Abstract

Ordinary least square (OLS) method is the most popular and commonly technique used to estimate numerical values of parameters of selected regression models. This was due to its unbiasedness property. However, when elements of dependent variable have unequal variances and/or correlated each other, there is no guarantee that the OLS estimator will show the most efficient within the class of linear unbiased estimators. For conditions generally encountered, GLS method is proposed an estimation procedure which yields coefficient estimators at least asymptotically more efficient than single equation OLS estimator. This method is derived by Aitken and it’s named Aitken GLS. This paper reported a study of application of Aitken’s GLS method for estimating parameter of demand function of animal protein in Indonesia of which have a system equation. This system of equation causes violation of the assumptions of homoscedasticity and independency of estimated parameters. Secondary data obtained from the Central Bureau of Statistics 2016 in 34 provinces in Indonesia was used in this study. Animal food was grouped in terms of fish, meat, eggs and milk. Results showed that error of those three equations were correlated. It suggests that GLS method should be used. Normality and homogenity assumptions were not violated. The determination coefficient was 99%, indicating that the method was very good.

Details

Title
Aitken’s Generalized Least Square Method for Estimating Parameter of Demand Function of Animal Protein In Indonesia
Author
Virgantari, Fitria 1 ; Hagni Wijayanti 1 ; Koeshendrajana, Sonny 2 

 Departement of Mathematics, Pakuan University 
 Agency for Research and Manpower Resource of the Marine Affairs and Fisheries 
Publication year
2019
Publication date
Aug 2019
Publisher
IOP Publishing
ISSN
17426588
e-ISSN
17426596
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2566240436
Copyright
© 2019. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.