There is growing interest in using structural estimation methods to address economic questions.
There are two main two advantages of using structural estimation methods. First,
they can solve the endogeneity problem confronting numerous reduced-form regression works.
Second, they estimate the deep parameters of the models, which allows researchers to analyze
many interesting economic questions through counterfactual analysis. In this dissertation, I
study three di erent economic questions by structurally estimating models of matching and
In the rst chapter, I develop an estimable model which illustrates that the presence of
moral hazard not only leads to ine ciency caused by risk sharing across rms and CEOs, but
also creates ine ciency due to a talent misallocation. A new empirical method is proposed
to identify the separate surplus of both rms and CEOs in a matching market with moral
hazard. An application of this method to the U.S market for CEOs shows that the aggregate
e ciency loss due to talent misallocation is $12:64 billion. This is more than four times as
large as the loss stemming from risk-sharing between rms and CEOs.
In the second chapter, my coauthor and I propose a new approach to identify models with
network e ects by invoking another side of the market. We show that other side of the market
provides additional information for identi cation. Our running application investigates the
importance of asymmetric information and network e ects in the yellow pages advertising
In the third chapter, I study estimation and non-parametric identi cation of a dynamic
matching model with a broader class of generalized unobserved heterogeneities. I rst provide
the identi cation results on the match surplus. I then show that the match equilibrium exists
and is globally unique. Finally, I provide a new estimation method for our dynamic matching
model, which provides more precise estimates than previous methods.