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Conditional forecasts in DSGE models

  • Junior Maih

    ()

    (Norges Bank (Central Bank of Norway))

New-generation DSGE models are sometimes misspecified in dimensions that matter for their forecasting performance. The paper suggests one way to improve the forecasts of a DSGE model using a conditioning information that need not be accurate. The technique presented allows for agents to anticipate the information on the conditioning variables several periods ahead. It also allows the forecaster to apply a continuum of degrees of uncertainty around the mean of the conditioning information, making hard-conditional and unconditional forecasts special cases. An application to a small open-economy DSGE model shows that the benefits of conditioning depend crucially on the ability of the model to capture the correlation between the conditioning information and the variables of interest.

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File URL: http://www.norges-bank.no/en/Published/Papers/Working-Papers/2010/WP-201007/
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Paper provided by Norges Bank in its series Working Paper with number 2010/07.

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Length: 29 pages
Date of creation: 27 Apr 2010
Date of revision:
Handle: RePEc:bno:worpap:2010_07
Note: First version:
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  1. Smets, Frank & Wouters, Rafael, 2004. "Comparing Shocks and Frictions in US and Euro Area Business Cycles: A Bayesian DSGE Approach," CEPR Discussion Papers 4750, C.E.P.R. Discussion Papers.
  2. Thomas Lubik & Frank Schorfheide, 2003. "Do Central Banks Respond to Exchange Rate Movements? A Structural Investigation," Economics Working Paper Archive 505, The Johns Hopkins University,Department of Economics.
  3. King, Robert G & Watson, Mark W, 1998. "The Solution of Singular Linear Difference Systems under Rational Expectations," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 1015-26, November.
  4. John C. Robertson & Ellis W. Tallman & Charles H. Whiteman, 2002. "Forecasting using relative entropy," FRB Atlanta Working Paper 2002-22, Federal Reserve Bank of Atlanta.
  5. Daniel F. Waggoner & Tao Zha, 1998. "Conditional forecasts in dynamic multivariate models," FRB Atlanta Working Paper 98-22, Federal Reserve Bank of Atlanta.
  6. Eric M. Leeper & Todd B. Walker & Shu-Chun Susan Yang, 2008. "Fiscal Foresight: Analytics and Econometrics," NBER Working Papers 14028, National Bureau of Economic Research, Inc.
  7. Jaromír Beneš & Andrew Binning & Kirdan Lees, 2008. "Incorporating judgement with DSGE models," Reserve Bank of New Zealand Discussion Paper Series DP2008/10, Reserve Bank of New Zealand.
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