arXiv 19 Sep 2023 · Statistics — Methodology
arXiv:2309.10481 · PDF · DOI · OpenAlex · Extracted main text
A nonlinear regression framework is proposed for time series and panel data for the situation where certain explanatory variables are available at a higher temporal resolution than the dependent variable. The main idea is to use the moments of the empirical distribution of these variables to construct regressors with the correct resolution. As the moments are likely to display nonlinear marginal and interaction effects, an artificial neural network regression function is proposed. The corresponding model operates within the traditional stochastic nonlinear least squares framework. In particular, a numerical Hessian is employed to calculate confidence intervals. The practical usefulness is demonstrated by analyzing the influence of daily temperatures in 260 European NUTS2 regions on the yearly growth of gross value added in these regions in the time period 2000 to 2021. In the particular example, the model allows for an appropriate assessment of regional economic impacts resulting from (future) changes in the regional temperature distribution (mean AND variance).
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Burke M., Hsiang S.M., Miguel E (2015) Global nonlinear effect of temperature on economic production | 1.000 | 8 | 5 | 100% |
| 2 | Kalkuhl, M., Wenz, L (2020) The impact of climate conditions on economic production | 1.000 | 6 | 5 | 100% |
| 3 | Dell M., Jones B.F., Olken B.A (2012) Temperature Shocks and Economic Growth: Evidence from the Last Half Century | 1.000 | 5 | 3 | 100% |
| 4 | Holtermann L., Rische M.C (2020) The Subnational Effects of Temperature on Economic Production: A Disaggregated Analysis in European Regions | 0.928 | 4 | 4 | 100% |
| 5 | Baltagi B.H (2021) Econometric Analysis of Panel Data | 0.511 | 2 | 1 | 100% |
| 6 | Jahn M (2020) Artificial neural network regression models: Predicting economic growth | 0.511 | 2 | 1 | 100% |
| 7 | Cornes R., van der Schrier G., van den Besselaar E.J.M., Jones P.D (2018) An Ensemble Version of the E-OBS Temperature and Precipitation Datasets | 0.405 | 1 | 1 | 100% |
| 8 | Feng L., Zhang J (2014) Application of artificial neural networks in tendency forecasting of economic growth | 0.405 | 1 | 1 | 100% |
| 9 | Hsiang, S (2016) Climate econometrics | 0.405 | 1 | 1 | 100% |
| 10 | Kock, A.B., Teräsvirta, T (2014) Forecasting performances of three automated modelling techniques during the economic crisis 2007-2009 | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 11 scored citations.