GLS estimation in python to forecast gross regional domestic product using generalized space–time autoregressive seemingly unrelated regression model - Rismawati Arum, Prizka; Fathoni Amri, Ihsan; Amri, Saeful (Frontiers in Applied Mathematics and Statistics, 2024)
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