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Efficient Simulation of DSGE Models with Inequality Constraints

  • Tom Holden

    ()

  • Michael Paetz

    ()

This paper presents a fast, simple and intuitive algorithm for simulation of linear dynamic stochastic general equilibrium models with inequality constraints. The algorithm handles both the computation of impulse responses, and stochastic simulation, and can deal with arbitrarily many bounded variables. To illustrate the usefulness and efficiency of this algorithm we provide two applications according to the zero lower bound (ZLB) on nominal interest rates. Our solution principle is much faster than comparable methods. We therefore expect this algorithm to be very helpful also for estimation procedures, and for a wide range of applications apart from monetary policy analysis.

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File URL: http://www.uni-hamburg.de/fachbereiche-einrichtungen/fb03/iwwt/makro/inequality.pdf
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Paper provided by Hamburg University, Department of Economics in its series Quantitative Macroeconomics Working Papers with number 21207b.

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Date of creation: Jul 2012
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Handle: RePEc:ham:qmwops:21207b
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  1. Braun, R. Anton & Körber, Lena Mareen & Waki, Yuichiro, 2012. "Some unpleasant properties of loglinearized solutions when the nominal rate is zero," FRB Atlanta Working Paper 2012-05, Federal Reserve Bank of Atlanta, revised 01 Nov 2015.
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  5. Michael Funke & Michael Paetz & Qianying Chen,, 2012. "Market and Non-Market Monetary Policy Tools in a Calibrated DSGE Model for Mainland China," Quantitative Macroeconomics Working Papers 21207, Hamburg University, Department of Economics.
  6. Tom Holden, 2010. "Products, patents and productivity persistence: A DSGE model of endogenous growth," Economics Series Working Papers 512, University of Oxford, Department of Economics.
  7. Clarida, Richard & Galí, Jordi & Gertler, Mark, 2002. "A Simple Framework for International Monetary Policy Analysis," CEPR Discussion Papers 3355, C.E.P.R. Discussion Papers.
  8. Jeff Fuhrer & Brian Madigan, 1994. "Monetary policy when interest rates are bounded at zero," Working Papers in Applied Economic Theory 94-06, Federal Reserve Bank of San Francisco.
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  12. Michael Funke & Michael Paetz, 2012. "A DSGE-based assessment of nonlinear loan-to-Value policies: Evidence from Hong Kong," Quantitative Macroeconomics Working Papers 21204, Hamburg University, Department of Economics.
  13. Jordi Galí & Mark Gertler, 1998. "Inflation dynamics: A structural econometric analysis," Economics Working Papers 341, Department of Economics and Business, Universitat Pompeu Fabra.
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  16. Adjemian, Stéphane & Bastani, Houtan & Karamé, Fréderic & Juillard, Michel & Maih, Junior & Mihoubi, Ferhat & Perendia, George & Pfeifer, Johannes & Ratto, Marco & Villemot, Sébastien, 2011. "Dynare: Reference Manual Version 4," Dynare Working Papers 1, CEPREMAP, revised Jul 2014.
  17. R. Anton Braun & Yuichiro Waki, 2005. "Monetary Policy during Japan's Lost Decade," CIRJE F-Series CIRJE-F-343, CIRJE, Faculty of Economics, University of Tokyo.
  18. Juillard Michel, 2011. "Local approximation of DSGE models around the risky steady state," wp.comunite 0087, Department of Communication, University of Teramo.
  19. Malin, Benjamin A. & Krueger, Dirk & Kubler, Felix, 2011. "Solving the multi-country real business cycle model using a Smolyak-collocation method," Journal of Economic Dynamics and Control, Elsevier, vol. 35(2), pages 229-239, February.
  20. Gauti B. Eggertsson & Michael Woodford, 2003. "The Zero Bound on Interest Rates and Optimal Monetary Policy," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 34(1), pages 139-235.
  21. Jung, Taehun & Teranishi, Yuki & Watanabe, Tsutomu, 2005. "Optimal Monetary Policy at the Zero-Interest-Rate Bound," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(5), pages 813-35, October.
  22. Ray C. Fair & John B. Taylor, 1980. "Solution and Maximum Likelihood Estimation of Dynamic Nonlinear Rational Expectations Models," Cowles Foundation Discussion Papers 564, Cowles Foundation for Research in Economics, Yale University.
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