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Report
Central bank transparency and nonlinear learning dynamics
Central bank communication plays an important role in shaping market participants' expectations. This paper studies a simple nonlinear model of monetary policy in which agents have incomplete information about the economic environment. It shows that agents' learning and the dynamics of the economy are heavily affected by central bank transparency about its policy rule. A central bank that does not communicate its rule can induce "learning equilibria" in which the economy alternates between periods of deflation coupled with low output and periods of high economic activity with excessive ...
Working Paper
Market-making with Search and Information Frictions
We develop a dynamic model of trading through market-makers that incorporates two canonical sources of illiquidity: trading (or search) frictions, which imply that market-makers have some amount of market power; and information frictions, which imply that market-makers face some degree of adverse selection. We use this model to study the effects of various technological innovations and regulatory initiatives that have reduced trading frictions in over-the-counter markets. Our main result is that reducing trading frictions can lead to less liquidity, as measured by bid-ask spreads. The key ...
Journal Article
Responding to Pandemic Learning Loss
The end of this school year marks just over two years since the onset of the COVID-19 pandemic. In that time, students and educators across the country have had to adapt continually to new styles of learning and education delivery. Many students have found success in virtual and hybrid environments, while others have had a more difficult time. This has led to a loss in learning compared to where students would normally have been based on their age and development stage. This loss has the potential to set back these students for years to come, affecting not only their development, but also the ...
Report
The science of monetary policy: an imperfect knowledge perspective
New Keynesian theory identifies a set of principles central to the design and implementation of monetary policy. These principles rely on the ability of a central bank to manage expectations precisely, with policy prescriptions typically derived under the assumption of perfect information and full rationality. However, the challenging macroeconomic environment bequeathed by the financial crisis has led many to question the efficacy of monetary policy, and, particularly, to question whether central banks can influence expectations with as much control as previously thought. In this paper, we ...
Report
Heterogeneous inflation expectations and learning
Using the panel component of the Michigan Survey of Consumers, we estimate a learning model of inflation expectations, allowing for heterogeneous use of both private information and lifetime inflation experience. ?Life-experience inflation? has a significant impact on individual expectations, but only for one-year-ahead inflation. Public information is substantially more relevant for longer-horizon expectations. Even controlling for life-experience inflation and public information, idiosyncratic information explains a nontrivial proportion of the inflation forecasts of agents. We find that ...
Working Paper
Risk, Financial Development and Firm Dynamics
I document that the average productivity of firms tends to increase, and its variance to decrease, as they age. These two facts combined suggest that managers learn to reduce their mistakes as they operate. I develop a quantitative framework mimicking these dynamics and find that young firms have substantially higher financing costs due to lower and riskier returns. In this scenario, a reduction in the financial development of an economy raises disproportionately the cost of credit of young-productive firms increasing the input misallocation within this subgroup. To test the validity of the ...
Report
Disagreement and learning in a dynamic contracting model
We present a dynamic contracting model in which the principal and the agent disagree about the resolution of uncertainty, and we illustrate the contract design in an application with Bayesian learning. The disagreement creates gains from trade that the principal realizes by transferring payment to states that the agent considers relatively more likely, a shift that changes incentives. In our dynamic setting, the interaction between incentive provision and learning creates an intertemporal source of ?disagreement risk? that alters optimal risk sharing. An endogenous regime shift between ...
Report
Optimal disinflation under learning
Highly volatile transition dynamics can emerge when a central bank disinflates while operating without full transparency. In our model, a central bank commits to a Taylor rule whose form is known but whose coefficients are not. Private agents learn about policy parameters via Bayesian updating. Under McCallum?s (1999) timing protocol, temporarily explosive dynamics can arise, making the transition highly volatile. Locally unstable dynamics emerge when there is substantial disagreement between actual and perceived feedback parameters. The central bank can achieve low average inflation, but its ...
Working Paper
Ambiguity Aversion and Variance Premium
This paper offers an ambiguity-based interpretation of variance premium?the difference between risk-neutral and objective expectations of market return variance?as a compounding effect of both belief distortion and variance differential regarding the uncertain economic regimes. Our approach endogenously generates variance premium without imposing exogenous stochastic volatility or jumps in consumption process. Such a framework can reasonably match the mean variance premium as well as the mean equity premium, equity volatility, and the mean risk-free rate in the data. We find that about 96 ...
Working Paper
Geometric Methods for Finite Rational Inattention
We present a geometric approach to the finite Rational Inattention (RI) model, recasting it as a convex optimization problem with reduced dimensionality that is well-suited to numerical methods. We provide an algorithm that outperforms existing RI computation techniques in terms of both speed and accuracy. We also introduce methods to quantify the impact of numerical inaccuracy on the behavioral predictions and to produce robust predictions regarding the most frequently implemented actions.