Nonparametric Estimation and Inference in Econometrics
Abstract
This dissertation includes two essays: The first one is on nonparametric inference in causal
effect models, and the second one is on nonparametric estimation in financial economics.
In the first essay, we propose a nonparametric test for unobserved heterogeneous treatment
effects in a general framework, allowing for self-selection to the treatment. The proposed modified
Kolmogorov-Smirnov-type test is consistent and simple to implement. Monte Carlo simulations
show that our test performs well in finite samples. For illustration, we apply our test to study
heterogeneous treatment effects of the Job Training Partnership Act on earnings and the impacts
of fertility on family income.
In the second essay, we provide an alternative to the existing estimations of implied volatility
in option pricing. The use of state price densities to gather information about market sentiment
and other empirical characteristics that describe important phenomena is popular in literature and
in practice. The estimation of the implied volatility surface to extract these densities is a crucial
intermediate step in the process, and the methods to do so are varied in literature. This essay
proposes an estimation procedure that is relative new in nonparametric literature: `1 trend filtering.
We show its advantages over typically used nonparametric and parametric methods, commonly
used in literature and in practice, to deal with this particular estimation problem. Additionally, the
method maintains smaller prediction errors than the comparison models across different number
of observations and levels of noise.
Subject
Specification testNonseparability
Unobserved heterogeneous treatment effects
State price density
Implied volatility
Nonparametric regression
Trend filtering
LASSO
Citation
Huang, Ta-Cheng (2018). Nonparametric Estimation and Inference in Econometrics. Doctoral dissertation, Texas A & M University. Available electronically from https : / /hdl .handle .net /1969 .1 /173461.