國立虎尾科技大學 |

Interval Estimation for Semiparametric Predictive Regression.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Interval Estimation for Semiparametric Predictive Regression./
作者:	Hong, Shaoxin.
面頁冊數:	1 online resource (66 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
Contained By:	Dissertation Abstracts International79-08B(E).
標題:	Statistics. -
電子資源:	click for full text (PQDT)
ISBN:	9780355795172

Interval Estimation for Semiparametric Predictive Regression.
Hong, Shaoxin.

Interval Estimation for Semiparametric Predictive Regression. - 1 online resource (66 pages)

Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.

Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2018.

Includes bibliographical references

Predictive regression is an important research topic in financial econometrics. Various estimation methods have been proposed for it, but they suffer from complicated asymptotic limits which depend on whether or not the predicting variable is stationary. This makes inference for the predictability difficult. In this paper we employ a nonlinear projection to deal with endogeneity of the state variable which results in a new semiparametric predictive regression model for describing the relationship between the state variables and the asset return. We propose a weighted profile estimation equation method to estimate the parameters and an empirical likelihood ratio test to examine the predictability of state variables. We establish the asymptotic normality of the proposed estimator and show the Wilks theorem holds for the test statistic regardless of predicting variables being stationary or not. This provides a unifying method for constructing confidence regions of the coefficients of state variables. Simulations demonstrate favorable finite sample performance of the proposed method over some existing approaches. Real examples illustrate the value of our methodology.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355795172Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

Interval Estimation for Semiparametric Predictive Regression.
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500 $a Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
500 $a Adviser: Jiancheng Jiang.
502 $a Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2018.
504 $a Includes bibliographical references
520 $a Predictive regression is an important research topic in financial econometrics. Various estimation methods have been proposed for it, but they suffer from complicated asymptotic limits which depend on whether or not the predicting variable is stationary. This makes inference for the predictability difficult. In this paper we employ a nonlinear projection to deal with endogeneity of the state variable which results in a new semiparametric predictive regression model for describing the relationship between the state variables and the asset return. We propose a weighted profile estimation equation method to estimate the parameters and an empirical likelihood ratio test to examine the predictability of state variables. We establish the asymptotic normality of the proposed estimator and show the Wilks theorem holds for the test statistic regardless of predicting variables being stationary or not. This provides a unifying method for constructing confidence regions of the coefficients of state variables. Simulations demonstrate favorable finite sample performance of the proposed method over some existing approaches. Real examples illustrate the value of our methodology.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
655 7 $a Electronic books. $2 local $3 554714
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of North Carolina at Charlotte. $b Mathematics. $3 1195576
773 0 $t Dissertation Abstracts International $g 79-08B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10746967 $z click for full text (PQDT)