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Minimum Distance Estimation and Testing of DSGE Models from Structural VARs

Author

Listed:
  • Patrick Fève
  • Julien Matheron
  • Jean-Guillaume Sahuc

    (EconomiX - EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique)

Abstract

The aim of this paper is to complement the MDE--SVAR approach when the weighting matrix is not optimal. In empirical studies, this choice is motivated by stochastic singularity or collinearity problems associated with the covariance matrix of Impulse Response Functions. Consequently, the asymptotic distribution cannot be used to test the economic model's fit. To circumvent this difficulty, we propose a simple simulation method to construct critical values for the test statistics. An empirical application with US data illustrates the proposed method.
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Patrick Fève & Julien Matheron & Jean-Guillaume Sahuc, 2009. "Minimum Distance Estimation and Testing of DSGE Models from Structural VARs," Post-Print hal-01612710, HAL.
    • Handle: RePEc:hal:journl:hal-01612710
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    Cited by:

    1. Lewis, Vivien & Poilly, Céline, 2012. "Firm entry, markups and the monetary transmission mechanism," Journal of Monetary Economics, Elsevier, vol. 59(7), pages 670-685.
    2. Franke, Reiner, 2013. "Competitive Moment Matching of a New-Keynesian and an Old-Keynesian Model," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79988, Verein für Socialpolitik / German Economic Association.
    3. Günes Kamber & Stephen Millard, 2012. "Using Estimated Models to Assess Nominal and Real Rigidities in the United Kingdom," International Journal of Central Banking, International Journal of Central Banking, vol. 8(4), pages 97-119, December.
    4. Giovanni Angelini & Giuseppe Cavaliere & Luca Fanelli, 2022. "Bootstrap inference and diagnostics in state space models: With applications to dynamic macro models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 3-22, January.
    5. Carrillo, Julio A., 2012. "How well does sticky information explain the dynamics of inflation, output, and real wages?," Journal of Economic Dynamics and Control, Elsevier, vol. 36(6), pages 830-850.
    6. Omotor, Douglason G. & Niringiye, Aggrey, 2011. "Optimum Currency Area and Shock Asymmetry: A Dynamic Analysis of the West African Monetary Zone (WAMZ)," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 71-82, September.
    7. Daniil Lomonosov, 2023. "Shocks of Business Activity and Specific Shocks to Oil Market in DSGE Model of Russian Economy and Their Influence Under Different Monetary Policy Regimes," Russian Journal of Money and Finance, Bank of Russia, vol. 82(4), pages 44-79, December.
    8. Antoine, Bertille & Sun, Wenqian, 2025. "Simulation-based estimation with many auxiliary statistics applied to long-run dynamic analysis," Journal of Econometrics, Elsevier, vol. 248(C).
    9. Reiner Franke, 2018. "Competitive moment matching of a New-Keynesian and an Old-Keynesian model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(2), pages 201-239, July.

    More about this item

    Keywords

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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • 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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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