GDP Estimation and Slow Down Signal Model for Indonesia: An Artificial Neural Network Approach  

Oleh:

Imansyah, Muhammad Handry ; Suryani

Jenis: Article from Journal - ilmiah nasional - terakreditasi DIKTI
Dalam koleksi: Jurnal Keuangan dan Perbankan: Journal of Finance dan Banking vol. 13 no. 1 (Jun. 2011), page 77-94.
Topik: forecasting GDP; business cycles; artificial neural network.
Fulltext: GDP Estimation.pdf (1.03MB)

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: JJ12.1 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

The purpose of this paper is to develop a model estimation of Gross Domestic Product (GDP) and economic slowdown signal models with an artificial neural network approach. This approach is as an alternative or complement to other approaches that are widely used such as regression model. An artificial neural network model is inspired from the biological sciences such as the working of the human brain in solving problems. In this study, external sectors have substantial role in influencing the growth of GDP. Almost 90 percent of leading indicators of external factors contributing the fluctuation of Indonesian GDP. Major trading partner of Indonesian manufactured goods such as China, South Korea, US and Japan to some extents, however, affect GDP fluctuation. The diversification of trading partners and commodities to be exported is one of the most important policies to reduce external shocks. Based on the model developed, the performance model is adequate in predicting the samples – in and outside – in terms of a lower error. This model, however, is still experimental in nature. Therefore, it needs to be further developed by using different topology and adding observations.

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