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A NEW DIAGNOSTIC TEST FOR CROSS-SECTION UNCORRELATEDNESS IN NON PARAMETRIC PANEL DATA MODELS

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A NEW DIAGNOSTIC TEST FOR CROSS-SECTION UNCORRELATEDNESS IN NON PARAMETRIC PANEL DATA MODELS. / Chen, Jia; Gao, Jiti; Li, Degui.

In: Econometric Theory, Vol. 28, No. 5, 10.2012, p. 1144-1163.

Research output: Contribution to journalArticle

Harvard

Chen, J, Gao, J & Li, D 2012, 'A NEW DIAGNOSTIC TEST FOR CROSS-SECTION UNCORRELATEDNESS IN NON PARAMETRIC PANEL DATA MODELS', Econometric Theory, vol. 28, no. 5, pp. 1144-1163. https://doi.org/10.1017/S0266466612000072

APA

Chen, J., Gao, J., & Li, D. (2012). A NEW DIAGNOSTIC TEST FOR CROSS-SECTION UNCORRELATEDNESS IN NON PARAMETRIC PANEL DATA MODELS. Econometric Theory, 28(5), 1144-1163. https://doi.org/10.1017/S0266466612000072

Vancouver

Chen J, Gao J, Li D. A NEW DIAGNOSTIC TEST FOR CROSS-SECTION UNCORRELATEDNESS IN NON PARAMETRIC PANEL DATA MODELS. Econometric Theory. 2012 Oct;28(5):1144-1163. https://doi.org/10.1017/S0266466612000072

Author

Chen, Jia ; Gao, Jiti ; Li, Degui. / A NEW DIAGNOSTIC TEST FOR CROSS-SECTION UNCORRELATEDNESS IN NON PARAMETRIC PANEL DATA MODELS. In: Econometric Theory. 2012 ; Vol. 28, No. 5. pp. 1144-1163.

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@article{38cb12fe301643df8de49a328b0a4438,
title = "A NEW DIAGNOSTIC TEST FOR CROSS-SECTION UNCORRELATEDNESS IN NON PARAMETRIC PANEL DATA MODELS",
abstract = "In this paper, we propose a new diagnostic test for residual cross-section uncorrelatedness (CU) in a nonparametric panel data model. The proposed nonparametric CU test is a nonparametric counterpart of an existing parametric cross-section dependence test proposed in Pesaran (2004, Cambridge Working paper in Economics 0435). Without assuming cross-section independence, we establish asymptotic distribution for the proposed test statistic for the case. where both the cross-section dimension and the time dimension go to infinity simultaneously, and then analyze the power function of the proposed test under a sequence of local alternatives that involve a nonlinear multifactor model. The simulation results and real data analysis show that the nonparametric CU test associated with an asymptotic critical value works well.",
keywords = "INDEPENDENCE",
author = "Jia Chen and Jiti Gao and Degui Li",
year = "2012",
month = "10",
doi = "10.1017/S0266466612000072",
language = "English",
volume = "28",
pages = "1144--1163",
journal = "Econometric Theory",
issn = "0266-4666",
publisher = "Cambridge University Press",
number = "5",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - A NEW DIAGNOSTIC TEST FOR CROSS-SECTION UNCORRELATEDNESS IN NON PARAMETRIC PANEL DATA MODELS

AU - Chen, Jia

AU - Gao, Jiti

AU - Li, Degui

PY - 2012/10

Y1 - 2012/10

N2 - In this paper, we propose a new diagnostic test for residual cross-section uncorrelatedness (CU) in a nonparametric panel data model. The proposed nonparametric CU test is a nonparametric counterpart of an existing parametric cross-section dependence test proposed in Pesaran (2004, Cambridge Working paper in Economics 0435). Without assuming cross-section independence, we establish asymptotic distribution for the proposed test statistic for the case. where both the cross-section dimension and the time dimension go to infinity simultaneously, and then analyze the power function of the proposed test under a sequence of local alternatives that involve a nonlinear multifactor model. The simulation results and real data analysis show that the nonparametric CU test associated with an asymptotic critical value works well.

AB - In this paper, we propose a new diagnostic test for residual cross-section uncorrelatedness (CU) in a nonparametric panel data model. The proposed nonparametric CU test is a nonparametric counterpart of an existing parametric cross-section dependence test proposed in Pesaran (2004, Cambridge Working paper in Economics 0435). Without assuming cross-section independence, we establish asymptotic distribution for the proposed test statistic for the case. where both the cross-section dimension and the time dimension go to infinity simultaneously, and then analyze the power function of the proposed test under a sequence of local alternatives that involve a nonlinear multifactor model. The simulation results and real data analysis show that the nonparametric CU test associated with an asymptotic critical value works well.

KW - INDEPENDENCE

UR - http://www.scopus.com/inward/record.url?scp=84865774441&partnerID=8YFLogxK

U2 - 10.1017/S0266466612000072

DO - 10.1017/S0266466612000072

M3 - Article

VL - 28

SP - 1144

EP - 1163

JO - Econometric Theory

JF - Econometric Theory

SN - 0266-4666

IS - 5

ER -