Abstract
Current study results on Machine Learning have been published. According to news reporting originating in Surrey, United Kingdom, by NewsRx journalists, research stated, "We use supervised machine learning to approximate the expectations typically contained in the optimality conditions of an economic model in the spirit of the parameterized expectations algorithm (PEA) with stochastic simulation. When the set of state variables is generated by a stochastic simulation, it is likely to suffer from multicollinearity." Financial support for this research came from Society of Computational Economics. The news reporters obtained a quote from the research from the University of Surrey, "We show that a neural network-based expectations algorithm can deal efficiently with multicollinearity by extending the optimal debt management problem studied by Faraglia, Marcet, Oikonomou, and Scott (2019) to four maturities. We find that the optimal policy prescribes an active role for the newly added medium-term maturities, enabling the planner to raise financial income without increasing its total borrowing in response to expenditure shocks."
NETL
NSTL
Wiley