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Issues In Adopting Dsge Models For Use In The Policy Process

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  • Martin Fukac
  • Adrian Pagan

Abstract

Our discussion is structured by three concerns - model design, matching the data and operational requirements. The paper begins with a general discussion of the structure of dynamic stochastic general equilibrium (DSGE) models where we investigate issues like (i) the type of restrictions being imposed by DSGE models upon system dynamics, (ii) the implication these models would have for 'location parameters', viz. growth rates, and (iii) whether these models can track the long-run movements in variables as well as matching dynamic adjustment. The paper further looks at the types of models that have been constructed in central banks for macro policy analysis. We distinguish four generations of these and detail how the emerging current generation, which are often referred to as DSGE models, differs from the previous generations. The last part of the paper is devoted to a variety of topics involving estimation and evaluation of DSGE models.
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Suggested Citation

  • Martin Fukac & Adrian Pagan, 2006. "Issues In Adopting Dsge Models For Use In The Policy Process," CAMA Working Papers 2006-10, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  • Handle: RePEc:een:camaaa:2006-10
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    File URL: https://cama.crawford.anu.edu.au/sites/default/files/publication/cama_crawford_anu_edu_au/2017-02/10_fukac_pagan_2006.pdf
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    References listed on IDEAS

    as
    1. Canova, Fabio, 1994. "Statistical Inference in Calibrated Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 9(S), pages 123-144, Suppl. De.
    2. Jordi Galí & Mark Gertler & J. David López-Salido, 2007. "Markups, Gaps, and the Welfare Costs of Business Fluctuations," The Review of Economics and Statistics, MIT Press, vol. 89(1), pages 44-59, November.
    3. James M. Nason & Gregor W. Smith, 2008. "Identifying the new Keynesian Phillips curve," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 525-551.
    4. Frank Schorfheide, 2000. "Loss function-based evaluation of DSGE models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 645-670.
    5. Forni, Mario & Giannone, Domenico & Lippi, Marco & Reichlin, Lucrezia, 2009. "Opening The Black Box: Structural Factor Models With Large Cross Sections," Econometric Theory, Cambridge University Press, vol. 25(05), pages 1319-1347, October.
    6. Cogley, Timothy & Nason, James M., 1993. "Impulse dynamics and propagation mechanisms in a real business cycle model," Economics Letters, Elsevier, vol. 43(1), pages 77-81.
    7. Kodde, D A & Palm, Franz C & Pfann, G A, 1990. "Asymptotic Least-Squares Estimation Efficiency Considerations and Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 5(3), pages 229-243, July-Sept.
    8. Forni, Mario & Lippi, Marco & Reichlin, Lucrezia, 2003. "Opening the Black Box: Structural Factor Models versus Structural VARs," CEPR Discussion Papers 4133, C.E.P.R. Discussion Papers.
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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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