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The Fiscal Multiplier Morass: A Bayesian Perspective

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  • Todd B. Walker

    (Indiana University)

  • Nora Traum

    (North Carolina State University)

  • Eric M. Leeper

    (Indiana University)

Abstract

A Bayesian prior predictive analysis is conducted on a suite of models to assess the probability that a model and corresponding prior distributions bias results toward a specific range of fiscal multipliers. We examine a wide range of DSGE models commonly used to estimate fiscal multipliers, including a real business cycle model, a New Keynesian model with nominal and real rigidities, and open economy models. We decompose changes in multipliers across models into wealth and substitution effects, allowing for a more uniform comparison across models. Through the prior predictive analysis, we show that many of the models and prior distributions impose a very tight range for the multiplier before the models are taken to data. We argue that constraining the multiplier to such a tight range prior to conditioning on data is tantamount to biasing results. A broader message of the paper calls for employing prior predictive analysis when estimating DSGE models.

Suggested Citation

  • Todd B. Walker & Nora Traum & Eric M. Leeper, 2011. "The Fiscal Multiplier Morass: A Bayesian Perspective," 2011 Meeting Papers 583, Society for Economic Dynamics.
  • Handle: RePEc:red:sed011:583
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    File URL: https://economicdynamics.org/meetpapers/2011/paper_583.pdf
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    Cited by:

    1. Thorsten Drautzburg & Harald Uhlig, 2015. "Fiscal Stimulus and Distortionary Taxation," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 18(4), pages 894-920, October.
    2. Kollmann, Robert & Ratto, Marco & Roeger, Werner & in′t Veld, Jan, 2013. "Fiscal policy, banks and the financial crisis," Journal of Economic Dynamics and Control, Elsevier, vol. 37(2), pages 387-403.
    3. Thorsten Drautzburg & Harald Uhlig, 2015. "Fiscal Stimulus and Distortionary Taxation," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 18(4), pages 894-920, October.

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