Money Demand and Seigniorage-Maximizing Inflation in Chile: Approximation, Learning, and Estimation with Neural Networks

Abstract

This paper examines money demand and the seigniorage-maximizing inflation rates in Chile, with linear error-correction models (ECM) and artificial neural network (ANN) methods. The purpose is to approximate more accurately the "true" underlying non-linear functional forms for the long-run equilibrium demand for money, to estimate the learning process in short-run monthly adjustment of money stocks, and to obtain better estimates of the seigniorage-maximizing rates of inflation. The ANN model shows that there is a high degree of non-linearity in the long-run demand for money in Chile, and that the seigniorage-maximizing inflation rates are much lower than the predictions of previous studies.