Adaptive learning in practice

Cárceles‐Poveda, Eva; Giannitsarou, Chryssi

doi:10.1016/j.jedc.2006.09.004

“…As shown in Carceles-Poveda and Giannitsarou (2007), the initialization of adaptive learning algorithms can have important e¤ects on the model dynamics. We therefore use two di¤erent initializations.…”

Section: Numerical Resultsmentioning

confidence: 99%

Asset pricing with adaptive learning

Cárceles‐Poveda

¹

,

Giannitsarou

²

2008

Review of Economic Dynamics

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Abstract. We study the extent to which self-referential adaptive learning can explain stylized asset pricing facts in a general equilibrium framework. In particular, we analyze the e¤ects of recursive least squares and constant gain algorithms in a production economy and a Lucas type endowment economy. We …nd that recursive least squares learning has almost no e¤ects on asset price behavior, since the algorithm converges relatively fast to rational expectations. On the other hand, constant gain learning may contribute towards explaining the stock price and return volatility as well as the predictability of excess returns in the endowment economy. In the production economy, however, the e¤ects of constant gain learning are mitigated by the persistence induced by capital accumulation. We conclude that, contrary to popular belief, standard self-referential learning cannot fully resolve the asset pricing puzzles observed in the data.

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“…The LS dominance also has been challenged in previous applied studies (see Bullard and Eusepi, 2005;Carceles-Poveda and Giannitsarou, 2007). Hence, it remains open the question of which algorithm should be taken as representative from an empirical standpoint.…”

Section: Introductionmentioning

confidence: 99%

A note on the representative adaptive learning algorithm

Berardi

¹

,

Galimberti

²

2014

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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 EconStor may AbstractWe compare forecasts from different adaptive learning algorithms and calibrations applied to US real-time data on inflation and growth. We find that the Least Squares with constant gains adjusted to match (past) survey forecasts provides the best overall performance both in terms of forecasting accuracy and in matching (future) survey forecasts.

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“…Its usage has since been prominent in studies on the effects of replacing the assumption of frictionless REE by the sticky process of expectations formation through adaptive learning (e.g., Marcet and Nicolini, 2003;Bullard and Eusepi, 2005;Orphanides and Williams, 2005b). For simulations, robust inferences can be obtained through this method by drawing the initials from a distribution centered around the REE values (see Carceles-Poveda and Giannitsarou, 2007).…”

Section: Equilibrium-related Methodsmentioning

confidence: 99%

On the initialization of adaptive learning in macroeconomic models

Berardi

¹

,

Galimberti

²

2017

Journal of Economic Dynamics and Control

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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 Zurich AbstractWe review and evaluate methods previously adopted in the applied literature of adaptive learning in order to initialize agents' beliefs. Previous methods are classified into three broad classes: equilibrium-related, training sample-based, and estimation-based. We conduct several simulations comparing the accuracy of the initial estimates provided by these methods and how they affect the accuracy of other estimated model parameters. We find evidence against their joint estimation with standard moment conditions: as the accuracy of estimated initials tends to deteriorate with the sample size, spillover effects also deteriorate the accuracy of the estimates of the model's structural parameters. We show how this problem can be attenuated by penalizing the variance of estimation errors. Even so, the joint estimation of learning initials with other model parameters is still subject to severe distortions in small samples. We find that equilibrium-related and training sample-based initials are less prone to these issues. We also demonstrate the empirical relevance of our results by estimating a New Keynesian Phillips curve with learning, where we find that our estimation approach provides robustness to the initialization of learning. That allows us to conclude that under adaptive learning the degree of price stickiness is lower compared to inferences under rational expectations, whereas the fraction of backward looking price setters increases.

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Adaptive learning in practice

Cited by 61 publications

References 23 publications

Asset pricing with adaptive learning

Asset pricing with adaptive learning

A note on the representative adaptive learning algorithm

On the initialization of adaptive learning in macroeconomic models

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