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Policy Makers Priors and Inflation Density Forecasts

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Author Info

  • Marco Vega

    (LSE & Central Bank of Peru)

Abstract

This paper models an inflation forecast density framework that closely resembles actual policy makers behaviour regarding the determination of the modal point, the uncertainty and asymmetry in the inflation forecasts. The framework combines policy makers prior information about these parameters with a standard parametric density estimation technique using Bayesian theory. The combination crucially hinges on an information-theoretic utility function gains of the policy maker from performing the forecast exercise.

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File URL: http://128.118.178.162/eps/em/papers/0403/0403005.pdf
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Bibliographic Info

Paper provided by EconWPA in its series Econometrics with number 0403005.

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Length: 32 pages
Date of creation: 13 Mar 2004
Date of revision:
Handle: RePEc:wpa:wuwpem:0403005

Note: Type of Document - pdf; pages: 32
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Web page: http://128.118.178.162

Related research

Keywords: Monetary Policy; Inflation Targeting; Bayesian Methods;

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References

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  1. Wallis, Kenneth F., 2001. "Chi-squared tests of interval and density forecasts and the Bank of England's fan charts," Working Paper Series 0083, European Central Bank.
  2. Svensson, Lars E. O., 1997. "Inflation forecast targeting: Implementing and monitoring inflation targets," European Economic Review, Elsevier, vol. 41(6), pages 1111-1146, June.
  3. Svensson, Lars E. O., 1998. "Inflation targeting as a monetary policy rule," CFS Working Paper Series 1998/16, Center for Financial Studies (CFS).
  4. Kilian, Lutz & Manganelli, Simone, 2003. "The central bank as a risk manager: quantifying and forecasting inflation risks," Working Paper Series 0226, European Central Bank.
  5. Neil R. Ericsson, 2001. "Forecast uncertainty in economic modeling," International Finance Discussion Papers 697, Board of Governors of the Federal Reserve System (U.S.).
  6. Tarkka, Juha & Mayes, David, 1999. "The Value of Publishing Official Central Bank Forecasts," Research Discussion Papers 22/1999, Bank of Finland.
  7. Christopher A. Sims, 2002. "The Role of Models and Probabilities in the Monetary Policy Process," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 33(2), pages 1-62.
  8. repec:sae:niesru:v:167:y::i:1:p:106-112 is not listed on IDEAS
  9. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
  10. John Geweke, 1999. "Using simulation methods for bayesian econometric models: inference, development,and communication," Econometric Reviews, Taylor & Francis Journals, vol. 18(1), pages 1-73.
  11. Charles A.E. Goodhart, 2001. "Monetary transmission lags and the formulation of the policy decision on interest rates," Review, Federal Reserve Bank of St. Louis, issue Jul, pages 165-186.
  12. C. A. E. Goodhart, 2001. "The Inflation Forecast," National Institute Economic Review, National Institute of Economic and Social Research, vol. 175(1), pages 59-66, January.
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Citations

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Cited by:
  1. Amparo Maset-Llaudes & Ana Mª Fuertes-Eugenio & Pilar Pardo-Forcadell, 2011. "Integrated Urban Regeneration: An Empirical Study Of The Normative Framework In Spanish Regions," ERSA conference papers ersa11p1781, European Regional Science Association.
  2. Dong Jin Lee, 2009. "Testing Parameter Stability in Quantile Models: An Application to the U.S. Inflation Process," Working papers 2009-26, University of Connecticut, Department of Economics.
  3. Juan Manuel Julio, 2005. "Implementacion, Uso e Interpretación del FAN CHART," BORRADORES DE ECONOMIA 002815, BANCO DE LA REPÚBLICA.
  4. Mihaela Bratu, 2011. "The Assessement Of Uncertainty In Predictions Determined By The Variables Aggregation," Annales Universitatis Apulensis Series Oeconomica, Faculty of Sciences, "1 Decembrie 1918" University, Alba Iulia, vol. 2(13), pages 31.

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