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Combining Hodrick-Prescott Filtering with a Production Function Approach to Estimate Output Gap

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  • Marta Areosa
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    Abstract

    Many models were used to identify the factors affecting the demand for overnight funds by commercial banks. Theses models overcome overdispersion problems caused by excess of zeros found in the dataset. Generalized Linear Latent and Mixed Models (GLLAMM) constitute a class of models which allows the identification of both the direct and indirect effects of rediscount rate through the inclusion of random effects in the intercept (incorporating specific effects for each bank) and other coefficients (identifying individual behavior of each bank regarding the same stimuli). The results suggest the use of overnight funds is mainly influenced by the opening amount in bank reserves, by the net value of operations in the SELIC clearinghouse, by the rediscount rate, by the volatility of in bank reserves and by the reserve requirements on demand deposits.The proposed methodology combines two of the most important techniques to estimate output gap: the production function approach and the Hodrick-Prescott .ltering. Three main advantages can be derived from this method: (i) it adds some economic structure to a .filtering method, (ii) it can be easily adapted to incorporate new characteristics into the filter and (iii) it simultaneously produces estimates for potential output and its unobservable components.

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    File URL: http://www.bcb.gov.br/pec/wps/ingl/wps172.pdf
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    Bibliographic Info

    Paper provided by Central Bank of Brazil, Research Department in its series Working Papers Series with number 172.

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    Date of creation: Aug 2008
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    Handle: RePEc:bcb:wpaper:172

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    Web page: http://www.bcb.gov.br/?english

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    1. Clark, Peter K, 1987. "The Cyclical Component of U.S. Economic Activity," The Quarterly Journal of Economics, MIT Press, vol. 102(4), pages 797-814, November.
    2. Franco Modigliani & Lucas Papademos, 1978. "Optimal demand policies against stagflation," Review of World Economics (Weltwirtschaftliches Archiv), Springer, vol. 114(4), pages 736-782, December.
    3. King, Robert G. & Rebelo, Sergio T., 1993. "Low frequency filtering and real business cycles," Journal of Economic Dynamics and Control, Elsevier, vol. 17(1-2), pages 207-231.
    4. Harvey, A C, 1985. "Trends and Cycles in Macroeconomic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 3(3), pages 216-27, June.
    5. Laurence Boone & Michel Juillard & Doug Laxton & Papa N'Diaye, 2002. "How Well Do Alternative Time-Varying Parameter Models of the NAIRU Help Policymakers Forecast Unemployment and Inflation in the OECD Countries?," Computing in Economics and Finance 2002 359, Society for Computational Economics.
    6. Sweta Chaman Saxena & Valerie Cerra, 2000. "Alternative Methods of Estimating Potential Output and the Output Gap," IMF Working Papers 00/59, International Monetary Fund.
    7. Tommaso Proietti & Alberto Musso & Thomas Westermann, 2007. "Estimating potential output and the output gap for the euro area: a model-based production function approach," Empirical Economics, Springer, vol. 33(1), pages 85-113, July.
    8. Richard Clarida & Jordi Gali & Mark Gertler, 1999. "The Science of Monetary Policy: A New Keynesian Perspective," NBER Working Papers 7147, National Bureau of Economic Research, Inc.
    9. St-Amant, P. & van Norden, S., 1997. "Measurement of the Output Gap: A Discussion of Recent Research at the Bank of Canada," Technical Reports 79, Bank of Canada.
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