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 到查詢結果 [ null ]

切換: 標籤 | MARC模式 | ISBD

Factor models: Testing and forecasting.

Yao, Jiawei.

FindBook

Google Book

Amazon

博客來

Factor models: Testing and forecasting.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Factor models: Testing and forecasting./
作者:	Yao, Jiawei.
面頁冊數:	114 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-07(E), Section: B.
Contained By:	Dissertation Abstracts International76-07B(E).
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3682786
ISBN:	9781321565713

Factor models: Testing and forecasting.
Yao, Jiawei.

Factor models: Testing and forecasting. - 114 p.

Source: Dissertation Abstracts International, Volume: 76-07(E), Section: B.

Thesis (Ph.D.)--Princeton University, 2015.

This dissertation focuses on two aspects of factor models, testing and forecasting. For testing, we investigate a more general high-dimensional testing problem, with an emphasis on panel data models. Specifically, we propose a novel technique to boost the power of testing a high-dimensional vector against sparse alternatives. Existing tests based on quadratic forms such as the Wald statistic often suffer from low powers, whereas more powerful tests such as thresholding and extreme-value tests require either stringent conditions or bootstrap to derive the null distribution, and often suffer from size distortions. Based on a screening technique, we introduce a ''power enhancement component", which is zero under the null hypothesis with high probability, but diverges quickly under sparse alternatives. The proposed test statistic combines the power enhancement component with an asymptotically pivotal statistic, and strengthens the power under sparse alternatives. As a byproduct, the power enhancement component also consistently identifies the elements that violate the null hypothesis.

ISBN: 9781321565713Subjects--Topical Terms:

517247
Statistics.

Factor models: Testing and forecasting.
LDR:02873nmm a2200301 4500 001 2065640
005 20151205152226.5
008 170521s2015 ||||||||||||||||| ||eng d
020 $a 9781321565713
035 $a (MiAaPQ)AAI3682786
035 $a AAI3682786
040 $a MiAaPQ $c MiAaPQ
100 1 $a Yao, Jiawei. $3 3180349
245 1 0 $a Factor models: Testing and forecasting.
300 $a 114 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-07(E), Section: B.
500 $a Adviser: Jianqing Fan.
502 $a Thesis (Ph.D.)--Princeton University, 2015.
520 $a This dissertation focuses on two aspects of factor models, testing and forecasting. For testing, we investigate a more general high-dimensional testing problem, with an emphasis on panel data models. Specifically, we propose a novel technique to boost the power of testing a high-dimensional vector against sparse alternatives. Existing tests based on quadratic forms such as the Wald statistic often suffer from low powers, whereas more powerful tests such as thresholding and extreme-value tests require either stringent conditions or bootstrap to derive the null distribution, and often suffer from size distortions. Based on a screening technique, we introduce a ''power enhancement component", which is zero under the null hypothesis with high probability, but diverges quickly under sparse alternatives. The proposed test statistic combines the power enhancement component with an asymptotically pivotal statistic, and strengthens the power under sparse alternatives. As a byproduct, the power enhancement component also consistently identifies the elements that violate the null hypothesis.
520 $a Next, we consider forecasting a single time series using many predictors when nonliearity is present. We develop a new methodology called sufficient forecasting, by connecting sliced inverse regression with factor models. The sufficient forecasting correctly estimates projections of the underlying factors and provides multiple predictive indices for further investigation. We derive asymptotic results for the estimate of the central space spanned by these projection directions. Our method allows the number of predictors larger than the sample size, and therefore extends the applicability of inverse regression. Numerical experiments demonstrate that the proposed method improves upon a linear forecasting model. Our results are further illustrated in an empirical study of macroeconomic variables, where sufficient forecasting is found to deliver additional predictive power over conventional methods.
590 $a School code: 0181.
650 4 $a Statistics. $3 517247
650 4 $a Finance. $3 542899
650 4 $a Economics. $3 517137
690 $a 0463
690 $a 0508
690 $a 0501
710 2 $a Princeton University. $b Operations Research and Financial Engineering. $3 2096743
773 0 $t Dissertation Abstracts International $g 76-07B(E).
790 $a 0181
791 $a Ph.D.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3682786