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Model-Free Impulse Responses

  • Oscar Jorda

    (Department of Economics, University of California Davis)

This paper introduces methods for computing impulse response functions that do not require specification and estimation of the unknown dynamic multivariate system itself. The central idea behind these methods is to estimate flexible local projections at each period of interest rather than extrapolating into increasingly distant horizons from a given model, as it is usually done in vector autoregressions (VAR). The advantages of local projections are numerous: (1) they can be estimated by simple regression techniques with standard regression packages; (2) they are more robust to misspecification; (3) standard error calculation is direct; and (4) they easily accommodate experimentation with highly non-linear and flexible specifications that may be impractical in a multivariate context. Therefore, these methods are a natural alternative to estimating impulse responses from VARs. An application to a simple, closed-economy monetary model suggests that the output loss and inflation effects of an interest rate shock depend on the stage of the business cycle.

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File URL: http://wp.econ.ucdavis.edu/03-8.pdf
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Paper provided by University of California, Davis, Department of Economics in its series Working Papers with number 38.

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Length: 42
Date of creation: 26 Aug 2003
Date of revision:
Handle: RePEc:cda:wpaper:03-8
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  1. Barro, Robert J., 1978. "Unanticipated Money, Output, and the Price Level in the United States," Scholarly Articles 3450988, Harvard University Department of Economics.
  2. Jinill Kim & Sunghyun Kim & Ernst Schaumburg & Christopher A. Sims, 2003. "Calculating and Using Second Order Accurate Solutions of Discrete Time," Levine's Bibliography 666156000000000284, UCLA Department of Economics.
  3. William Poole, 1999. "Monetary policy rules?," Review, Federal Reserve Bank of St. Louis, issue Mar, pages 3-12.
  4. Jinill Kim & Sunghyun Kim & Ernst Schaumburg & Christopher A. Sims, 2003. "Calculating and using second order accurate solutions of discrete time dynamic equilibrium models," Finance and Economics Discussion Series 2003-61, Board of Governors of the Federal Reserve System (U.S.).
  5. Hamilton, James D., 1999. "A Parametric Approach to Flexible Nonlinear Inference," University of California at San Diego, Economics Working Paper Series qt68s8157x, Department of Economics, UC San Diego.
  6. Mccallum, Bennet T., 1988. "Robustness properties of a rule for monetary policy," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 29(1), pages 173-203, January.
  7. Thapar, Aditi, 2008. "Using private forecasts to estimate the effects of monetary policy," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 806-824, May.
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  16. Schmitt-Grohe, Stephanie & Uribe, Martin, 2004. "Solving dynamic general equilibrium models using a second-order approximation to the policy function," Journal of Economic Dynamics and Control, Elsevier, vol. 28(4), pages 755-775, January.
  17. Glenn Rudebusch & Lars E.O. Svensson, 1999. "Policy Rules for Inflation Targeting," NBER Chapters, in: Monetary Policy Rules, pages 203-262 National Bureau of Economic Research, Inc.
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  30. Kevin Salyer & Oscar Jorda, 2003. "The Response of Term Rates to Monetary Policy Uncertainty," Working Papers 16, University of California, Davis, Department of Economics.
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