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Keywords:Bayesian learning OR Bayesian Learning 

Working Paper
Modeling the Evolution of Expectations and Uncertainty in General Equilibrium

We develop methods to solve general equilibrium models in which forward-looking agents are subject to waves of pessimism, optimism, and uncertainty that turn out to critically affect macroeconomic outcomes. Agents in the model are fully rational, conduct Bayesian learning, and they know that they do not know. Therefore, agents take into account that their beliefs will evolve according to what they will observe. This framework accommodates both gradual and abrupt changes in beliefs and allows for an analytical characterization of uncertainty. Shocks to beliefs affect economic dynamics and ...
Working Paper Series , Paper WP-2013-12

Working Paper
Learning, Rare Disasters, and Asset Prices

In this paper, we examine how learning about disaster risk affects asset pricing in an endowment economy. We extend the literature on rare disasters by allowing for two sources of uncertainty: (1) the lack of historical data results in unknown parameters for the disaster process, and (2) the disaster takes time to unfold and is not directly observable. The model generates time variation in the risk premium through Bayesian updating of agents' beliefs regarding the likelihood and severity of disaster realization. The model accounts for the level and volatility of U.S. equity returns and ...
Finance and Economics Discussion Series , Paper 2013-85

Working Paper
Constrained Discretion and Central Bank Transparency

We develop and estimate a general equilibrium model in which monetary policy can deviate from active inflation stabilization and agents face uncertainty about the nature of these deviations. When observing a deviation, agents conduct Bayesian learning to infer its likely duration. Under constrained discretion, only short deviations occur: Agents are confident about a prompt return to the active regime, macroeconomic uncertainty is low, welfare is high. However, if a deviation persists, agents? beliefs start drifting, uncertainty accelerates, and welfare declines. If the duration of the ...
Working Paper Series , Paper WP-2014-16

Working Paper
Credit Scores and Inequality across the Life Cycle

Credit scores are a primary screening device for the allocation of credit, housing, and sometimes even employment. In the data, credit scores grow and fan out with age; at the same time, income and consumption inequality also increase with a cohort’s age. We postulate a simple model with hidden information to explore the joint determination of credit scores, income, and consumption over an individual’s lifetime which can replicate these empirical facts. We use the model to understand the role of technologies like big data or legal restrictions limiting information on certain adverse ...
Working Papers , Paper 26-07

Working Paper
An Investigation into the Uncertainty Revision Process of Professional Forecasters

Following Manzan (2021), this paper examines how professional forecasters revise their fixed-event uncertainty (variance) forecasts and tests the Bayesian learning prediction that variance forecasts should decrease as the horizon shortens. We show that Manzan's (2021) use of first moment "efficiency" tests are not applicable to studying revisions of variance forecasts. Instead, we employ monotonicity tests developed by Patton and Timmermann (2012) in the first application of these tests to second moments of survey expectations. We find strong evidence that the variance forecasts are ...
Working Papers , Paper 24-19

Working Paper
Income Volatility and Portfolio Choices

Based on administrative data from Statistics Norway, we find economically significant shifts in households' financial portfolios around structural breaks in income volatility. When the standard deviation of labor-income growth doubles, the share of risky assets decreases by 4 percentage points. We ask whether this estimated marginal effect is consistent with a standard model of portfolio choice with idiosyncratic volatility shocks. The standard model generates a much more aggressive portfolio response than we see in the data. We show that Bayesian learning about the underlying volatility ...
Working Paper , Paper 20-01

Working Paper
Constrained Discretion and Central Bank Transparency

We develop and estimate a general equilibrium model to quantitatively assess the effects and welfare implications of central bank transparency. Monetary policy can deviate from active inflation stabilization and agents conduct Bayesian learning about the nature of these deviations. Under constrained discretion, only short deviations occur, agents? uncertainty about the macroeconomy remains contained, and welfare is high. However, if a deviation persists, uncertainty accelerates and welfare declines. Announcing the future policy course raises uncertainty in the short run by revealing that ...
Working Paper Series , Paper WP-2016-15

Working Paper
Learning about Regime Change

Total factor productivity (TFP) and investment specific technology (IST) growth both exhibit regime-switching behavior, but the regime at any given time is difficult to infer. We build a rational expectations real business cycle model where the underlying TFP and IST regimes are unobserved. We then develop a general perturbation solution algorithm for a wide class of models with unobserved regime-switching. Using our method, we show that learning about regime-switching alters the responses to regime shifts and intra-regime shocks, increases asymmetries in the responses, generates forecast ...
Working Paper Series , Paper 2020-15

Working Paper
News-driven uncertainty fluctuations

We embed a news shock, a noisy indicator of the future state, in a two-state Markov-switching growth model. Our framework, combined with parameter learning, features rich history-dependent uncertainty dynamics. We show that bad news that arrives during a prolonged economic boom can trigger a ?Minsky moment??a sudden collapse in asset values. The effect is greatly amplified when agents have a preference for early resolution of uncertainty. We leverage survey recession probability forecasts to solve a sequential learning problem and estimate the full posterior distribution of model primitives. ...
Working Papers , Paper 18-3

Working Paper
Optimal Long-Term Contracting with Learning

We introduce uncertainty into Holmstrom and Milgrom (1987) to study optimal long-term contracting with learning. In a dynamic relationship, the agent's shirking not only reduces current performance but also increases the agent's information rent due to the persistent belief manipulation effect. We characterize the optimal contract using the dynamic programming technique in which information rent is the unique state variable. In the optimal contract, the optimal effort is front-loaded and decreases stochastically over time. Furthermore, the optimal contract exhibits an option-like feature in ...
FRB Atlanta Working Paper , Paper 2016-10

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