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Journal of Econometrics, Forthcoming
We estimate 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. Our 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 increase in the death rate among reported cases. The model forecasts perform well, even relative to models from epidemiology and statistics.
New Zealand Economic Papers, Forthcoming
We estimate a statistical model for COVID-19 cases and deaths in New Zealand.  New Zealand is an important test case for statistical and theoretical research into the dynamics  of  the  global  pandemic  since  it  went  through  a  full  cycle  of  infections.   We choose functional forms for infections and deaths that incorporate important features of epidemiological models but allow for flexible parameterization to capture different trajectories of the pandemic.  Our Bayesian estimation reveals that the simple statistical framework we employ fits the data well and allows for a transparent characterization of the uncertainty surrounding the trajectories of infections and deaths.

Working Papers

Revise and Resubmit, Review of Economic Studies

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 activity. The underlying belief distortions reduce aggregate demand and propagate through frictional goods and labor markets. As a by-product of the analysis, solution techniques that preserve the effects of time-varying belief distortions in the class of linear solutions are developed.

Revise and Resubmit, Journal of Econometrics
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. We develop a sequential Monte Carlo algorithm and use 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 policy shock in the New Keynesian model of Smets and Wouters (2007), we show that the upper bound of the error bands is very sensitive to the prior but the lower bound is not, with the prior on wage rigidity playing a particularly important role.
Revise and Resubmit, Journal of Financial Economics
Episodes of booming innovation coincide with intense speculation in financial markets. What can asset prices teach us about innovations during bubbles? In our theory, investor speculation about which firms will succeed creates a bubble. An innovation raises the stock price of its creator more than justified by future outcomes. However, prices of competing firms do not get penalized even though their profits suffer. These predictions do not arise in alternative theories of bubbles; we confirm them and other aspects of our model using over a million patents. Efficient innovation policy uses information from prices and real outcomes despite their disconnect.
Revise and Resubmit, Journal of Economic Surveys
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.  We revisit these themes with a time-varying parameter vector autoregression, which attributes the large jumps primarily to increased volatility rather than changes in the the type or propagation of shocks.
We highlight a reason for the vast range of estimates for the effect of demographics on interest rates: the magnitudes are not well-identified without often omitted data on capital and life-cycle consumption. Using nonparametric prior sensitivity analysis for an overlapping generations model estimated through Bayesian methods, we show small changes in the prior for the discount rate, intertemporal elasticity of substitution, and depreciation rate can shift posterior quantiles for the effects of demographics by up to 1.5 percentage points. Capital-output ratio data substantially tighten estimates of the depreciation rate but not the discount rate. Life-cycle consumption informs all three parameters.

Non-Academic Articles

Federal Reserve Bank of Richmond Economic Brief, June 2021, No. 21-19
Federal Reserve Bank of Richmond Economic Brief, January 2021, No. 21-03
Federal Reserve Bank of Richmond Economic Brief, September 2020, No. 20-10
Federal Reserve Bank of Richmond Special Report, May 8, 2020
Regional Matters, April 23, 2020