2021
DOI: 10.1016/j.jet.2021.105203
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Monetary policy rules in a non-rational world: A macroeconomic experiment

Abstract: I introduce a new learning-to-forecast experimental design, where subjects in a virtual New-Keynesian macroeconomy based on Woodford (2013) need to forecast individual instead of aggregate outcomes. This approach is motivated by the critique of Preston (2005) and Woodford (2013) that substituting arbitrary forms of expectations into the reduced-form New-Keynesian model (consisting of the "DIS" equation, the "Phillips curve" and the "Taylor" rule) is inconsistent with its microfoundations. Using this design, I … Show more

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Cited by 18 publications
(9 citation statements)
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“…Our framework is a learning-to-forecast (LTF) experiment based on an extended version of Woodford's (2013) model of heterogeneous expectations and monetary policy. The New Keynesian model with heterogeneous expectations was rst introduced in the lab by Mauersberger (2017). In the LTF setup, participants provide incentivized period-by-period forecasts that are used as stand-ins for households' and rms' expectations.…”
Section: Experimental Frameworkmentioning
confidence: 99%
See 1 more Smart Citation
“…Our framework is a learning-to-forecast (LTF) experiment based on an extended version of Woodford's (2013) model of heterogeneous expectations and monetary policy. The New Keynesian model with heterogeneous expectations was rst introduced in the lab by Mauersberger (2017). In the LTF setup, participants provide incentivized period-by-period forecasts that are used as stand-ins for households' and rms' expectations.…”
Section: Experimental Frameworkmentioning
confidence: 99%
“…Excessive volatility of individual forecasts is well-documented in LTF literature, especially in heterogeneous expectations environments(Pfajfar and akelj, 2016;Mauersberger, 2017).…”
mentioning
confidence: 98%
“…She corroborates a finite depth of reasoning, mainly first-or second-order beliefs. Yet, the convergence to the Nash equilibrium occurs due to learning theories [12,[17][18][19].…”
Section: Literaturementioning
confidence: 99%
“…(2013) ; Hommes et al. (2019b) ; Pfajfar, Žakelj, 2014 , Pfajfar, Žakelj, 2018 , and Mauersberger (2021) ).…”
Section: Experimental Designunclassified