Forecasting with Neuro-Dynamic Programming
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917630654283776 |
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| author | Fernandes, Pedro Afonso |
| author_facet | Fernandes, Pedro Afonso |
| contents | Economic forecasting is concerned with the estimation of some variable like gross domestic product (GDP) in the next period given a set of variables that describes the current situation or state of the economy, including industrial production, retail trade turnover or economic confidence. Neuro-dynamic programming (NDP) provides tools to deal with forecasting and other sequential problems with such high-dimensional states spaces. Whereas conventional forecasting methods penalises the difference (or loss) between predicted and actual outcomes, NDP favours the difference between temporally successive predictions, following an interactive and trial-and-error approach. Past data provides a guidance to train the models, but in a different way from ordinary least squares (OLS) and other supervised learning methods, signalling the adjustment costs between sequential states. We found that it is possible to train a GDP forecasting model with data concerned with other countries that performs better than models trained with past data from the tested country (Portugal). In addition, we found that non-linear architectures to approximate the value function of a sequential problem, namely, neural networks can perform better than a simple linear architecture, lowering the out-of-sample mean absolute forecast error (MAE) by 32% from an OLS model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_03737 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Forecasting with Neuro-Dynamic Programming Fernandes, Pedro Afonso Econometrics Economic forecasting is concerned with the estimation of some variable like gross domestic product (GDP) in the next period given a set of variables that describes the current situation or state of the economy, including industrial production, retail trade turnover or economic confidence. Neuro-dynamic programming (NDP) provides tools to deal with forecasting and other sequential problems with such high-dimensional states spaces. Whereas conventional forecasting methods penalises the difference (or loss) between predicted and actual outcomes, NDP favours the difference between temporally successive predictions, following an interactive and trial-and-error approach. Past data provides a guidance to train the models, but in a different way from ordinary least squares (OLS) and other supervised learning methods, signalling the adjustment costs between sequential states. We found that it is possible to train a GDP forecasting model with data concerned with other countries that performs better than models trained with past data from the tested country (Portugal). In addition, we found that non-linear architectures to approximate the value function of a sequential problem, namely, neural networks can perform better than a simple linear architecture, lowering the out-of-sample mean absolute forecast error (MAE) by 32% from an OLS model. |
| title | Forecasting with Neuro-Dynamic Programming |
| topic | Econometrics |
| url | https://arxiv.org/abs/2404.03737 |