Department of Economics
Institutional Affiliation: Cornell University
Information about this author at RePEc
NBER Working Papers and Publications
|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...