Estimating the Error Correction Model by Applying It to the Sectors of the Gross Domestic Product of the Republic Of Sudan (1960-2020s)

Abstract

The study addressed time series data issues, investigating the long-term relationship between GDP components, the agricultural sector, and other economic sectors. It utilized desorption and economic model estimation to depict the short-term dynamic relationship and estimate long-term equilibrium parameters. Causality and co-integration of time series data, particularly concerning the gross domestic product (GDP) of agro-industrial and service sectors in dollars, were explored. Using time series data from 1960 to 2020, Granger causality tests and an Error Correction model were applied via E-Views for analysis. Findings indicated a 10% decline in the agricultural sector's contribution led to an 8% decrease in GDP. Similarly, reductions of 10% in the service and industrial sectors resulted in GDP declines of 25% and 62%, respectively. Speed error correction in the GDP equation was statistically significant at 1%. Typically, 74% of GDP imbalances in the long run were corrected within a year, with correction speeds for contributing sectors (agriculture, industry, and services) at 19%, 2%, and 1%, respectively. The results affirmed causal relationships between GDP and the service and agriculture sectors, as well as intimate causal relations between agriculture and industry and services. Lastly, the study recommended enhancing agricultural sector development through flexible investment support policies grounded in efficient and sustainable technology, emphasizing attention to both the agricultural and industrial sectors to improve GDP contributions, meet potential exports, and satisfy local demand.