Abstract
A factor model with a break in its factor loadings is observationally equivalent to a model without changes in the loadings but with a change in the variance of its factors. This approach effectively transforms a high-dimensional structural change problem into a low-dimensional problem. This paper considers the likelihood ratio (LR) test for a variance change in the estimated factors. The LR test implicitly explores a special feature of the estimated factors: the pre-break and post-break variances can be a singular matrix under the alternative hypothesis, making the LR test diverging faster and thus more powerful than Wald-type tests. The better power property of the LR test is also confirmed by simulations. We also consider mean changes and multiple breaks. We apply this procedure to the factor modeling of the US employment and study the structural change problem using monthly industry-level data.
| Original language | English |
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| Publication status | Presented - 23 Sept 2022 |
| Event | 2022 NBER-NSF Time Series Conference - Boston University, Boston, United States Duration: 23 Sept 2022 → 24 Sept 2022 https://www.nber.org/sites/default/files/2022-09/CEME%20Time%20Series%202022.pdf |
Conference
| Conference | 2022 NBER-NSF Time Series Conference |
|---|---|
| Place | United States |
| City | Boston |
| Period | 23/09/22 → 24/09/22 |
| Internet address |
Bibliographical note
Information for this record is supplemented by the author(s) concerned.Research Keywords
- High dimensional factor models
- LR test
- structural breaks