Robust diagnostic and robust estimation methods for fixed effect panel data model in presence of high leverage points and multicollinearity
The Diagnostic Robust Generalized Potential based on Minimum Volume Ellipsoid (MVE) is proposed in linear regression to detect high leverage points (HLPs). However, it takes a very long computational running time and also has small rate of swamping and masking effects. Hence the Improvised Diagno...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/68632/1/FS%202018%201%20-%20IR.pdf |
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| Summary: | The Diagnostic Robust Generalized Potential based on Minimum Volume Ellipsoid
(MVE) is proposed in linear regression to detect high leverage points (HLPs).
However, it takes a very long computational running time and also has small rate of
swamping and masking effects. Hence the Improvised Diagnostic Robust Generalized
Potential based on Index Set Equality (IDRGP (ISE)) is proposed to linear and fixed
effect panel data model. The results indicate that IDRGP(ISE) successfully identify
high leverage points with the reduction in the rate of swamping and masking effects
and has less computational running time.
To date no research has been done to identify HLPs for panel data. Hence, to close the
gap in the literature we propose Within Group Improvised Diagnostic Robust
Generalized Potential (WIDRGP). It is very successful in detecting HLPs and
relatively fast to compute.
The Generalized M-estimator (GM6) is the widely used method to overcome the
problem of HLPs for multiple linear regression model. However, this method is less
efficient since it is based on Robust Mahalanobis Distance RMD- MVE as an initial
π–weight function. Its efficiency decreases as the number of good leverage points
increases. Hence, the Generalized M-estimator (GM) based on Fast Improvised
Generalized Studentized Residuals (FIMGT), denoted as (GM-FIMGT) is developed.
The results show that the GM-FIMGT is highly efficient and relatively fast. A robust
Within Group GM estimator based on FIMGT estimator (WGM-FIMGT) for fixed
effect panel data model is proposed. The findings indicate that the WGM-FIMGT is
very efficient compared to the existing estimators.
Thus far, no research has been done on the detection of multicollinearity for fixed
effect panel data models in the presence of HLPs. Hence, Robust Variance Inflation
Factor based on GM-FIMGT (RVIF(GM-FIMGT)) is formulated. The results of the
study show that it is very effective in detecting multicollinearity in the presence of
HLPs.
The Jackknife ridge regression is one of the commonly used method to remedy the
problem of multicollinearity. Nonetheless, it is very sensitive to outliers and HLPs.
Hence Robust Jackknife ridge regression based on FIMGT (RJFIMGT) is developed
to rectify the combined problem of multicollinearity and high leverage points. The
results of the study indicate that the RJFIMGT is the most efficient method when
multicollinearity problem come together with the presence of HLPs.
Still no research has been done on the parameter estimation of fixed effect panel data
model in the presence of multicollinearity and HLPs. Thus the within Group Robust
Jackknife ridge regression based on FIMGT (WRJFIMGT) is developed to close the
gap in the literature. The findings signify that WRJFIMGT provides the best
estimates when multicollinearity and HLPs are present in a data set |
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