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Modelling Local Polynomial for longitudinal data a case study: Inflation sectors in Indonesia


Article Information

Title: Modelling Local Polynomial for longitudinal data a case study: Inflation sectors in Indonesia

Authors: Suparti, Alan Prahutama, Rita Rahmawati, Tiani W. Utami

Journal: ARPN Journal of Engineering and Applied Sciences

HEC Recognition History
Category From To
Y 2023-07-01 2024-09-30
Y 2022-07-01 2023-06-30
Y 2021-07-01 2022-06-30
X 2020-07-01 2021-06-30

Publisher: Khyber Medical College, Peshawar

Country: Pakistan

Year: 2016

Volume: 11

Issue: 23

Language: English

Categories

Abstract

Regression analysis is one of statistical methods for modelling the relation between response variable and predictors variable. Analysis of regression approach was doing three ways such as parametric regression, nonparametric regression and semi parametric regression. One of nonparametric regression methods is Local Polynomial, which is it need kernel function to modelling. Longitudinal data is the data that combine time series and cross sectional data. Nowadays, we can developed modelling longitudinal data used local polynomial. The first steps to modelling it, we should find optimum bandwidth. One of method to find optimum bandwidth is use Generalized Cross Validation (GCV) method. The optimum bandwidth is smallest GCV’s value. In this paper, we modelling seven inflation sector in Indonesia. The sectors are (1) foodstuffs; (2) food, beverages, cigarettes and tobacco; (3) housing, water, electricity, gas and fuel; (4) clothing; (5) health sector; (6) education and sport sector; and (7) transportation, communication and financial services. We used Gaussian kernel function as weighted. The results of this research produce R-square 82.01%.


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