorrelation properties of the data. |
| first_indexed | 2025-11-14T19:16:34Z |
| format | Article |
| id | nottingham-32664 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:16:34Z |
| publishDate | 2014 |
| publisher | Wiley |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-326642020-05-04T20:13:12Z https://eprints.nottingham.ac.uk/32664/ Break date estimation for models with deterministic structural change Harvey, David I. Leybourne, Stephen J. In this article, we consider estimating the timing of a break in level and/or trend when the order of integration and autocorrelation properties of the data are unknown. For stationary innovations, break point estimation is commonly performed by minimizing the sum of squared residuals across all candidate break points, using a regression of the levels of the series on the assumed deterministic components. For unit root processes, the obvious modification is to use a first differenced version of the regression, while a further alternative in a stationary autoregressive setting is to consider a GLS-type quasi-differenced regression. Given uncertainty over which of these approaches to adopt in practice, we develop a hybrid break fraction estimator that selects from the levels-based estimator, the first-difference-based estimator, and a range of quasi-difference-based estimators, according to which achieves the global minimum sum of squared residuals. We establish the asymptotic properties of the estimators considered, and compare their performance in practically relevant sample sizes using simulation. We find that the new hybrid estimator has desirable asymptotic properties and performs very well in finite samples, providing a reliable approach to break date estimation without requiring decisions to be made regarding the autocorrelation properties of the data. Wiley 2014-10 Article PeerReviewed Harvey, David I. and Leybourne, Stephen J. (2014) Break date estimation for models with deterministic structural change. Oxford Bulletin of Economics and Statistics, 76 (5). pp. 623-642. ISSN 1468-0084 Break point estimation; Break in level; Break in trend; Local-to-zero breaks http://onlinelibrary.wiley.com/doi/10.1111/obes.12037/abstract doi:10.1111/obes.12037 doi:10.1111/obes.12037 |
| spellingShingle | Break point estimation; Break in level; Break in trend; Local-to-zero breaks Harvey, David I. Leybourne, Stephen J. Break date estimation for models with deterministic structural change |
| title | Break date estimation for models with deterministic structural change |
| title_full | Break date estimation for models with deterministic structural change |
| title_fullStr | Break date estimation for models with deterministic structural change |
| title_full_unstemmed | Break date estimation for models with deterministic structural change |
| title_short | Break date estimation for models with deterministic structural change |
| title_sort | break date estimation for models with deterministic structural change |
| topic | Break point estimation; Break in level; Break in trend; Local-to-zero breaks |
| url | https://eprints.nottingham.ac.uk/32664/ https://eprints.nottingham.ac.uk/32664/ https://eprints.nottingham.ac.uk/32664/ |