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Recursive Thick Modeling and the Choice of Monetary Policy in Mexico

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  • Arnulfo Rodríguez
  • Pedro N. Rodríguez
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    Abstract

    The choice of monetary policy is the most important concern of central banks. However, this choice is always confronted, inter alia, with two relevant aspects of economic policy: parameter instability and model uncertainty. This paper deals with both types of uncertainty using a very specific class of models in an optimal control framework. For optimal policy rates series featuring the first two moments similar to those of the actual nominal interest rates in Mexico, we show that recursive thick modeling gives a better approximation than recursive thin modeling. We complement previous work by evaluating the usefulness of both recursive thick modeling and recursive thin modeling in terms of direction-of-change forecastability.

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    File URL: http://www.banxico.org.mx/publicaciones-y-discursos/publicaciones/documentos-de-investigacion/banxico/%7BD481B972-1D49-3971-B79F-604EB29FFC56%7D.pdf
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    Bibliographic Info

    Paper provided by Banco de México in its series Working Papers with number 2007-04.

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    Date of creation: Mar 2007
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    Handle: RePEc:bdm:wpaper:2007-04

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    Web page: http://www.banxico.org.mx
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    Keywords: Macroeconomic policy; Model uncertainty; Optimal control; Monetary policy; Inflation targeting;

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    17. Rosario Dell'Aquila & Elvezio Ronchetti, 2004. "Stock and Bond Return Predictability : The Discrimination Power of Model Selection Criteria," Research Papers by the Institute of Economics and Econometrics, Geneva School of Economics and Management, University of Geneva 2004.05, Institut d'Economie et Econométrie, Université de Genève.
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