Working Papers
The missing outcome problem is a pervasive problem in economics that arises in many situations
and hinders the researcher's capacity to recover population moments. The literature has focused
on the identifying power of shape restrictions which can be invoked in empirical studies. In this
study, we propose a novel approach of partial identification that does not rely on shape restrictions
but exploits variation in the sample of University of Michigan Index of Consumer Sentiment that
resulted from the substitution of landlines with cellphones in telephone surveys. We provide conditions
under which the bounds are improved and extend our approach to treatment effects literature.
Networks with missing links can arise due to boundary specification, non-response in surveys,
and fixed choice survey design. We model the link formation of a friendship network with missing
links arising from non-response in surveys and fixed choice problem and construct sharp upper bounds
on centrality measures. We provide identification conditions to overcome degree heterogeneity.
Finally, we apply our proposed approach to study a friendship network in North Carolina.
with Hector H. Sandoval · Under Review
Undercoverage occurs when population members do not appear in the sample frame. For instance,
the substitution of landlines with cellphones that took place in the last two decades increased
the undercovered population in telephone surveys because survey practices tended to exclude cellphones.
This paper shows how to construct an identification region à la Manski to assess the extent of the
undercoverage problem for a population mean and for the coverage error considered in survey research
literature. We illustrate the approach using two indices of consumer confidence during 2003–2018.
with Enrijeta Shino
Empirical models find a positive effect of political knowledge on turnout, however this may not
reflect the true causal effect. Using survey data from the 2016 American National Election Studies,
we employ a nonparametric bounding method to overcome this identification challenge. This method
relies on weak and credible assumptions to partially identify the average treatment effect of
political knowledge on turnout.
Linear-in-means models, first proposed by Manski (1993), are used in modelling social interactions.
Manski shows that identification of the endogenous, contextual, and correlated effects is not possible
when we condition on individual member characteristics to identify the groups. Different from Manski
we use a nonlinear linear-in-means model to identify the main effects under several very mild conditions
and estimate our model using a GMM approach.
