Sample Selection Model with Bootstrap (BPSSM) Approach:

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DC Field	Value	Language
dc.contributor.author	Muhamad Safiih Lola	-
dc.contributor.author	Wan Saliha Wan Alwi	-
dc.contributor.author	Nurul Hila Zainuddin	-
dc.date.accessioned	2017-04-09T08:42:09Z	-
dc.date.available	2017-04-09T08:42:09Z	-
dc.date.issued	2016	-
dc.identifier.uri	http://hdl.handle.net/123456789/5488	-
dc.description.abstract	Heckman Sampel Selection Model (PSSM) has been adopted widely in the study of labour work. This model contains exogenous, endogenous and standard error variables. However, this model is constantly exposed to high inaccuracy of estimation result. Therefore, to obtain an accurate and precise estimation, the bootstrap approach is introduced. The bootstrap approach will be hybrid with PSSM model known as BPSSM to achieve estimation result that is more precise. Then, the BPSSM is applied to Malaysian Population and Family Survey 1994 (MPFS-1994) data. The results showed that BPSSM provide a smaller standard error and shorter confidence intervals.	en_US
dc.language.iso	en	en_US
dc.publisher	Scientific Research Publishing	en_US
dc.subject	Sampel Selection	en_US
dc.subject	Bootstrap	en_US
dc.subject	Standard Error	en_US
dc.subject	Confidence Intervals	en_US
dc.title	Sample Selection Model with Bootstrap (BPSSM) Approach:	en_US
dc.title.alternative	Case Study of the Malaysian Population and Family Survey	en_US
dc.type	Article	en_US
Appears in Collections:	Journal Articles

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