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Improved Methods for Statistical Inference in the Context of Various Types of Parameter Variation
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Improved Methods for Statistical Inference in the Context of Various Types of Parameter Variation. |
| 개인저자 | Casini, Alessandro. |
| 단체저자명 | Boston University. Economics GRS. |
| 발행사항 | [S.l.]: Boston University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 572 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-05A. Dissertation Abstract International |
| ISBN | 9781392599679 |
| 학위논문주기 | Thesis (Ph.D.)--Boston University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-05, Section: A.
Advisor: Perron, Pierre. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | This dissertation addresses various issues related to statistical inference in the context of parameter time-variation. The problem is considered within general regression models as well as in the context of methods for forecast evaluation. The first chapter develops a theory of evolutionary spectra for heteroskedasticity and autocorrelation-robust (HAR) inference when the data may not satisfy secondorder stationarity. We introduce a class of nonstationary stochastic processes that have a time-varying spectral representation and presents a new positive semidefinite heteroskedasticity- and autocorrelation consistent (HAC) estimator. We obtain an optimal HAC estimator under the mean-squared error (MSE) criterion and show its consistency. We propose a data-dependent procedure based on a "plug-in" approach that determines the bandwidth parameters for given kernels and a given sample size. The second chapter develops a continuous record asymptotic framework to build inference methods for the date of a structural change in a linear regression model. We impose very mild regularity conditions on an underlying continuous-time model assumed to generate the data. We consider the least-squares estimate of the break date and establish consistency and convergence rate. We provide a limit theory for shrinking magnitudes of shifts and locally increasing variances. The third chapter develops a novel continuous-time asymptotic framework for inference on whether the predictive ability of a given forecast model remains stable over time. As the sampling interval between observations shrinks to zero the sequence of forecast losses is approximated by a continuous-time stochastic process possessing certain pathwise properties. We consider an hypotheses testing problem based on the local properties of the continuous-time limit counterpart of the sequence of losses. The fourth chapter develops a class of Generalized Laplace (GL) inference methods for the change-point dates in a linear time series regression model with multiple structural changes. The GL estimator is defined by an integration rather than optimization-based method and relies on the least-squares criterion function. On the theoretical side, depending on some smoothing parameter, the class of GL estimators exhibits a dual limiting distribution |
| 일반주제명 | Economics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
