Functional Regression with Nonstationarity and Error Contamination: Application to the Economic Impact of Climate Change
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866912620057985024 |
|---|---|
| author | Nam, Kyungsik Seo, Won-Ki |
| author_facet | Nam, Kyungsik Seo, Won-Ki |
| contents | This paper studies a regression model with functional dependent and explanatory variables, both of which exhibit nonstationary dynamics. The model assumes that the nonstationary stochastic trends of the dependent variable are explained by those of the explanatory variables, and hence that there exists a stable long-run relationship between the two variables despite their nonstationary behavior. We also assume that the functional observations may be error-contaminated. We develop novel autocovariance-based estimation and inference methods for this model. The methodology is broadly applicable to economic and statistical functional time series with nonstationary dynamics. To illustrate our methodology and its usefulness, we apply it to evaluating the global economic impact of climate change, an issue of intrinsic importance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08591 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Functional Regression with Nonstationarity and Error Contamination: Application to the Economic Impact of Climate Change Nam, Kyungsik Seo, Won-Ki Methodology Econometrics 62M10, 62R10 This paper studies a regression model with functional dependent and explanatory variables, both of which exhibit nonstationary dynamics. The model assumes that the nonstationary stochastic trends of the dependent variable are explained by those of the explanatory variables, and hence that there exists a stable long-run relationship between the two variables despite their nonstationary behavior. We also assume that the functional observations may be error-contaminated. We develop novel autocovariance-based estimation and inference methods for this model. The methodology is broadly applicable to economic and statistical functional time series with nonstationary dynamics. To illustrate our methodology and its usefulness, we apply it to evaluating the global economic impact of climate change, an issue of intrinsic importance. |
| title | Functional Regression with Nonstationarity and Error Contamination: Application to the Economic Impact of Climate Change |
| topic | Methodology Econometrics 62M10, 62R10 |
| url | https://arxiv.org/abs/2509.08591 |