rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Three Essays on Semiparametric Econometrics
    sub title: Theory and Application
  name:
    name part: Li, Hongjun
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Li, Qi
    role:
      role term: thesis advisor.
  name:
    name part: Huang, Jianhua
    role:
      role term: thesis advisor.
  name:
    name part: Texas A & M University
    role:
      role term: degree granting institution.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2015
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: This dissertation aims at investigating the theory and application of semiparametric econometrics. I first inspect the selection of optimal bandwidth using the cross-validation method for the kernel estimation of cumulative distribution/survivor functions. Then, I analyze the determination of the number of factors with the methods of principal component and information criteria. I also show the application of semiparametric methods to "purchasing power parity" puzzle. Firstly, I propose a data-driven least squares cross-validation method to optimally select smoothing parameters for the nonparametric estimation of cumulative distribution/ survivor functions. The general multivariate covariates can be continuous, discrete/ordered categorical or a mix of either. I establish the asymptotic optimality of least squares cross-validation method. Also, I show that the estimators of cumulative distribution/survivor functions using the smoothing parameters selected by the proposed method is asymptotically normally distributed. Monte Carlo simulation verifies the finite-sample properties of the least squares cross-validation method. Secondly, I provide some discussions on the econometric theory for factor models of large dimensions where the number of factors (r) is allowed to increase as the two dimensions, cross-sections (N) and time dimensions (T) increase. I mainly focus on the determination of the number of factors. I extend the existing panel criteria to high dimension case where r may be increasing with N or T. I show that the number of factors can be consistently estimated using the criteria. Also, Monte-Carlo simulation demonstrates the finite sample properties of the proposed estimating method. Lastly, I consider an empirical application of semiparametric econometrics to the problem of purchasing power parity (hereafter PPP) hypothesis test. Traditional linear cointegration tests of PPP hypothesis often lead to rejection of the PPP hypothesis. More recent studies allo…
  note: by Hongjun Li.
  note: "Major Subject: Economics"
  note: Includes vita.
  note: Doctor of Philosophy Texas A & M University 2014 http://hdl.handle.net/1969.1/152605
  note: Includes bibliographical references.
  note: Text (Dissertation)
  subject:
    topic: Major Economics
  subject:
    topic: Semiparametric
  subject:
    topic: Cross-validation
  subject:
    topic: Bandwidth selection
  subject:
    topic: Factor analysis
  subject:
    topic: Number of factors
  subject:
    topic: Purchasing power parity
  subject:
    topic: Varying coefficient models
  identifier: http://hdl.handle.net/1969.1/152605
  record info:
    record content source: TXA
    record creation date: 150206
    record change date: 20151206125727.0
    record identifier: in00003489417
    language of cataloging:
      language term: eng