| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000433397 |
| 005 | | 20200225141104 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781392599679 |
| 035 | |
▼a (MiAaPQ)AAI13812887 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 330 |
| 100 | 1 |
▼a Casini, Alessandro. |
| 245 | 10 |
▼a Improved Methods for Statistical Inference in the Context of Various Types of Parameter Variation. |
| 260 | |
▼a [S.l.]:
▼b Boston University.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 572 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-05, Section: A. |
| 500 | |
▼a Advisor: Perron, Pierre. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Boston University, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0017. |
| 650 | 4 |
▼a Economics. |
| 690 | |
▼a 0501 |
| 710 | 20 |
▼a Boston University.
▼b Economics GRS. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-05A. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0017 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15490762
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1816162 |
| 991 | |
▼a E-BOOK |