Money Demand in Uruguay an artificial neural network approach

Elizabeth Bucacos ()
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Elizabeth Bucacos: Banco Central del Uruguay

No 1996003, Documentos de trabajo from Banco Central del Uruguay

Abstract: This paper examines money demand with error-correction models (ECM) and artificial neural network (ANN) methods in order to approximate more accurately the "true" underlying non-linear functional forms for the long-run equilibrium demand for money, and to estimate the learning and adjustment processes for money stocks in the short run. Non-linear techniques like Artificial Neural Networks are more appropriate to deal with those nonlinearities because, among other reasons, ANN can process information in multiple layers, each neuron has a nonlinear response to inputs, and they work in parallel. Unlike previous studies, in this paper inflation and exchange-rate uncertainty are explicitly incorporated in the short-run demand for money.

Keywords: Money Demand; Financial Innovation, Error-Correction Models, Artificial Neural Networks; Uncertainty; Seigniorage-Maximizing Inflation (search for similar items in EconPapers)
Pages: 51 pages
Date: 1996
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Persistent link: https://EconPapers.repec.org/RePEc:bku:doctra:1996003

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