arXiv 4 Apr 2024 · Econometrics
arXiv:2404.03737 · PDF · DOI · OpenAlex · Extracted main text
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.
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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 | D. P. Bertsekas and J. N. Tsitsiklis (1996) Neuro-Dynamic Programming | 1.000 | 8 | 3 | 100% |
| 2 | D. P. Bertsekas (2011) Approximate Dynamic Programming (online chapter) | 1.000 | 5 | 3 | 100% |
| 3 | L. Ljungqvist and T. J. Sargent (2018) Recursive Macroeconomic Theory | 0.928 | 4 | 3 | 100% |
| 4 | R. E. Bellman and S. E. Dreyfus (1962) Applied Dynamic Programming | 0.843 | 3 | 3 | 100% |
| 5 | R. S. Sutton and A. G. Barto (2018) Reinforcement Learning: An Introduction | 0.843 | 3 | 3 | 100% |
| 6 | C. Szepesvári (2010) Algorithms for Reinforcement Learning | 0.737 | 3 | 2 | 100% |
| 7 | R. E. Lucas and E. C. Prescott (1971) Investment under Uncertainty | 0.644 | 2 | 2 | 100% |
| 8 | S. Mannor, D. Simester, P. Sun, and J. N. Tsitsiklis (2007) Bias and Variance Approximation in Value Function Estimates | 0.644 | 2 | 2 | 100% |
| 9 | D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Gu… Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm | 0.644 | 2 | 2 | 100% |
| 10 | R. S. Sutton (1988) Learning to predict by the methods of temporal differences | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 30 scored citations.