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The Role of Trends and Detrending in DSGE Models

  • Andrle, Michal

The paper discusses the role of stochastic trends in DSGE models and effects of stochastic detrending. We argue that explicit structural assumptions on trend behavior is convenient, namely for emerging countries. In emerging countries permanent shocks are an important part of business cycle dynamics. The reason is that permanent shocks spill over the whole frequency range, potentially, including business cycle frequencies. Applying high- or band-pass filter to obtain business cycle dynamics, however, does not eliminate the influence of permanent shocks on comovements of time series. The contribution of the paper is to provide a way how to calculate the role of permanent shocks on the detrended/ filtered business cycle population dynamics in a DSGE model laboratory using the frequency domain methods.

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File URL: http://mpra.ub.uni-muenchen.de/13289/1/MPRA_paper_13289.pdf
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 13289.

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Date of creation: 01 Aug 2008
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Handle: RePEc:pra:mprapa:13289
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  1. Mark Aguiar & Gita Gopinath, 2007. "Emerging Market Business Cycles: The Cycle Is the Trend," Journal of Political Economy, University of Chicago Press, vol. 115, pages 69-102.
  2. Peter N. Ireland & Scott Schuh, 2006. "Productivity and U.S. macroeconomic performance: interpreting the past and predicting the future with a two-sector real business cycle model," Working Papers 06-10, Federal Reserve Bank of Boston.
  3. Charles Nelson & Eric Zivot, 2000. "Why are Beveridge-Nelson and Unobserved-Component Decompositions of GDP so Different?," Econometric Society World Congress 2000 Contributed Papers 0692, Econometric Society.
  4. King, Robert G. & Plosser, Charles I. & Stock, James H. & Watson, Mark W., 1991. "Stochastic Trends and Economic Fluctuations," American Economic Review, American Economic Association, vol. 81(4), pages 819-40, September.
  5. Valle e Azevedo, João, 2007. "Interpretation of the Effects of Filtering Integrated Time Series," MPRA Paper 6574, University Library of Munich, Germany.
  6. Greenwood, Jeremy & Hercowitz, Zvi & Krusell, Per, 1997. "Long-Run Implications of Investment-Specific Technological Change," American Economic Review, American Economic Association, vol. 87(3), pages 342-62, June.
  7. Timothy Cogley & James M. Nason, 1991. "Effects of the Hodrick-Prescott filter on integrated time series," Proceedings, Federal Reserve Bank of San Francisco, issue Nov.
  8. Stephanie Schmitt-Grohe & Martin Uribe, 2002. "Closing Small Open Economy Models," NBER Working Papers 9270, National Bureau of Economic Research, Inc.
  9. Proietti, Tommaso, 2008. "Structural Time Series Models for Business Cycle Analysis," MPRA Paper 6854, University Library of Munich, Germany.
  10. Tommaso Proietti, 2006. "Trend-Cycle Decompositions with Correlated Components," Econometric Reviews, Taylor & Francis Journals, vol. 25(1), pages 61-84.
  11. Whelan, Karl, 2003. " A Two-Sector Approach to Modeling U.S. NIPA Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 35(4), pages 627-56, August.
  12. Rochelle Edge & Michael Kiley & Jean-Philippe Laforte, 2005. "An estimated DSGE model of the US economy with an application to natural rate measures," Proceedings, Board of Governors of the Federal Reserve System (U.S.).
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