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Making a match: combining theory and evidence in policy-oriented macroeconomic modelling

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  • Alasdair Scott
  • George Kapetanios
  • Adrian Pagan

Abstract

A persistent question arising in the development of models for the analysis of macroeconomic policy has been the relative role of economic theory and evidence (data) in their construction. This paper looks at some strategies for transforming a Conceptual Model to become a Data-Adjusted Model, and how to adjust further to an Operational Model for policy analysis. Using a typical dynamic GE small open economy calibrated to UK data, we examine how some simple but formal econometric tests can be applied to test the match of the CM to the data. We also use the CM as a laboratory to assess model-building strategies. Our example suggests that, since one will never be sure that the choice of variables is appropriate, it is better to start with a CM and work towards making it match the data than attempting the converse

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  • Alasdair Scott & George Kapetanios & Adrian Pagan, 2005. "Making a match: combining theory and evidence in policy-oriented macroeconomic modelling," Computing in Economics and Finance 2005 462, Society for Computational Economics.
    • Handle: RePEc:sce:scecf5:462
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    6. Mala Raghavan & George Athanasopoulos & Param Silvapulle, 2016. "Canadian monetary policy analysis using a structural VARMA model," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 49(1), pages 347-373, February.
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    9. Raghavan, Mala & Athanasopoulos, George, 2019. "Analysis of shock transmissions to a small open emerging economy using a SVARMA model," Economic Modelling, Elsevier, vol. 77(C), pages 187-203.
    10. Adrian Pagan & Tim Robinson, 2019. "Implications of Partial Information for Applied Macroeconomic Modelling," Melbourne Institute Working Paper Series wp2019n12, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    11. Martin Fukac & Adrian Pagan, 2009. "Structural Macro-Econometric Modelling in a Policy Environment," NCER Working Paper Series 50, National Centre for Econometric Research.
    12. Jeanfils, Philippe & Burggraeve, Koen, 2008. ""NONAME": A new quarterly model for Belgium," Economic Modelling, Elsevier, vol. 25(1), pages 118-127, January.
    13. Poskitt, D.S., 2016. "Vector autoregressive moving average identification for macroeconomic modeling: A new methodology," Journal of Econometrics, Elsevier, vol. 192(2), pages 468-484.
    14. Eric M. Leeper & Todd B. Walker & Shu-Chun Susan Yang, 2008. "Fiscal Foresight: Analytics and Econometrics," CAEPR Working Papers 2008-013, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
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    16. Cheuk Yin Ho, 2007. "Illegal migration and economic growth: simulation analysis in an international context," Economics Bulletin, AccessEcon, vol. 6(41), pages 1-13.
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    19. 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.
    20. Jarkko Jääskelä & David Jennings, 2010. "Monetary Policy and the Exchange Rate: Evaluation of VAR Models," RBA Research Discussion Papers rdp2010-07, Reserve Bank of Australia.
    21. Franchi, Massimo & Vidotto, Anna, 2013. "A check for finite order VAR representations of DSGE models," Economics Letters, Elsevier, vol. 120(1), pages 100-103.
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    23. Wang, Lipeng & Zhang, Mengyu & Verousis, Thanos, 2021. "The road to economic recovery: Pandemics and innovation," International Review of Financial Analysis, Elsevier, vol. 75(C).

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    Keywords

    economic model;

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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