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All Journal Jurnal Ilmu Dasar Jurnal Sains dan Seni ITS IPTEK Journal of Proceedings Series
Suhartono Suhartono
Institut Teknologi Sepuluh Nopember, Surabaya
Arts Humanities Computer Science & IT Control & Systems Engineering Mathematics

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Journal : IPTEK Journal of Proceedings Series

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Simulation Study of Parameter Estimation Two-Level GSTARX-GLS Model Andria Prima Ditago; Suhartono Suhartono
IPTEK Journal of Proceedings Series No 1 (2015): 1st International Seminar on Science and Technology (ISST) 2015
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23546026.y2015i1.1167

Abstract

GSTAR is a special form of the VAR model and is one of the commonly used models for modeling and forecasting time series data and location. At GSTAR modeling, estimation method used is OLS, the method is considered to have a weakness, which will result in an inefficient estimator. Thus, one appropriate method is GLS. In this study, conducted modeling GSTARX two levels by adding a predictor of calendar variation model. Parameter estimation of the first level models made of predictors with a linear regression model, while the second level models using error models which is done on first level with GSTAR model. Calendar variation model discussed is the impact of Ramadhan effect. Results of the simulation study showed that GSTAR-GLS models produces a more efficient estimator than GSTAR-OLS, seen from the obtained standard error smaller.
Simulation of Generalized Space-Time Autoregressive with Exogenous Variables Model with X Variable of Type Metric Reza Mubarak; Suhartono Suhartono
IPTEK Journal of Proceedings Series No 1 (2015): 1st International Seminar on Science and Technology (ISST) 2015
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (331.186 KB) | DOI: 10.12962/j23546026.y2015i1.1170

Abstract

One of the models time series which also involves spatial aspects (spatio-temporal) is Generalized Space Time Autoregressive (GSTAR). Until now, GSTAR modelling don’t involve metric-type, which is called GSTARX. Parameter estimation for spatio temporal modeling is still limited by using Ordinary Least Square (OLS) which is less efficient because the residuals are correlated. Generalized Least Square (GLS) is one of the alternative methods for parameter estimation residuals are correlated. In this study would like to looking at the efficiency of GLS estimation method is compared with OLS to correlated data in GSTARX model. Simulation results show that the estimation GLS method is more efficient than using OLS if residual correlated.
Co-Authors Agus Suharsono Andria Prima Ditago Clara Agustin Stephani Dhoriva Urwatul Wutsqa Eva Laylatus Fakhrul Azizi Indillah Hidayatul Khusna Indah Kurnia Putri Indana La Zulfa Nayla Nayla Ma’rufah Ma’rufah Noorgam Ika Rachmawati Retno Dyah Handini Reza Mubarak Santi Puteri Rahayu Setiawan Setiawan