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The Fiscal Multiplier Morass: A Bayesian Perspective

Listed author(s):
  • Todd B. Walker

    (Indiana University)

  • Nora Traum

    (North Carolina State University)

  • Eric M. Leeper

    (Indiana University)

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.

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Paper provided by Society for Economic Dynamics in its series 2011 Meeting Papers with number 583.

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Date of creation: 2011
Handle: RePEc:red:sed011:583
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Society for Economic Dynamics Marina Azzimonti Department of Economics Stonybrook University 10 Nicolls Road Stonybrook NY 11790 USA

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