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Briefing
Macroeconomic Effects of Household Pessimism and Optimism
Survey data on households' expectations about macroeconomic outcomes reveal systematic differences from statistical (or rational) forecasts. We construct an empirical measure of these differences, which we refer to as "belief wedges." Across economic variables, such as inflation and unemployment, these belief wedges are significant and move in parallel with the business cycle. We present a theory of time-varying belief wedges that accounts for these empirical facts. Our theory provides a formal interpretation of these wedges as pessimism and optimism. Embedding the theory into a quantitative ...
Working Paper
Estimating the Effects of Demographics on Interest Rates: A Robust Bayesian Perspective
There are a vast range of estimates for the effect of demographics on interest rates. I show that these magnitudes are not well-identified without data on capital and life-cycle consumption. However, these data are often omitted. Using nonparametric prior sensitivity analysis for an overlapping generations model estimated through Bayesian methods, I show that without these data, small changes in the prior for the discount rate, intertemporal elasticity of substitution, and capital depreciation rate can shift the posterior quantiles for the effects of demographics by up to 1.5 percentage ...
Working Paper
Survey Data and Subjective Beliefs in Business Cycle Models
This paper develops a theory of subjective beliefs that departs from rational expectations, and shows that biases in household beliefs have quantitatively large effects on macroeconomic aggregates. The departures are formalized using model-consistent notions of pessimism and optimism and are disciplined by data on household forecasts. The role of subjective beliefs is quantified in a business cycle model with goods and labor market frictions. Consistent with the survey evidence, an increase in pessimism generates upward biases in unemployment and inflation forecasts and lowers economic ...
Briefing
COVID-19 over Time and across States: Predictions from a Statistical Model
We discuss a statistical time series model to capture and forecast the dynamics of COVID-19 in the fifty U.S. states and Washington, D.C. We design the model to replicate the typical pattern of infections during a pandemic. We rely on Bayesian methods, which provide a straightforward way to quantify the uncertainty surrounding our estimates and forecasts. In this brief, we focus on North Carolina and Washington, D.C., since they have experienced different trajectories of COVID-19 and may have different implications for the efficacy of our approach.
Working Paper
Averaging Impulse Responses Using Prediction Pools
Macroeconomists construct impulse responses using many competing time series models and different statistical paradigms (Bayesian or frequentist). We adapt optimal linear prediction pools to efficiently combine impulse response estimators for the effects of the same economic shock from this vast class of possible models. We thus alleviate the need to choose one specific model, obtaining weights that are typically positive for more than one model. Three Monte Carlo simulations and two monetary shock empirical applications illustrate how the weights leverage the strengths of each model by (i) ...
Working Paper
Global Robust Bayesian Analysis in Large Models
This paper develops a tool for global prior sensitivity analysis in large Bayesian models. Without imposing parametric restrictions, the methodology provides bounds for posterior means or quantiles given any prior close to the original in relative entropy, and reveals features of the prior that are important for the posterior statistics of interest. The author develops a sequential Monte Carlo algorithm and uses approximations to the likelihood and statistic of interest to implement the calculations. Applying the methodology to the error bands for the impulse response of output to a monetary ...
Briefing
What Does Sectoral Inflation Tell Us About the Aggregate Trend in Inflation?
To know the appropriate stance of monetary policy, policymakers need to determine the overall trend in inflation. This is challenging in the face of varied and evolving patterns in inflation across sectors. We describe a multisector statistical model that provides a systematic approach to appropriately weight incoming inflation data from each sector. By flexibly applying time-varying weights to different sectors, this model adjusts to changing patterns in these sectors over time — including during the pandemic — and suggests that trend inflation is lower than might be suggested by the ...
Briefing
Why Are Economists Still Uncertain About the Effects of Monetary Policy?
Despite decades of research, there remains substantial uncertainty about the quantitative effects of monetary policy. Different models produce conflicting predictions, and these predictions lack precision. This article discusses some reasons for these issues. In addition to the relative lack of data, the structure of the economy has continued to evolve, posing challenges for empirical macroeconomic analysis more generally. Economists have been confronting these challenges by developing tools to jointly consider a range of models and continuing to seek new sources of data.
Working Paper
Multilateral Comovement in a New Keynesian World: A Little Trade Goes a Long Way
We study how international linkages and nominal price rigidities jointly shape the dynamics of inflation and output across multiple large economies. We describe how these features produce a global system of Phillips curves explicitly connected by multilateral trade relationships. In equilibrium, disturbances abroad propagate to domestic variables not only directly, through pairwise trade between countries, but also indirectly through third-country effects arising from the network structure of trade. The combined propagation mechanisms imply that country-specific shocks alone explain almost 90 ...