Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/74852
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chen, Jia | en |
dc.contributor.author | Gao, Jiti | en |
dc.contributor.author | Li, Degui | en |
dc.date.issued | 2012 | en |
dc.identifier.citation | Econometric Theory, 2012; 28(5):1144-1163 | en |
dc.identifier.issn | 0266-4666 | en |
dc.identifier.uri | http://hdl.handle.net/2440/74852 | - |
dc.description.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. | en |
dc.description.statementofresponsibility | Jia Chen And Jiti Gao And Degui Li | en |
dc.language.iso | en | en |
dc.publisher | Cambridge University Press | en |
dc.rights | © Cambridge University Press 2012 | en |
dc.title | A new diagnostic test for cross-section uncorrelatedness in non parametric panel data models | en |
dc.type | Journal article | en |
dc.contributor.school | School of Economics | en |
dc.identifier.doi | 10.1017/S0266466612000072 | en |
Appears in Collections: | Economics publications |
Files in This Item:
File | Size | Format | |
---|---|---|---|
hdl_74852.pdf | 152.73 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.