Showing results 1 to 8 of approximately 8.(refine search)
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 ...
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 ...
Bubbles and the Value of Innovation
Episodes of booming innovation coincide with intense speculation in financial markets leading to bubbles—increases in market valuations and firm creation followed by a crash. We provide a framework reproducing these facts that makes a rich set of predictions on how speculation changes both the private and social values of innovation. We confirm the theory in the universe of U.S. patents issued from 1926 through 2010. Measures based on financial market information indicate that speculation increases the private value of innovation and reduces negative spillovers to competing firms. No ...
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 ...
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 ...
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.
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 ...
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.