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Taking a DSGE Model to the Data Meaningfully

Author

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  • Franchi, Massimo
  • Jusélius, Katarina
Abstract
All economists say that they want to take their model to the data. But with incomplete and highly imperfect data, doing so is difficult and requires carefully matching the assumptions of the model with the statistical properties of the data. The cointegrated VAR (CVAR) offers a way of doing so. In this paper we outline a method for translating the assumptions underlying a DSGE model into a set of testable assumptions on a cointegrated VAR model and illustrate the ideas with the RBC model in Ireland (2004). Accounting for unit roots (near unit roots) in the model is shown to provide a powerful robustification of the statistical and economic inference about persistent and less persistent movements in the data. We propose that all basic assumptions underlying the theory model should be formulated as a set of testable hypotheses on the long-run structure of a CVAR model, a so called ?theory consistent hypothetical scenario?. The advantage of such a scenario is that if forces us to formulate all testable implications of the basic hypotheses underlying a theory model. We demonstrate that most assumptions underlying the DSGE model and, hence, the RBC model are rejected when properly tested. Leaving the RBC model aside, we then report a structured CVAR analysis that summarizes the main features of the data in terms of long-run relations and common stochastic trends. We argue that structuring the data in this way offers a number of ?sophisticated? stylized facts that a theory model has to replicate in order to claim empirical relevance.

Suggested Citation

  • Franchi, Massimo & Jusélius, Katarina, 2007. "Taking a DSGE Model to the Data Meaningfully," Economics Discussion Papers 2007-6, Kiel Institute for the World Economy (IfW Kiel).
    • Handle: RePEc:zbw:ifwedp:5520
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    References listed on IDEAS

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    Cited by:

    1. Ivan O. KITOV & Oleg I. KITOV & Svetlana A. DOLINSKAYA, 2009. "Modelling Real Gdp Per Capita In The Usa:Cointegration Tests," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 4(1(7)_ Spr).
    2. Juselius, Katarina, 2014. "Testing for near I(2) trends when the signal-to-noise ratio is small," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 8, pages 1-30.
    3. Jaakko Kuorikoski & Aki Lehtinen, 2018. "Model selection in macroeconomics: DSGE and ad hocness," Journal of Economic Methodology, Taylor & Francis Journals, vol. 25(3), pages 252-264, July.
    4. Kapetanios, George & Millard, Stephen & Price, Simon & Petrova, Katerina, 2018. "Time varying cointegration and the UK Great Ratios," Essex Finance Centre Working Papers 23320, University of Essex, Essex Business School.
    5. Juselius, Katarina, 2015. "Haavelmo’S Probability Approach And The Cointegrated Var," Econometric Theory, Cambridge University Press, vol. 31(2), pages 213-232, April.
    6. Juselius, Katarina & Dimelis, Sophia, 2019. "The Greek crisis: A story of self-reinforcing feedback mechanisms," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 13, pages 1-22.
    7. Kevin D. Hoover & Soren Johansen & Katarina Juselius, 2008. "Allowing the Data to Speak Freely: The Macroeconometrics of the Cointegrated Vector Autoregression," American Economic Review, American Economic Association, vol. 98(2), pages 251-255, May.
    8. Katarina Juselius, 2021. "Searching for a Theory That Fits the Data: A Personal Research Odyssey," Econometrics, MDPI, vol. 9(1), pages 1-27, February.
    9. Fabio Bacchini & Cristina Brandimarte & Piero Crivelli & Roberta De Santis & Marco Fioramanti & Alessandro Girardi & Roberto Golinelli & Cecilia Jona-Lasinio & Massimo Mancini & Carmine Pappalardo & D, 2013. "Building the core of the Istat system of models for forecasting the Italian economy: MeMo-It," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 15(1), pages 17-45.
    10. Gimet, Céline & Lagoarde-Segot, Thomas & Reyes-Ortiz, Luis, 2019. "Financialization and the macroeconomy. Theory and empirical evidence," Economic Modelling, Elsevier, vol. 81(C), pages 89-110.
    11. J. Barkley Rosser Jr & Richard P.F. Holt & David Colander, 2010. "European Economics at a Crossroads," Books, Edward Elgar Publishing, number 13585.
    12. Paul De Grauwe, 2012. "Lectures on Behavioral Macroeconomics," Economics Books, Princeton University Press, edition 1, volume 1, number 9891.
    13. David Colander, 2010. "The Keynesian Method, Complexity, and the Training of Economists," Middlebury College Working Paper Series 1035, Middlebury College, Department of Economics.
    14. Giorgio Fagiolo & Andrea Roventini, 2012. "Macroeconomic Policy in DSGE and Agent-Based Models," Revue de l'OFCE, Presses de Sciences-Po, vol. 0(5), pages 67-116.
    15. Barbara Dluhosch, 2011. "European Economics at a Crossroads, by J. Barkley Rosser, Jr., Richard P. F. Holt, and David Colander," Journal of Regional Science, Wiley Blackwell, vol. 51(3), pages 629-631, August.
    16. Kapetanios, George & Millard, Stephen & Petrova, Katerina & Price, Simon, 2020. "Time-varying cointegration with an application to the UK Great Ratios," Economics Letters, Elsevier, vol. 193(C).
    17. Johansen, Søren & Juselius, Katarina & Frydman, Roman & Goldberg, Michael, 2010. "Testing hypotheses in an I(2) model with piecewise linear trends. An analysis of the persistent long swings in the Dmk/$ rate," Journal of Econometrics, Elsevier, vol. 158(1), pages 117-129, September.
    18. Dilip Nachane, 2017. "Dynamic Stochastic General Equilibrium (DSGE) Modelling :Theory And Practice," Working Papers id:11699, eSocialSciences.

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    More about this item

    Keywords

    DSGE; RBC; cointegrated VAR;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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