NBER

Paul Novosad

Economics Department
Dartmouth College
6106 Rockefeller Center
Room 301
Hanover, NH 03755

E-Mail: EmailAddress: hidden: you can email any NBER-related person as first underscore last at nber dot org
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Institutional Affiliation: Dartmouth College

NBER Working Papers and Publications

July 2020Adaptive Control of COVID-19 Outbreaks in India: Local, Gradual, and Trigger-based Exit Paths from Lockdown
with Anup Malani, Satej Soman, Sam Asher, Clement Imbert, Vaidehi Tandel, Anish Agarwal, Abdullah Alomar, Arnab Sarker, Devavrat Shah, Dennis Shen, Jonathan Gruber, Stuti Sachdeva, David Kaiser, Luis M.A. Bettencourt: w27532
Managing the outbreak of COVID-19 in India constitutes an unprecedented health emergency in one of the largest and most diverse nations in the world. On May 4, 2020, India started the process of releasing its population from a national lockdown during which extreme social distancing was implemented. We describe and simulate an adaptive control approach to exit this situation, while maintaining the epidemic under control. Adaptive control is a flexible counter-cyclical policy approach, whereby different areas release from lockdown in potentially different gradual ways, dependent on the local progression of the dis- ease. Because of these features, adaptive control requires the ability to decrease or increase social distancing in response to observed and projected dynamics of the disease out...
March 2020Comparing Intergenerational Educational Mobility for Minorities Around the World
with Sam Asher, Charlie Rafkin
in Measuring and Understanding the Distribution and Intra/Inter-Generational Mobility of Income and Wealth, Raj Chetty, John N. Friedman, Janet C. Gornick, Barry Johnson, and Arthur Kennickell, editors
December 2016Classification Trees for Heterogeneous Moment-Based Models
with Sam Asher, Denis Nekipelov, Stephen P. Ryan: w22976
A basic problem in applied settings is that different parameters may apply to the same model in different populations. We address this problem by proposing a method using moment trees; leveraging the basic intuition of a classification tree, our method partitions the covariate space into disjoint subsets and fits a set of moments within each subspace. We prove the consistency of this estimator and show standard rates of convergence apply post-model selection. Monte Carlo evidence demonstrates the excellent small sample performance and faster-than-parametric convergence rates of the model selection step in two common empirical contexts. Finally, we showcase the usefulness of our approach by estimating heterogeneous treatment effects in a regression discontinuity design in a development sett...

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