Department of Economics
ithaca, NY 14853
Institutional Affiliation: Cornell University
Information about this author at RePEc
NBER Working Papers and Publications
|April 2020||Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem|
with Charles F. Manski: w27023
As a consequence of missing data on tests for infection and imperfect accuracy of tests, reported rates of population infection by the SARS CoV-2 virus are lower than actual rates of infection. Hence, reported rates of severe illness conditional on infection are higher than actual rates. Understanding the time path of the COVID-19 pandemic has been hampered by the absence of bounds on infection rates that are credible and informative. This paper explains the logical problem of bounding these rates and reports illustrative findings, using data from Illinois, New York, and Italy. We combine the data with assumptions on the infection rate in the untested population and on the accuracy of the tests that appear credible in the current context. We find that the infection rate might be substantia...
Published: Charles F. Manski & Francesca Molinari, 2020. "Estimating the COVID-19 infection rate: Anatomy of an inference problem," Journal of Econometrics, .
|July 2019||Precise or Imprecise Probabilities? Evidence from Survey Response on Late-onset Dementia|
with Pamela Giustinelli, Charles F. Manski: w26125
We elicit numerical expectations for late-onset dementia in the Health and Retirement Study. Our elicitation distinguishes between precise and imprecise probabilities, while accounting for rounding of reports. Respondents quantify imprecision using probability intervals. Nearly half of respondents hold imprecise dementia probabilities, while almost a third of precise-probability respondents round their reports. We provide the first empirical evidence on dementia-risk perceptions among dementia-free older Americans and novel evidence about imprecise probabilities in a nationally-representative sample. We show, in a specific framework, that failing to account for imprecise or rounded probabilities can yield incorrect predictions of long-term care insurance purchase decisions.
|February 2019||Best Linear Approximations to Set Identified Functions: With an Application to the Gender Wage Gap|
with Arun G. Chandrasekhar, Victor Chernozhukov, Paul Schrimpf: w25593
This paper provides inference methods for best linear approximations to functions which are known to lie within a band. It extends the partial identification literature by allowing the upper and lower functions defining the band to carry an index, and to be unknown but parametrically or non-parametrically estimable functions. The identification region of the parameters of the best linear approximation is characterized via its support function, and limit theory is developed for the latter. We prove that the support function can be approximated by a Gaussian process and establish validity of the Bayesian bootstrap for inference. Because the bounds may carry an index, the approach covers many canonical examples in the partial identification literature arising in the presence of interval value...
|April 2018||Tail and Center Rounding of Probabilistic Expectations in the Health and Retirement Study|
with Pamela Giustinelli, Charles F. Manski: w24559
A growing number of surveys elicit respondents’ expectations for future events on a 0-100 scale of percent chance. These data reveal substantial heaping at multiples of 10 and 5 percent, suggesting that respondents round their reports. This paper studies the nature of rounding by analyzing response patterns across expectations questions and waves of the Health and Retirement Study. We discover a tendency by about half of the respondents to provide more refined responses in the tails of the scale than the center. Only about five percent provide more refined responses in the center than the tails. We find that rounding varies across question domains, which range from personal health to personal finances to macroeconomic events. We develop a two-stage framework to characterize person-specific...