Analyzing the Relationship between the Digital Economy and the GDP of Iran and Malaysia Using Long Short-Term Memory Neural Networks
Subject Areas : ICT
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Keywords: Digital economy, Machine learning, Artificial neural networks, Long short-term memory,
Abstract :
With the increasing growth of digital technologies, examining the role of the digital economy in the growth and structural changes of the macroeconomics has become one of the key policy issues. The main issue of the present study is to examine the effect of digitalization of the economy on the GDP of Iran and Malaysia, and it uses machine learning algorithms, especially neural networks with Multi-Level Long Short-term Memory layers, to model the behavior of GDP in these countries. In this regard, four separate model configurations have been designed and implemented: (1) a hybrid model including classical economic variables (capital and labor) and information and communication technology (ICT) indicators, (2) a model based solely on ICT indicators, (3) a model based solely on classical economic variables, and (4) a hybrid reduced model. The models were trained independently for each country, and loss function analysis, convergence point determination, and forecast accuracy measurement with an error threshold of 0.1 were used to evaluate their performance. The results showed that digitalization, especially through infrastructure such as fixed broadband internet, mobile network coverage, and international broadband usage, is significantly associated with GDP. The hybrid model that simultaneously uses conventional and digital variables performed best in Iran and Malaysia, indicating that digitalization by itself cannot replace conventional factors of production, but plays a complementary and reinforcing role in the growth process. Accordingly, policymakers seeking to increase economic growth and productivity should pursue the development of digital economy infrastructure alongside investment in human and physical capital.
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