Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.17708 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913404802826240 |
|---|---|
| author | Gourieroux, Christian Lee, Quinlan |
| author_facet | Gourieroux, Christian Lee, Quinlan |
| contents | We introduce a class of relative error decomposition measures that are well-suited for the analysis of shocks in nonlinear dynamic models. They include the Forecast Relative Error Decomposition (FRED), Forecast Error Kullback Decomposition (FEKD) and Forecast Error Laplace Decomposition (FELD). These measures are favourable over the traditional Forecast Error Variance Decomposition (FEVD) because they account for nonlinear dependence in both a serial and cross-sectional sense. This is illustrated by applications to dynamic models for qualitative data, count data, stochastic volatility and cyberrisk. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17708 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Forecast Relative Error Decomposition Gourieroux, Christian Lee, Quinlan Econometrics We introduce a class of relative error decomposition measures that are well-suited for the analysis of shocks in nonlinear dynamic models. They include the Forecast Relative Error Decomposition (FRED), Forecast Error Kullback Decomposition (FEKD) and Forecast Error Laplace Decomposition (FELD). These measures are favourable over the traditional Forecast Error Variance Decomposition (FEVD) because they account for nonlinear dependence in both a serial and cross-sectional sense. This is illustrated by applications to dynamic models for qualitative data, count data, stochastic volatility and cyberrisk. |
| title | Forecast Relative Error Decomposition |
| topic | Econometrics |
| url | https://arxiv.org/abs/2406.17708 |