Working papers

Households' Preferences for Inflation and Monetary Policy Tradeoffs

with Damjan Pfajfar

We document novel facts about the preferences for inflation and monetary policy of U.S. households. Using a special module in the Survey of Consumer Expectations (SCE) in June 2023, we find that many households are highly attentive to news about monetary policy and interest rates. The median household perceives the Federal Reserve's inflation target to be three percent, but would prefer it to be lower. Quantifying the tradeoff between inflation and unemployment, we find an average acceptable sacrifice ratio of 0.6. This value is much lower than the necessary sacrifice ratios estimated in the literature, implying that households are likely to find disinflation costly. Average stated preferences are well represented by a loss function with near equal weights on inflation and unemployment. That said, the relative preference for price stability exhibits sizable heterogeneity among U.S. households: Older, more educated, or male respondents exhibit a stronger preference for inflation stabilization.

The Natural Rate of Interest Through a Hall of Mirrors

with Phurichai Rungcharoenkitkul

We propose a novel explanation for persistent movements in the natural rate of interest (r-star) based on two-sided learning between the central bank and the private sector. We analyze a New-Keynesian model where both learn about r-star from each other. When both sides fail to recognise that their actions influence the other’s beliefs, a “hall-of-mirrors” effect arises that causes persistent shifts in r-star in response to cyclical shocks. The effect can explain the post-2008 decline in r-star even if long-run fundamentals had not changed. Conversely, a surge in inflation accompanied by monetary policy tightening can induce a persistent r-star increase.

Presentations: 2022: AEA annual meeting, Swiss National Bank Research Conference, Bank of Thailand; 2021: Bank of Finland,, conference on Expectations in Dynamic Macroeconomic Models conference at the Czech National Bank, CEF Annual Meeting, EEA Annual Congress, European Winter Meeting of the Econometric Society, MMF Annual Conference.

A Comprehensive Empirical Evaluation of Biases in Expectation Formation 

with Kenneth Eva

We revisit predictability of forecast errors in macroeconomic survey data, which is often taken as evidence of behavioral biases at odds with rational expectations. We argue that to reject rational expectations, one must be able to predict forecast errors out of sample. However, the regressions used in the literature perform poorly out of sample in most cases. The models seem unstable and could not have helped to improve forecasts with access only to available information. We do find some notable exceptions, such as mean bias in interest rate forecasts, that survive our out-of-sample tests. Our findings thus narrow down the set of biases that merit the attention of researchers in behavioral macroeconomics.

Presentations: 2023: ASSA Annual Meeting, Econometric Society North American Summer Meeting, NBER Summer Institute; 2022: CEF Annual Meeting

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.

Presentations: 2020: CEF Annual Meeting

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.

Presentations: 2022: Penn State University; 2021: Shanghai University of Finance and Economics, ASSA Annual Meeting; 2020: Norges Bank Macro Modelling Workshop, CEBRA Annual Meeting; IWH-CIREQ-GW Macroeconometrics Workshop; 2019: GCER conference at Georgetown University, FAU Nuremberg, Deutsche Bundesbank, Dallas Fed; 2018: George Washington University, Singapore Management University


Learning and Misperception: Implications for Price-Level Targeting

Journal of Economic Dynamics and Control (forthcoming), 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.

Presentations: 2022: SUERF workkshop on "Macroeconomic models for monetary policy", GCGBF Annual Conference; 2021: Oesterreichische Nationalbank; 2020: EEA Annual Meeting; 2019: European Central Bank

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.

Presentations: 2021: SED Annual Meeting in Barcelona; 2019: Boston College, Dallas Fed, SED Annual Meeting in St. Louis, CEBRA Annual Meeting at Columbia University; 2018: UBC, UC Irvine, Drexel University, San Francisco Fed, "Expectations in Dynamic Macroeconomic Models" conference at Birmingham University, "Asset Prices and the Macroeconomy" conference at Mannheim University, Midwest Macro Conference, CEA Annual Meeting, Econometric Society North American Summer Meeting; 2017: Chicago Fed.

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.

Presentations: 2017: ASSA Annual Meeting in Chicago

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.

Presentations: 2017: Duke University, USC; 2016: NBER Summer Institute, Dynare Conference in Rome, "Expectations in Dynamic Macroeconomic Models" conference at De Nederlandsche Bank, 2015: Banca d'Espana, Banque de France, UQAM, University of Lausanne, University of Bern, National University of Singapore, Singapore Management University, Econometric Society World Congress in Montreal; 2014: Econometric Society European Winter Meeting, EEA Annual Meeting

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.

Presentations: 2018: Barcelona GSE Summer Forum; 2017: Humboldt University, University of Cologne, GCER conference at Georgetown University, Banque de France-Bundesbank annual workshop, IAB-Bundesbank joint conference; 2016: Universitat Autonoma Barcelona, University of Lyon, OeNB-Bundesbank annual workshop; 2015: EEA Annual Meeting in Mannheim, UQAM