Northwestern University Weinberg College of Arts and Science
Ph.D. in Economics, August 2019 - Ongoing
New York University Stern School of Business
B.S. in Business Economics with a minor in Mathematics, May 2017
Graduated summa cum laude
Online Estimation of DSGE Models
with Marco Del Negro, Edward Herbst, Ethan Matlin, Reca Sarfati, and Frank Schorfheide
The Econometrics Journal: Volume 24, Issue 1, Jan. 2021, Pg. C33-C58
Also available as an NBER Working Paper.
This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, document the accuracy and runtime benefits of generalized data tempering for “online” estimation (that is, re-estimating a model as new data become available), and provide examples of multimodal posteriors that are well captured by SMC methods. We then use the online estimation of the DSGE model to compute pseudo-out-of-sample density forecasts and study the sensitivity of the predictive performance to changes in the prior distribution. We find that making priors less informative (compared to the benchmark priors used in the literature) by increasing the prior variance does not lead to a deterioration of forecast accuracy.
DSGE Forecasts of the Lost Recovery
with Marco Del Negro, Marc P. Giannoni, Abhi Gupta, Pearl Li, & Erica Moszkowski
International Journal of Forecasting: Volume 35, Issue 4, Oct-Dec 2019, Pg. 1770-1789
Also available as a Federal Reserve Bank of New York Staff Report.
The years following the Great Recession were challenging for forecasters. Unlike other deep downturns, this recession was not followed by a swift recovery, but generated a sizable and persistent output gap that was not accompanied by deflation as a traditional Phillips curve relationship would have predicted. Moreover, the zero lower bound and unconventional monetary policy generated an unprecedented policy environment. We document the real real-time forecasting performance of the New York Fed dynamic stochastic general equilibrium (DSGE) model during this period and explain the results using the pseudo real-time forecasting performance results from a battery of DSGE models. We find the New York Fed DSGE model’s forecasting accuracy to be comparable to that of private forecasters and notably better, for output growth, than the median forecasts from the Federal Open Market Committee’s Summary of Economic Projections. The model’s financial frictions were key in obtaining these results as they implied a slow recovery following the financial crisis.
Research and Teaching Experience
Northwestern University Department of Economics
Research Assistant for Prof. Matt Rognlie, Summer 2020 - Ongoing
Federal Reserve Bank of New York
Research Analyst, Macroeconomic and Monetary Studies, Research Group, July 2017 - July 2019
New York University Department of Economics
Research Assistant for Prof. Tim Christensen, Spring 2016/2017
Research Assistant for Prof. Dave Backus, Spring 2016
New York University Stern Urbanization Project
Research Assistant for Prof. Paul Romer, Fall 2015
Honors and Grants
XSEDE Research Allocation - XRAC SES190003 (March 2019)
Estimating Heterogeneous Agent Dynamic Stochastic General Equilibrium (DSGE) Models using Sequential Monte Carlo
Award for Academic Excellence in Economics (Given to a single student in the undergraduate college)
Beta Gamma Sigma Honors Society
“Estimating Non-Linear Macroeconomic Models at the New York Fed,” JuliaCon 2018, University College London, August 2018