The Stability of Macroeconomic Systems with Bayesian Learners

Bullard, James B.; Suda, Jacek

doi:10.20955/wp.2008.043

Cited by 27 publications

(6 citation statements)

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“…An alternative would be to assume that agents act as Bayesian econometricians, who update their priors in light of new sample observations. The latter case has been studied in Bullard and Suda (2011), who show how extra terms may appear in the economy's law of motion. 8 Although agents' learning with the described endogenous gain is by no means optimal, it can be expected to provide a fairly good approximation to the optimal forecasting behavior of agents who are concerned about possible unknown breaks in the economy, but who do not want to take a stand on the nature or timing of the breaks, or on the existence or number of regimes, and assuming that the agents, in their loss function, are much more concerned about very large forecast errors than relatively small ones.…”

Section: Learningmentioning

confidence: 99%

Learning and time-varying macroeconomic volatility

Milani

2014

Journal of Economic Dynamics and Control

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a b s t r a c tThis paper presents a DSGE model in which agents' learning about the economy can endogenously generate time-varying macroeconomic volatility. Economic agents use simple models to form expectations and need to learn the relevant parameters. Their gain coefficient is endogenous and is adjusted according to past forecast errors.The model is estimated using likelihood-based Bayesian methods. The endogenous gain is jointly estimated with the structural parameters of the system.The estimation results show that private agents appear to have often switched to constant-gain learning, with a high constant gain, during most of the 1970s and until the early 1980s, while reverting to a decreasing gain later on. As a result, the model can generate a pattern of volatility, which is increasing in the 1970s and falling in the second half of the sample, with a decline that can roughly match the magnitude of the so-called "Great Moderation" in the 1984-2007 period. The paper also documents how a failure to incorporate learning into the estimation may lead econometricians to spuriously find time-varying volatility in the exogenous shocks, even when these have constant variance by construction.

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Section: Learningmentioning

confidence: 99%

Learning and time-varying macroeconomic volatility

Milani

2014

Journal of Economic Dynamics and Control

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“…Cogley and Sargent (2008a) show that the consumption and investment choices under fully Bayesian (i.e., internalizing future updating) and adaptive learning behavior are very similar for low values of the coefficient of relative risk aversion. Similarly, Bullard and Suda (2016) show that Bayesian learning schemes, while more sophisticated, do not alter the standard expectational stability conditions in a class of linear expectational models. They do show, however, that the transitional dynamics could be different.…”

Section: Discussionmentioning

confidence: 91%

Belief-Twisting Shocks and the Macroeconomy

Suda

2018

Macroecon. Dynam.

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I study the role of shocks to beliefs combined with Bayesian learning in a standard equilibrium business cycle framework. In particular, I examine how a prior belief arising from the Great Depression may have influenced the macroeconomy during the last 75 years. In the model, households hold twisted beliefs concerning the likelihood and persistence of recession and boom states that are affected by the Great Depression. These initial beliefs are substantially different from the true data generating process and are only gradually unwound during subsequent years. Even though the driving stochastic process for technology is unchanged over the entire period, the nature of macroeconomic performance is altered considerably for many decades before eventually converging to the rational expectations equilibrium. This provides some evidence of the lingering effects of beliefs-twisting events on the behavior of macroeconomic variables.

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“…Here we show that under Bayesian Learning, current expectations are affected by past forecast errors; this, together with the assumption that exclusion restriction holds, establishes the validity of past forecast errors as instruments for current forecasts. We closely follow Bullard and Suda (2008). Suppose that the true fundamental, θ, follows an AR 1process:…”

Section: Resultsmentioning

confidence: 99%

Endogenous Debt Maturity and Rollover Risk

Brancati¹,

Macchiavelli²

2016

FEDS

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We challenge the common view that short-term debt, by having to be rolled over continuously, is a risk factor that exposes banks to higher default risk. First, we show that the average effect of expiring obligations on default risk is insignificant; it is only when a bank has limited access to new funds that maturing debt has a detrimental impact on default risk. Next, we show that both limited access to new funds and shorter maturities are causally determined by deteriorating market expectations about the bank's future profitability. In other words, short-term debt is not a cause of fragility but the result of creditors losing faith in the long-run prospects of the bank, hence forcing it to shorten its debt maturity. Finally, we build a model that endogenizes the debt maturity structure and predicts that worse market expectations lead to a maturity shortening.

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The Stability of Macroeconomic Systems with Bayesian Learners

Cited by 27 publications

References 0 publications

Learning and time-varying macroeconomic volatility

Learning and time-varying macroeconomic volatility

Belief-Twisting Shocks and the Macroeconomy

Endogenous Debt Maturity and Rollover Risk

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