Search Results
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 Paper
Learning Monetary Policy Strategies at the Effective Lower Bound with Sudden Surprises
Central banks around the world have revised their operating frameworks in an attempt to counter the challenges presented by the effective lower bound (ELB) on policy rates. We examine how private sector agents might learn such a new regime and the effect of future shocks on that process. In our model agents use Bayesian updating to learn the parameters of an asymmetric average inflation targeting rule that is adopted while at the ELB. Little can be discovered until the economy improves enough that rates would be near liftoff under the old policy regime; learning then proceeds until either the ...
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
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 ...
Report
When Long-Run Trends Are Unknown: Bond Pricing Implications
We propose a macro-finance model in which inflation, growth, and the policy rate are driven by unobservable long-run trends and transitory cycles that investors must infer from aggregate data. Their subjective estimates of these trends, and the uncertainty surrounding them, are priced into the Treasury yield curve in a tractable way through both interest rate expectations and bond risk premia. Empirical estimates reveal an upward smooth trend in the long-run real interest rate (r-star) until the 1980s, and large investor uncertainty with confidence bands on as wide as 3.4 percentage points, ...
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
Forward Guidance with Bayesian Learning and Estimation
Considerable attention has been devoted to evaluating the macroeconomic effectiveness of the Federal Reserve's communications about future policy rates (forward guidance) in light of the U.S. economy's long spell at the zero lower bound (ZLB). In this paper, we study whether forward guidance represented a shift in the systematic description of monetary policy by estimating a New Keynesian model using Bayesian techniques. In doing so, we take into account the uncertainty that agents have about policy regimes using an incomplete information setup in which they update their beliefs using Bayes ...
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 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 ...
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 ...