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All Journal ESTIMASI: Journal of Statistics and Its Application Eksponensial
Astafira, Ilyas
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other

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Pemodelan Produk Domestik Regional Bruto (PDRB) di Indonesia Periode 2018-2021 dengan Analisis Regresi Data Panel Kesuma, Ahmad Rizky; Rinanda, Farikah Ayu; Astafira, Ilyas; Afriani, Nur; Fadlirhohim, Rizki Dwi; Lestari, Tri Septi Ayu; Sifriyani, Sifriyani
ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 2, Juli, 2024 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v5i2.27522

Abstract

High and sustainable economic growth is the main condition or a must for the continuity of economic development and increased welfare. GRDP is defined as the total added value generated by all business units in an area. The analytical method used in this study is panel data regression analysis. Panel data regression is used to observe the relationship between one dependent variable and one or more independent variables. This study aims to determine the panel regression model of Gross Regional Domestic Product (GDP) in Indonesia for the period 2018 to 2021 and to find out whether the domestic investment investment variable and the cooperative business volume variable affect GRDP in Indonesia for the 2018-2021 period. The results obtained in this study are that the best panel regression model for modeling GRDP is the FEM model and the variable Domestic Investment Investment and Cooperative Business Volume are variables that have a significant effect on the GRDP variable in Indonesia for the 2018-2021 period.
Peramalan Curah Hujan Di Kota Samarinda Menggunakan Vector Error Correction Model Astafira, Ilyas; Siringoringo, Meiliyani; Fathurahman, M.
EKSPONENSIAL Vol. 15 No. 2 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i2.1377

Abstract

The Vector Error Correction Model (VECM) was one of the multivariate time series models that was a development of the Vector Autoregressive (VAR). VECM could be used to forecast non-stationary time series variables that had cointegration relationships. This study used monthly data of rainfall, minimum air temperature, and maximum air humidity variables from January 2015 to December 2023 to form the VECM model. The purpose of this study was to obtain a VECM model for rainfall in the city of Samarinda and to forecast rainfall in the city of Samarinda using VECM. The results of the study showed that the VECM model that formed was VECM(1) with two cointegration relationships. The rainfall forecasted results with VECM(1) indicated a downward trend until April 2024 and a horizontal pattern from May to December, with the highest rainfall in January at 214 mm and the lowest rainfall in April at 182.5 mm. The forecasted results ranged between 180-300 mm, which was categorized as moderate, with forecasting accuracy using a MAPE value of 32.369%, which was considered quite good.
Co-Authors Afriani, Nur Fadlirhohim, Rizki Dwi Kesuma, Ahmad Rizky Lestari, Tri Septi Ayu M. Fathurahman Rinanda, Farikah Ayu Sifriyani, Sifriyani Siringoringo, Meiliyani