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: Two Monte Carlo studies on the estimation of panel data econometrics
  name:
    name part: Chang, Young-Jae.
    role:
      role term: creator
  name:
    name part: Baltagi, Badi H.
    role:
      role term: degree supervisor.
  name:
    name part: Hocking, Ronald R.
    role:
      role term: degree committee member.
  name:
    name part: Neilson, William S.
    role:
      role term: degree committee member.
  name:
    name part: Pierce, Brooks
    role:
      role term: degree committee member.
  name:
    name part: Texas A & M University
    role:
      role term: degree granting institution.
  type of resource: text
  genre: bibliography
  genre: Academic theses
  origin info:
    place:
      place term: xx
    date issued: 1991
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    extent: x, 113 leaves ; 29 cm
  abstract: This dissertation studies the estimation problem of panel data via Monte Carlo experiments. The chapter of the AR(1) processes with an arbitrary assumption on the initial observations highlights one advantage of using panel data. It is important to allow an arbitrary variance on the initial observations because the assumption of stationarity is invalid for many economic time series. A panel data framework is an ideal setting for this analysis because there exist N initial observations which enable us to estimate a new parameter. The mle's are derived under both the stationarity and arbitrary variance assumptions (GPW) following Anderson and Hsiao (1982). OLS, iterative Cochrane-Orcutt (1949) (ICO), Beach and MacKinnon (1978) (BM), GPW and pretest (PRE) estimators are compared. OLS performs poorly for all positive p values due to the presence of both serial correlation and heteroskedasticity. ICO compares well with GPW in the estimation of p, while it performs poorly in the estimation of β relative to the other estimators. The performance of BM deteriorates as the process departs from the stationary case. In contrast, GPW and PRE perform well relative to true GLS. In the chapter on the comparison of estimation methods for an unbalanced one way regression model, four ANOVA, ML, REML and MINQUE (MQI, MQA) estimators are compared. Four ANOVA type, ML type and MQA estimators perform well relative to true GLS in the estimation of β. MQI performs poorly when γ is large and the pattern is severely unbalanced. The performance of OLS deteriorates as γ increases while WITHIN performs poorly only when γ is small. In the estimation of variance components, ANOVA type and MQI perform poorly relative to ML, REML and MQA. However, only the poor performance of MQI for large p and severely unbalanced patterns seems to be translated into the poor performance in the β estimation. Unless the pattern is severely unbalanced and γ is large, the simple ANOVA type estimators can be used. For …
  note: Typescript (photocopy).
  note: Vita.
  note: "Major subject: Economics."
  note: Ph. D. Texas A & M University 1991
  note: Includes bibliographical references.
  subject:
    topic: Econometrics
  subject:
    topic: Panel analysis
  subject:
    topic: Monte Carlo method
  subject:
    topic: Major economics
  identifier: http://proxy.library.tamu.edu/login
  related item:
    location:
      url: http://proxy.library.tamu.edu/login
  record info:
    record content source: TXA
    record creation date: 920923
    record change date: 20200904152543.0
    record identifier: in00001074897
    language of cataloging:
      language term: eng