東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Keeping variables within bounds: Usi...

Morin, Lealand.

FindBook

Google Book

Amazon

博客來

Keeping variables within bounds: Using information between observations.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Keeping variables within bounds: Using information between observations./
作者:	Morin, Lealand.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
面頁冊數:	202 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-01C.
Contained By:	Dissertation Abstracts International75-01C.
標題:	Economic theory. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10588458

Keeping variables within bounds: Using information between observations.
Morin, Lealand.

Keeping variables within bounds: Using information between observations. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 202 p.

Source: Dissertation Abstracts International, Volume: 75-01C.

Thesis (Ph.D.)--Queen's University (Canada), 2016.

This research develops an econometric framework to analyze time series processes with bounds. The framework is general enough that it can incorporate several different kinds of bounding information that constrain continuous-time stochastic processes between discretely-sampled observations. It applies to situations in which the process is known to remain within an interval between observations, by way of either a known constraint or through the observation of extreme realizations of the process. The main statistical technique employs the theory of maximum likelihood estimation. This approach leads to the development of the asymptotic distribution theory for the estimation of the parameters in bounded diffusion models. The results of this analysis present several implications for empirical research. The advantages are realized in the form of efficiency gains, bias reduction and in the flexibility of model specification. A bias arises in the presence of bounding information that is ignored, while it is mitigated within this framework. An efficiency gain arises, in the sense that the statistical methods make use of conditioning information, as revealed by the bounds. Further, the specification of an econometric model can be uncoupled from the restriction to the bounds, leaving the researcher free to model the process near the bound in a way that avoids bias from misspecification. One byproduct of the improvements in model specification is that the more precise model estimation exposes other sources of misspecification. Some processes reveal themselves to be unlikely candidates for a given diffusion model, once the observations are analyzed in combination with the bounding information. A closer inspection of the theoretical foundation behind diffusion models leads to a more general specification of the model. This approach is used to produce a set of algorithms to make the model computationally feasible and more widely applicable. Finally, the modeling framework is applied to a series of interest rates, which, for several years, have been constrained by the lower bound of zero. The estimates from a series of diffusion models suggest a substantial difference in estimation results between models that ignore bounds and the framework that takes bounding information into consideration.Subjects--Topical Terms:

1556984
Economic theory.

Keeping variables within bounds: Using information between observations.
LDR:03117nmm a2200253 4500 001 2117439
005 20170516070348.5
008 180830s2016 ||||||||||||||||| ||eng d
035 $a (MiAaPQ)AAI10588458
035 $a AAI10588458
040 $a MiAaPQ $c MiAaPQ
100 1 $a Morin, Lealand. $3 3279208
245 1 0 $a Keeping variables within bounds: Using information between observations.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 202 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-01C.
502 $a Thesis (Ph.D.)--Queen's University (Canada), 2016.
520 $a This research develops an econometric framework to analyze time series processes with bounds. The framework is general enough that it can incorporate several different kinds of bounding information that constrain continuous-time stochastic processes between discretely-sampled observations. It applies to situations in which the process is known to remain within an interval between observations, by way of either a known constraint or through the observation of extreme realizations of the process. The main statistical technique employs the theory of maximum likelihood estimation. This approach leads to the development of the asymptotic distribution theory for the estimation of the parameters in bounded diffusion models. The results of this analysis present several implications for empirical research. The advantages are realized in the form of efficiency gains, bias reduction and in the flexibility of model specification. A bias arises in the presence of bounding information that is ignored, while it is mitigated within this framework. An efficiency gain arises, in the sense that the statistical methods make use of conditioning information, as revealed by the bounds. Further, the specification of an econometric model can be uncoupled from the restriction to the bounds, leaving the researcher free to model the process near the bound in a way that avoids bias from misspecification. One byproduct of the improvements in model specification is that the more precise model estimation exposes other sources of misspecification. Some processes reveal themselves to be unlikely candidates for a given diffusion model, once the observations are analyzed in combination with the bounding information. A closer inspection of the theoretical foundation behind diffusion models leads to a more general specification of the model. This approach is used to produce a set of algorithms to make the model computationally feasible and more widely applicable. Finally, the modeling framework is applied to a series of interest rates, which, for several years, have been constrained by the lower bound of zero. The estimates from a series of diffusion models suggest a substantial difference in estimation results between models that ignore bounds and the framework that takes bounding information into consideration.
590 $a School code: 0283.
650 4 $a Economic theory. $3 1556984
690 $a 0511
710 2 $a Queen's University (Canada). $3 1017786
773 0 $t Dissertation Abstracts International $g 75-01C.
790 $a 0283
791 $a Ph.D.
792 $a 2016
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10588458