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Working Paper
How To Go Viral: A COVID-19 Model with Endogenously Time-Varying Parameters
This paper estimates a panel model with endogenously time-varying parameters for COVID-19 cases and deaths in U.S. states. The functional form for infections incorporates important features of epidemiological models but is flexibly parameterized to capture different trajectories of the pandemic. Daily deaths are modeled as a spike-and-slab regression on lagged cases. The paper's Bayesian estimation reveals that social distancing and testing have significant effects on the parameters. For example, a 10 percentage point increase in the positive test rate is associated with a 2 percentage point ...
Working Paper
Forecasting in the Absence of Precedent
We survey approaches to macroeconomic forecasting during the COVID-19 pandemic. Due to the unprecedented nature of the episode, there was greater dependence on information outside the econometric model, captured through either adjustments to the model or additional data. The transparency and flexibility of assumptions were especially important for interpreting real-time forecasts and updating forecasts as new data were observed. With data available at the time of writing, we show how various assumptions were violated and how these systematically biased forecasts.
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
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
How Does Trade Impact the Way GDP Growth and Inflation Comove Across Countries?
Seemingly small international trade linkages can lead to substantial spillovers across countries, going a long way in explaining the well-documented global comovement in GDP growth and inflation across countries. The spillovers come largely from indirect effects, with shocks in a foreign country not only propagating to the domestic economy directly but also cumulating through the trade network via other foreign countries. We develop and estimate a model incorporating these network effects, and we find that inflationary shocks in Europe have substantial effects on U.S. inflation and that U.S. ...
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 ...
Discussion Paper
Forecasting the COVID-19 Pandemic in the Fifth District
How many COVID-19 cases will there be in the coming days and months? While the Fifth District appears to be past the peak number of daily cases, a wide range of future outcomes is still possible.
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.
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 ...