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

Authors

Keywords:

money demand, seigniorage, inflation, artificial neural networks, error-correction models, Chile

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.

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References

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Published

1998-10-31

How to Cite

McNelis, P. D. (1998). Money Demand and Seigniorage-Maximizing Inflation in Chile: Approximation, Learning, and Estimation with Neural Networks. Economic Analysis Review, 13(2), 3–24. Retrieved from https://www.rae-ear.org/index.php/rae/article/view/129

Issue

Vol. 13 No. 2 (1998)

Section

Articles

License

Copyright (c) 1998 Paul D. McNelis

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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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