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BRIEF RESEARCH REPORT article

Front. Appl. Math. Stat.
Sec. Mathematical Finance
Volume 10 - 2024 | doi: 10.3389/fams.2024.1365723

GLS Estimation in Phyton to Forecast Gross Regional Domestic Product Using Generalized Space-Time Autoregressive Seemingly Unrelated Regression (GSTAR-SUR) Model

Provisionally accepted
Prizka R. Arum Prizka R. Arum *
  • Universitas Muhammadiyah Semarang, Semarang, Indonesia

The final, formatted version of the article will be published soon.

    Economic growth is essential for regional economic performance, with Gross Regional Domestic Product (GRDP) being a key indicator of economic development over time. In this research case, the GRDP data of various provinces in java for the years 2010-2023 will be used as the variable being studied. The data obtained from the GRDP variable contains spatial and temporal information, hence requiring an appropriate model to forecast spatio-temporal data, namely the Generalized Space Time Autoregressive (GSTAR) model. However, in estimating the parameters, the GSTAR model is unable to detect correlated residuals between equations, resulting in inefficient estimators. Therefore, an appropriate estimation method is needed to address correlated residuals, namely the Generalized Least Square (GLS) estimation method within the Seemingly Unrelated Regression (SUR) framework. The GSTAR-SUR method is applied to forecast Java's economic growth rate. The optimal model, GSTAR SUR (11)-I(1) with Inverse distance location weights, demonstrates high accuracy with a Mean Absolute

    Keywords: GRDP, Java Island, GSTAR, SUR, GSTAR-SUR

    Received: 08 Jan 2024; Accepted: 08 Jul 2024.

    Copyright: © 2024 Arum. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Prizka R. Arum, Universitas Muhammadiyah Semarang, Semarang, Indonesia

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