The Factor Structure of Disagreement
with Edward Herbst
We estimate a three-dimensional dynamic factor model on individual forecasts in the Survey of Professional Forecasters using Bayesian methods. The factors extract the most important dimensions along which disagreement comoves across variables. We interpret our results through a general semi-structural dispersed information model of heterogeneous expectations. The two most important factors in the data describe disagreement about aggregate supply and demand, respectively. Up until the Great Moderation, supply disagreement dominated, while in recent decades and particularly during the Great Recession, demand disagreement has become more important. By contrast, disagreement about monetary policy shocks seems to play a minor role in the data. Our findings can serve to discipline structural models of heterogeneous expectations.
Impulse-based Computation of Optimal Policy Problems
with James Hebden
We propose a new computational procedure to solve for optimal monetary policy and other policy counterfactuals in linear models with occasionally binding constraints. The procedure neither requires knowledge of the structural or reduced-form equations of the model, its state variables, nor its shock processes. It also does not require filtering structural shocks on the equilibrium path of interest. All that is required is a projection of the variables entering the policy problem and impulse response functions of these variables to the monetary policy instruments, computed under an arbitrary instrument rule. We show how to compute solutions for Taylor-type instrument rules as well as optimal paths for quadratic loss functions under discretion and commitment, and discuss various extensions including imperfect information. The procedure facilitates the comparison of the effects of a policy regime across models, and can thus be used to address concerns of model uncertainty.
A Hall of Mirrors: Misperception of the Natural Real Rate of Interest
with Phurichai Rungcharoenkitkul
Prevailing explanations of low-for-long interest rates appeal to a secular decline in the natural interest rate, or r-star. The underlying causes are thought to be outside monetary policy's control. This paper proposes informational feedback via learning as an alternative driver of r-star. We extend the canonical New Keynesian model to an incomplete information setting in which both the central bank and the private sector have private information about r-star determinants and try to learn each other's information from observed macroeconomic outcomes. When each side underestimates the effect of its own action on the other's inference, a strong feedback loop emerges that amplifies noise and can lead to large and persistent changes in perceived r-star that are in fact disconnected from fundamentals. We simulate a calibrated model and show that this `hall-of-mirrors' mechanism can explain much of the decline in real interest rates since 2008, without appealing to any changes in the actual r-star determinants.
Learning and Misperception: Implications for Price-Level Targeting
Revise and Resubmit, Journal of Economic Dynamics and Control. With Martin Bodenstein and James Hebden
Given the mixed success with forward guidance policies in the aftermath of the financial crisis, monetary policy strategies that target the price level have been advocated as a more effective way to provide economic stimulus in a deep recession when conventional monetary policy is limited by the zero lower bound on nominal interest rates. Yet, the effectiveness of both forward guidance and price-level targeting strategies depends on a central bank's ability to steer agents' expectations about the future path of the policy rate. We develop a flexible method of learning about the central bank's reaction function from observed interest rates that takes into account the limited informational content at the zero lower bound. When agents learn, switching from an inflation targeting to a price-level targeting strategy at the onset of a recession may not yield the desired stabilization benefits but make matters worse. Nevertheless, agents can eventually learn the systematic monetary policy response of a price-level targeting strategy, an advantage not shared by ad-hoc forward guidance strategies including temporary price-level targeting.
Asset Price Beliefs and Optimal Monetary Policy
Journal of Monetary Economics (2021), with Colin Caines
We characterize optimal monetary policy when agents have extrapolative beliefs about asset prices that induce inefficient fluctuations in asset prices, aggregate demand and investment. We find that the optimal monetary policy raises interest rates when expected capital gains or the level of current asset prices is high, but does not eliminate deviations of asset prices from their fundamental value. When the asset is in elastic supply, optimal policy also leans against the wind, tolerating low inflation and output when asset prices are too high. Optimal policy can be reasonably approximated by simple interest rate rules that respond to capital gains. Our results are robust to a wide range of belief specifications.
In full-information estimates, long-run risks explain at most a quarter of p/d variance, and habit explains even less
Critical Finance Review (2021), with Andrew Chen and Rebecca Wasyk
Many consumption-based models succeed in matching long lists of asset price moments. We propose an alternative, full-information Bayesian evaluation that decomposes the price-dividend ratio (p/d) into contributions from long-run risks, habit, and a residual. We find that long-run risks account for less than 25% of the variance of p/d and that habit’s contribution is negligible. This result is robust to the prior, including priors that assume long-run risks in consumption and highly persistent habit. However, the residual mostly tracks decades-long movements in p/d. At business cycle frequency, long-run risks explain about 70% of the movements of p/d while habit’s contribution stays negligible.
The Role of Learning for Asset Prices and Business Cycles
Journal of Monetary Economics (2020)
I examine the implications of learning-based asset pricing in a model in which firms face credit constraints that depend partly on their market value. Agents learn about stock prices, but have conditionally model-consistent expectations otherwise. The model jointly matches key asset price and business cycle statistics, while the combination of financial frictions and learning produces powerful feedback between asset prices and real activity, adding substantial amplification. The model reproduces many patterns of forecast error predictability in survey data that are inconsistent with rational expectations. A reaction of the monetary policy rule to asset price growth increases welfare under learning.
Unemployment Insurance and International Risk Sharing
European Economic Review (2019), with Stéphane Moyen and Nikolai Stähler
We discuss how cross-country unemployment insurance can be used to improve international risk sharing. We use a two-country business cycle model with incomplete financial markets and frictional labor markets where the unemployment insurance scheme operates across both countries. Cross-country insurance through the unemployment insurance system can be achieved without affecting unemployment outcomes. The Ramsey-optimal policy however prescribes a more countercyclical replacement rate when international risk sharing concerns enter the unemployment insurance trade-off. We calibrate our model to Eurozone data and find that optimal stabilizing transfers through the unemployment insurance system are sizable and mainly stabilize consumption in the periphery countries, while optimal replacement rates are countercylical overall. We also find that debt-financed national policies are a poor substitute for fiscal transfers.