Sparse restricted perceptions equilibrium

Audzei, Volha; Slobodyan, Sergey

doi:10.1016/j.jedc.2022.104415

Cited by 2 publications

(2 citation statements)

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“…As such, this paper can be seen as extending their work in several directions, where we disentangle the effects of the fixed‐equilibrium beliefs, the timing of expectations and the learning algorithm on the model fit. Audzei and Slobodyan (2022) consider a model where agents use misspecified models, and they are allowed to evaluate and change their forecasting models over time. They find that in some parameter regions, agents find it optimal to use their choice of a (misspecified) AR(1) rule.…”

Section: Introductionmentioning

confidence: 99%

“…11 Different types of misspecification equilibria have been proposed in the literature. A nonexhaustive list includes Restricted Perceptions Equilibria (RPE), which generally refer to underparameterized forecasting rules (see, e.g., Sargent (1991), Evans and Honkapohja (2001), Branch (2004), Adam (2007), Bullard, Evans, and Honkapohja (2008), Lansing (2009), Branch and Evans (2010), Lansing and Ma (2017), Audzei and Slobodyan (2022), and Natural Expectations (Fuster, Laibson, and Mendel (2010)) where agents use autoregressive models with lower orders than implied by the correct model. The closest misspecification equilibrium to our work is that of Consistent Expectations Equilibria (CEE) (Hommes and Sorger (1998)), where agents use a simple linear AR(1) rule in a nonlinear model. …”

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confidence: 99%

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Behavioral learning equilibria in New Keynesian models

Hommes,

Mavromatis,

Özden

et al. 2023

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We introduce Behavioral Learning Equilibria (BLE) into a multivariate linear framework and apply it to New Keynesian DSGE models. In a BLE, boundedly rational agents use simple, but optimal AR(1) forecasting rules whose parameters are consistent with the observed sample mean and autocorrelation of past data. We study the BLE concept in a standard 3‐equation New Keynesian model and develop an estimation methodology for the canonical Smets and Wouters (2007) model. A horse race between Rational Expectations (REE), BLE, and constant gain learning models shows that the BLE model outperforms the REE benchmark and is competitive with constant gain learning models in terms of in‐sample and out‐of‐sample fitness. Sample‐autocorrelation learning of optimal AR(1) beliefs provides the best fit when short‐term survey data on inflation expectations are taken into account in the estimation. As a policy application, we show that optimal Taylor rules under AR(1) expectations inherit history dependence and require a lower degrees of interest rate smoothing than REE.

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Section: Introductionmentioning

confidence: 99%