Generalized Stochastic Gradient Learning

Evans, George W.; Honkapohja, Seppo; Williams, Noah

doi:10.3386/t0317

Cited by 33 publications

(35 citation statements)

References 22 publications

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“…Parameters estimated with both recursive least squares and Bayesian learning converge to rational expectations equilibrium. 5 However, it is also evident that the dynamic paths of a t and b t di¤er-and that these di¤erences decrease over time. Figure 2 depicts …rst 100 periods from the same simulation.…”

Section: Bayesian Learning Dynamics Can Di¤er From Rlsmentioning

confidence: 99%

The stability of macroeconomic systems with Bayesian learners

Bullard

Suda

2016

Journal of Economic Dynamics and Control

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We study abstract macroeconomic systems in which expectations play an important role. Consistent with the recent literature on recursive learning and expectations, we replace the agents in the economy with econometricians. Unlike the recursive learning literature, however, the econometricians in the analysis here are Bayesian learners. We are interested in the extent to which expectational stability remains the key concept in the Bayesian environment. We isolate conditions under which versions of expectational stability conditions govern the stability of these systems just as in the standard case of recursive learning. We conclude that Bayesian learning schemes, while they are more sophisticated, do not alter the essential expectational stability …ndings in the literature.

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Section: Bayesian Learning Dynamics Can Di¤er From Rlsmentioning

confidence: 99%

The stability of macroeconomic systems with Bayesian learners

Bullard

Suda

2016

Journal of Economic Dynamics and Control

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“…Here the "small constant gain" approximation (i.e. small   (1)) is employed, making the framework similar to that used in Evans, Honkapohja, and Williams (2010). Formally, the setting is now…”

Section: Additional Interpretation and Analysismentioning

confidence: 99%

“…An alternative set-up would be to allow time variation in . Papers by Bullard (1992), McGough (2003), Sargent and Williams (2005), and Evans, Honkapohja, and Williams (2010) look at this issue in models with learning. Cogley and Sargent (2005) look at empirical time-varying parameter models without learning.…”

Section: Priors On Parameter Variationmentioning

confidence: 99%

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Bayesian Model Averaging, Learning, and Model Selection*

Evans¹,

Honkapohja²,

Sargent³

et al. 2013

Macroeconomics at the Service of Public Policy

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Agents have two forecasting models, one consistent with the unique rational expectations equilibrium, another that assumes a time-varying parameter structure. When agents use Bayesian updating to choose between models in a self-referential system, we find that learning dynamics lead to selection of one of the two models. However, there are parameter regions for which the non-rational forecasting model is selected in the long-run. A key structural parameter governing outcomes measures the degree of expectations feedback in Muth's model of price determination.

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“…Sargent and Williams (2005); Evans et al (2010) establish a Bayesian interpretation of learning. Other than relating the learning algorithm to the Kalman filter, the Bayesian optimal estimator of a random walk time-varying parameters model, the priors also carry implications about the specific calibrations of the learning gains.…”

Section: Related Literature Theoretical Foundationsmentioning

confidence: 99%

Empirical Calibration of Adaptive Learning

Berardi

Galimberti

2015

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in ETH ZurichAbstract Adaptive learning introduces persistence in the evolution of agents' beliefs over time.For applied purposes this is a convenient feature to help explain why economies present sluggish adjustments towards equilibrium. The pace of learning is directly determined by the gain parameter, which regulates how quickly new information is incorporated into agents' beliefs.We document renewed empirical calibrations of plausible gain values for adaptive learning applications to macroeconomic data. We cover a broad range of model specifications of applied interest. Our analysis also includes innovative approaches to the endogenous determination of time-varying gains in real-time, and a thorough discussion of the different theoretical interpretations of the learning gain. We also evaluate the merits of different approaches to the gain calibration according to their performance in forecasting macroeconomic variables and in matching survey forecasts.Our results indicate a great degree of heterogeneity in the gain calibrations according to the variable forecasted and the lag length of the model specifications. Calibrations to match survey forecasts are found to be lower than those derived according to the forecasting performance, suggesting some degree of bounded rationality in the speed with which agents update their beliefs.

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Generalized Stochastic Gradient Learning

Cited by 33 publications

References 22 publications

The stability of macroeconomic systems with Bayesian learners

The stability of macroeconomic systems with Bayesian learners

Bayesian Model Averaging, Learning, and Model Selection*

Empirical Calibration of Adaptive Learning

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