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Title: Essays on nonparametric econometrics
Author: Lee, Young Jun
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2019
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This dissertation consists of three chapters that focus on the nonparametric method on time-varying parameter models and optimal transport problem. The first chapter, which is jointly authored with Dennis Kristensen, develops a novel asymptotic theory for local polynomial (quasi-) maximum-likelihood estimators of time-varying parameters in a broad class of nonlinear time series models. Under weak regularity conditions, we show the proposed estimators are consistent and follow normal distributions in large samples. We demonstrate the usefulness of our general results by applying our theory to local (quasi-) maximum-likelihood estimators of a time-varying VAR's, ARCH and GARCH, and Poisson autogressions. The second chapter proposes a sieve M-estimation of the solution to the optimal transport problem. Many problems in economics, including matching models and quantile methods, have the structure of an optimal transport problem. The sieve M-estimator is consistent under very little structure on the underlying optimal transport problem being solved. I then derive convergence rates for the estimator and its derivative when the surplus function Φ(X, Y) = X'Y. The derived convergence rates are the same as the optimal rate in the context of regression and density estimations. The results can be extended to the conditional optimal transport problem having the conditional vector quantiles as an application. In the third chapter, I consider the multidimensional matching as one of the primary applications of the optimal transport problem. We employ the sieve simultaneous minimum distance estimation method to estimate the parameters in the equilibrium wage and assignment functions. Our estimation results show that worker-job complementarities in manual skills strongly decreased, whereas complementarities in cognitive skills increased. This phenomenon is consistent with the one of Lindenlaub (2017).
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available