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Recursive Thick Modeling and the Choice of Monetary Policy in Mexico

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  • Arnulfo Rodriguez

    ()
    (Economic Studies Division Bank of Mexico)

  • Pedro N. Rodriguez

    (Universidad Complutense de Madrid)

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    Abstract

    By following the spirit in Favero and Milani (2005), we use recursive thick modeling to take into account model uncertainty for the choice of optimal monetary policy. We consider an open economy model and generate multiple models for only the aggregate demand and aggregate supply. Models are constructed by matching the rankings of aggregate demand and aggregate supply and adding other specifications for the rest of the variables. The main results show that recursive thick modeling with equal and different weights approximates the recent historical behavior of nominal interest rates in Mexico better than recursive thin modeling

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    File URL: http://repec.org/sce2006/up.21601.1137547841.pdf
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    Bibliographic Info

    Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 30.

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    Date of creation: 04 Jul 2006
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    Handle: RePEc:sce:scecfa:30

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    Related research

    Keywords: model uncertainty; optimal control; out-of-bag; thin modeling and thick modeling;

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    1. Söderström, Ulf, 1999. "Monetary policy with uncertain parameters," Working Paper Series in Economics and Finance 308, Stockholm School of Economics.
    2. Favero, Carlo A & Milani, Fabio, 2005. "Parameter Instability, Model Uncertainty and the Choice of Monetary Policy," CEPR Discussion Papers 4909, C.E.P.R. Discussion Papers.
    3. Giordani, Paolo & Soderlind, Paul, 2004. "Solution of macromodels with Hansen-Sargent robust policies: some extensions," Journal of Economic Dynamics and Control, Elsevier, vol. 28(12), pages 2367-2397, December.
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    7. Watt, P A, 1979. "Tests of Equality between Sets of Coefficients in Two Linear Regressions When Disturbance Variances Are Unequal: Some Small Sample Properties," The Manchester School of Economic & Social Studies, University of Manchester, vol. 47(4), pages 391-96, December.
    8. Laurence M. Ball, 1999. "Policy Rules for Open Economies," NBER Chapters, in: Monetary Policy Rules, pages 127-156 National Bureau of Economic Research, Inc.
    9. Fabio Milani, 2004. "Monetary Policy with a Wider Information Set: a Bayesian Model Averaging Approach," Macroeconomics 0401004, EconWPA.
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    12. Granger, Clive W. J. & Jeon, Yongil, 2004. "Thick modeling," Economic Modelling, Elsevier, vol. 21(2), pages 323-343, March.
    13. Pierre-Olivier Gourinchas & Aaron Tornell, 1996. "Exchange Rate Dynamics and Learning," NBER Working Papers 5530, National Bureau of Economic Research, Inc.
    14. Martin Eichenbaum & Charles Evans, 1992. "Some empirical evidence on the effects of monetary policy shocks on exchange rates," Working Paper Series, Macroeconomic Issues 92-32, Federal Reserve Bank of Chicago.
    15. Bossaerts, Peter & Hillion, Pierre, 1999. "Implementing Statistical Criteria to Select Return Forecasting Models: What Do We Learn?," Review of Financial Studies, Society for Financial Studies, vol. 12(2), pages 405-28.
    16. 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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