Estimating the Error Correction Model by Applying It to the Sectors of the Gross Domestic Product of the Republic Of Sudan (1960-2020s) | ||||
مجلة العلوم التجارية والبيئية | ||||
Volume 3, Issue 1, March 2024, Page 94-111 PDF (629.55 K) | ||||
Document Type: المقالة الأصلية | ||||
DOI: 10.21608/jcese.2024.254571.1048 | ||||
View on SCiNiTO | ||||
Authors | ||||
Yousif jaffar 1; Mohamed Elamin A. Allatif2; Intisar Yousif Ahmed1 | ||||
1قسم الاقتصاد الزراعي، كلية الدراسات الزراعية، جامعة السودان للعلوم والتكنولوجيا، السودان | ||||
2Department of Agricultural Economic, College of Agricultural Studies, Sudan ,University of Science and Technology, Sudan | ||||
Abstract | ||||
The study aimed to treat time series data problems and find out the relationship between components of GDP and contribution of agricultural sector and other economic sectors in long run, through the desorption and estimation of economic model to reflect the dynamic relationship between them in short run with an estimation of equilibrium parameters in long run. The study also tried to examine the direction of causality and co-integration of time series data and the gross domestic of agro-industrial and service in dollars. The study depended basically on time series data from secondary source for the period (1960-2020).Granger causality test and Error Correction model were applied to analyze the data using a program of economic performances (E-Views). The results revealed that the decline in the contribution of agricultural sector by 10% lead to lower GDP by 8%. However, the decline in the contribution of the service and industrial sectors each by 10% lead to lower GDP by (25% and 62%), respectively. Results also confirmed that speed error correction of GDP equation was statistically significant at 1%. Typically, 74% of imbalance of GDP in the long run has been corrected in year, while the error correction speed in the sectors that contributing to GDP (agriculture, industry and services) of imbalance was corrected by (19%, 2% and 1 %) respectively | ||||
Keywords | ||||
Time series data problems; GDP components; Agricultural sector; Causality and co-integration; Economic model estimation | ||||
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