Robust cross sectional dependence test in panel regression model
The central focus in most recent studies on panel data is on the issue of cross sectional dependence; there exist correlations between different groups (cross section) of innovations in the panel. In the presence of cross sectional dependence, we can no longer use the general assumption of independ...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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2009
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/10779/1/robust_cross_sectional.pdf |
| _version_ | 1796946128294379520 |
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| author | Md. Shariff, N.S. Baharumshah, A.Z. |
| author_facet | Md. Shariff, N.S. Baharumshah, A.Z. |
| author_sort | Md. Shariff, N.S. |
| collection | UM |
| description | The central focus in most recent studies on panel data is on the issue of cross sectional dependence; there exist correlations between different groups (cross section) of
innovations in the panel. In the presence of cross sectional dependence, we can no longer use the general assumption of independence across units for disturbances. Thus, it is
important to test for cross sectional dependence before modeling and estimating panel data takes place in order to avoid the misspecification of the model which subsequently
results in invalid tests, bias, inconsistency and inefficiency in parameter estimates. Difficulty arises when (i) disturbances are correlated across cross section; (ii) data are subjected to outliersl shocks. It has been reported that the main possible causes are global shocks and unobserved factors that affect the cross sectional dependence (see Cerrato (200)). However, in reality, it is also possible to have local shocks which affect innovations in the model. The question that usually arises is whether cross dependencies does exist among innovations in the presence of outliers. The usual tests based on Breusch and Pagan (1980) and Pesaran (2004) uses residuals obtained from Ordinary Least Square Fit (OLS) which are subject to influence of outliers. This paper propose robust cross dependency tests of Breusch and Pagan (1980) and Pesaran (2004) to aid cross sectional dependence test in the presence of outliers. Here, robust regression (RREG) model is employed to capture the outliers' effects and filter outlying observation by down weighting the spurious data.
In this paper, we compare the performances of the Breusch and Pagan (1980), Pesaran (2004) tests as well as the proposed methods under varying degree of cross section dependencies via simulation studies. Our resJb have shown that robust approaches are able to produce reliable test of cross sectional dependency under the conditions considered.
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| first_indexed | 2024-03-06T05:26:59Z |
| format | Conference or Workshop Item |
| id | um.eprints-10779 |
| institution | Universiti Malaya |
| language | English |
| last_indexed | 2024-03-06T05:26:59Z |
| publishDate | 2009 |
| record_format | dspace |
| spelling | um.eprints-107792014-07-21T00:46:41Z http://eprints.um.edu.my/10779/ Robust cross sectional dependence test in panel regression model Md. Shariff, N.S. Baharumshah, A.Z. QA Mathematics The central focus in most recent studies on panel data is on the issue of cross sectional dependence; there exist correlations between different groups (cross section) of innovations in the panel. In the presence of cross sectional dependence, we can no longer use the general assumption of independence across units for disturbances. Thus, it is important to test for cross sectional dependence before modeling and estimating panel data takes place in order to avoid the misspecification of the model which subsequently results in invalid tests, bias, inconsistency and inefficiency in parameter estimates. Difficulty arises when (i) disturbances are correlated across cross section; (ii) data are subjected to outliersl shocks. It has been reported that the main possible causes are global shocks and unobserved factors that affect the cross sectional dependence (see Cerrato (200)). However, in reality, it is also possible to have local shocks which affect innovations in the model. The question that usually arises is whether cross dependencies does exist among innovations in the presence of outliers. The usual tests based on Breusch and Pagan (1980) and Pesaran (2004) uses residuals obtained from Ordinary Least Square Fit (OLS) which are subject to influence of outliers. This paper propose robust cross dependency tests of Breusch and Pagan (1980) and Pesaran (2004) to aid cross sectional dependence test in the presence of outliers. Here, robust regression (RREG) model is employed to capture the outliers' effects and filter outlying observation by down weighting the spurious data. In this paper, we compare the performances of the Breusch and Pagan (1980), Pesaran (2004) tests as well as the proposed methods under varying degree of cross section dependencies via simulation studies. Our resJb have shown that robust approaches are able to produce reliable test of cross sectional dependency under the conditions considered. 2009-06 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.um.edu.my/10779/1/robust_cross_sectional.pdf Md. Shariff, N.S. and Baharumshah, A.Z. (2009) Robust cross sectional dependence test in panel regression model. In: International Conference on Robust Statistics, 14-19 Jun 2009, Parma, Italy. (Submitted) |
| spellingShingle | QA Mathematics Md. Shariff, N.S. Baharumshah, A.Z. Robust cross sectional dependence test in panel regression model |
| title | Robust cross sectional dependence test in panel regression model |
| title_full | Robust cross sectional dependence test in panel regression model |
| title_fullStr | Robust cross sectional dependence test in panel regression model |
| title_full_unstemmed | Robust cross sectional dependence test in panel regression model |
| title_short | Robust cross sectional dependence test in panel regression model |
| title_sort | robust cross sectional dependence test in panel regression model |
| topic | QA Mathematics |
| url | http://eprints.um.edu.my/10779/1/robust_cross_sectional.pdf |
| work_keys_str_mv | AT mdshariffns robustcrosssectionaldependencetestinpanelregressionmodel AT baharumshahaz robustcrosssectionaldependencetestinpanelregressionmodel |