Semiparametric inference for non-LAN models

Bo Zhou

Research output: ThesisDoctoral ThesisScientific

192 Downloads (Pure)

Abstract

This thesis consists of three essays in theory of econometrics and statistics, focusing on the issue of semiparametric efficiency in non-LAN (Locally Asymptotically Normality) models. The first essay starts with a univariate case of the unit root testing problem, of which the limit experiment is of the LABF (Locally Asymptotically Brownian Functional) model. A novel approach is designed for developing the semiparametric power envelope and a family of rank-based tests that are semiparametrically efficient is proposed. The second essay generalizes the approach to all LAQ (Locally Asymptotically Quadratic) models. Moreover, it expands the rank statistics in a unique way from the univariate case to the multivariate case. Using these results, in the third essay, the semiparametric power envelop of all invariant tests for stock return predictability is developed. And subsequently, a new family of tests that are more efficient than the existing ones is proposed.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Tilburg University
Supervisors/Advisors
  • Werker, Bas, Promotor
  • van den Akker, Ramon, Co-promotor
Award date6 Dec 2017
Place of PublicationTilburg
Publisher
Print ISBNs978 90 5668 537 9
Publication statusPublished - 2017

Fingerprint

Semiparametric inference
Statistics
Normality
Stock return predictability
Econometrics
Power envelope
Unit root testing
Experiment
Semiparametric efficiency

Cite this

Zhou, B. (2017). Semiparametric inference for non-LAN models. Tilburg: CentER, Center for Economic Research.
Zhou, Bo. / Semiparametric inference for non-LAN models. Tilburg : CentER, Center for Economic Research, 2017. 187 p.
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Zhou, B 2017, 'Semiparametric inference for non-LAN models', Doctor of Philosophy, Tilburg University, Tilburg.

Semiparametric inference for non-LAN models. / Zhou, Bo.

Tilburg : CentER, Center for Economic Research, 2017. 187 p.

Research output: ThesisDoctoral ThesisScientific

TY - THES

T1 - Semiparametric inference for non-LAN models

AU - Zhou, Bo

PY - 2017

Y1 - 2017

N2 - This thesis consists of three essays in theory of econometrics and statistics, focusing on the issue of semiparametric efficiency in non-LAN (Locally Asymptotically Normality) models. The first essay starts with a univariate case of the unit root testing problem, of which the limit experiment is of the LABF (Locally Asymptotically Brownian Functional) model. A novel approach is designed for developing the semiparametric power envelope and a family of rank-based tests that are semiparametrically efficient is proposed. The second essay generalizes the approach to all LAQ (Locally Asymptotically Quadratic) models. Moreover, it expands the rank statistics in a unique way from the univariate case to the multivariate case. Using these results, in the third essay, the semiparametric power envelop of all invariant tests for stock return predictability is developed. And subsequently, a new family of tests that are more efficient than the existing ones is proposed.

AB - This thesis consists of three essays in theory of econometrics and statistics, focusing on the issue of semiparametric efficiency in non-LAN (Locally Asymptotically Normality) models. The first essay starts with a univariate case of the unit root testing problem, of which the limit experiment is of the LABF (Locally Asymptotically Brownian Functional) model. A novel approach is designed for developing the semiparametric power envelope and a family of rank-based tests that are semiparametrically efficient is proposed. The second essay generalizes the approach to all LAQ (Locally Asymptotically Quadratic) models. Moreover, it expands the rank statistics in a unique way from the univariate case to the multivariate case. Using these results, in the third essay, the semiparametric power envelop of all invariant tests for stock return predictability is developed. And subsequently, a new family of tests that are more efficient than the existing ones is proposed.

M3 - Doctoral Thesis

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Zhou B. Semiparametric inference for non-LAN models. Tilburg: CentER, Center for Economic Research, 2017. 187 p. (CentER Dissertation Series).