Health Types and Health Inequality, with Margherita Borella,
Mariacristina De Nardi,
Benjamin Kruger, and
Elena Manresa.
The Econometric Journal, 2025.
Abstract: While health affects many economic outcomes, its dynamics are still poorly understood. We use k-means clustering, a machine learning technique, and data from the Health and Retirement Study to identify health types during middle and old age. We identify five health types: the vigorous resilient, the fair-health resilient, the fair-health vulnerable, the frail resilient, and the frail vulnerable. They are characterized by different starting health and health and mortality trajectories. Our five health types account for 84% of the variation in health trajectories and are not explained by observable characteristics, such as age, marital status, education, gender, race, health-related behaviors, and health insurance status, but rather, by one’s past health dynamics. We also show that health types are important drivers of health and mortality heterogeneity and dynamics. Our results underscore the importance of better understanding health type formation and of modeling it appropriately to properly evaluate the effects of health on people’s decisions and the implications of policy reforms.
Tracking R of COVID-19, with Francisco Arroyo-Mariolli,
Simas Kucinskas, and
Carlos Rondon-Moreno.
PLoS ONE 16(1): e0244474, 2021
Abstract: We develop a new method for estimating the effective reproduction number of an infectious disease (R) and apply it to track the dynamics of COVID-19. The method is based on the fact that in the SIR model, R is linearly related to the growth rate of the number of infected individuals. This time-varying growth rate is estimated using the Kalman filter from data on new cases. The method is easy to implement in standard statistical software, and it performs well even when the number of infected individuals is imperfectly measured, or the infection does not follow the SIR model. Our estimates of R for COVID-19 for 124 countries across the world are provided in an interactive online dashboard, and they are used to assess the effectiveness of non-pharmaceutical interventions in a sample of 14 European countries.
Semi-Structural Forecasting Model, with Francisco Arroyo-Mariolli,
Jorge Fornero, and
Roberto Zúñiga.
Working Papers of the Central Bank of Chile, N 866, 2020
Abstract: The semi-structural gap forecasting (MSEP) model is the new gap model used by the Central Bank of Chile to forecast key macroeconomics variables. This document provides the technical details of this model including equations, estimated parameters and transmission mechanisms. The model has been improved relative to its initial version along several dimensions: (i) The parameters have been estimated with Bayesian methods; (ii) it separates core inflation into tradable and non-tradable inflation, linking each component to fundamental drivers; (iii) it explicitly specifies the empirical relationships between terms of trade and real exchange rate. We found that for a typical monetary policy shocks there are similar effects in comparison with the former MEP model.