  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.
