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Overcoming the Forecasting Limitations of Forward-Looking Theory Based Models

Author

Listed:
  • Andrés González
  • Lavan Mahadeva
  • Diego Rodríguez
  • Luis Rojas

Abstract

Theory-consistent models have to be kept small to be tractable. If they are to forecast well, they have to condition on data that are unmodelled, noisy, patchy and about the future. Agents can also use these data to form their own expectations. In this paper we illustrate a scheme for jointly conditioning the forecasts and internal expectations of linearised forward-looking DSGE models on data through a Kalman Filter fixed-interval smoother. We also trial some diagnostics of this approach, in particular decompositions that reveal when a forecast conditioned on one set of variables implies estimates of other variables which are inconsistent with economic priors.

Suggested Citation

  • Andrés González & Lavan Mahadeva & Diego Rodríguez & Luis Rojas, 2011. "Overcoming the Forecasting Limitations of Forward-Looking Theory Based Models," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 29(66), pages 246-294, December.
    • Handle: RePEc:bdr:ensayo:v:29:y:2011:i:66:p:246-294
    DOI: 10.32468/Espe.6607
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    References listed on IDEAS

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    1. Coenen, Gunter & Levin, Andrew & Wieland, Volker, 2005. "Data uncertainty and the role of money as an information variable for monetary policy," European Economic Review, Elsevier, vol. 49(4), pages 975-1006, May.
    2. Junior Maih, 2010. "Conditional forecasts in DSGE models," Working Paper 2010/07, Norges Bank.
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    More about this item

    Keywords

    Conditional forecast; DSGE; Kalman filter;
    All these keywords.

    JEL classification:

    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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