Indonesian Trade Deficit with China: Background and Grey Forecasting

Indonesia's trade balance with China has remained negative since 2010. The current study forecasts Indonesia's trade deficit with China for five years using the Even Grey Forecasting model EGM (1,1,α,θ). The sample was conducted by collecting the data of traded deficits for the past ten years. Data were collected from the official websites of Indonesia's Central Bureau of Statistics of (BPS), Ministry of Trade, among others. By building upon the literature, the study argues that trade deficits might have occurred from internal and external factors, such as the lack of infrastructure, the depreciation of the Rupiah (Indonesian currency) against the U.S. dollar, and the ASEAN-China Free Trade Agreement. Comparative analysis with Linear Regression (LR), Exponential Regression (ER), and Exponential Triple Smoothing (ETS) revealed the superiority of the grey forecasting model for trade deficit prediction. The study found that the trade deficit was minimum during the COVID-19 pandemic. It also showed an increasing trade deficit in the post-COVID period. The study concludes with some recommendations for Indonesia to minimize the trade deficit.

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Forecast of the informatization index of China from 2011 to 2015 using the Grey forecasting model

2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS) ◽

10.1109/gsis.2015.7301861 ◽

2015 ◽

Author(s):

Yancai Zhang

Keyword(s):

Forecasting Model ◽

Grey Forecasting ◽

Grey Forecasting Model

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Study of a discrete grey forecasting model based on the quality cost characteristic curve

Grey Systems Theory and Application ◽

10.1108/gs-06-2017-0016 ◽

2017 ◽

Vol 7 (3) ◽

pp. 376-384 ◽

Cited By ~ 5

Author(s):

Wenjie Dong ◽

Sifeng Liu ◽

Zhigeng Fang ◽

Xiaoyu Yang ◽

Qian Hu ◽

...

Keyword(s):

Prediction Accuracy ◽

Characteristic Curve ◽

Grey System Theory ◽

Forecasting Model ◽

Quality Level ◽

Cost Models ◽

Quality Cost ◽

Content Type ◽

Grey Forecasting ◽

Grey Forecasting Model

Purpose The purpose of this paper is to clarify several commonly used quality cost models based on Juran’s characteristic curve. Through mathematical deduction, the lowest point of quality cost and the lowest level of quality level (often depicted by qualification rate) can be obtained. This paper also aims to introduce a new prediction model, namely discrete grey model (DGM), to forecast the changing trend of quality cost. Design/methodology/approach This paper comes to the conclusion by means of mathematical deduction. To make it more clear, the authors get the lowest quality level and the lowest quality cost by taking the derivative of the equation of quality cost and quality level. By introducing the weakening buffer operator, the authors can significantly improve the prediction accuracy of DGM. Findings This paper demonstrates that DGM can be used to forecast quality cost based on Juran’s cost characteristic curve, especially when the authors do not have much information or the sample capacity is rather small. When operated by practical weakening buffer operator, the randomness of time series can be obviously weakened and the prediction accuracy can be significantly improved. Practical implications This paper uses a real case from a literature to verify the validity of discrete grey forecasting model, getting the conclusion that there is a certain degree of feasibility and rationality of DGM to forecast the variation tendency of quality cost. Originality/value This paper perfects the theory of quality cost based on Juran’s characteristic curve and expands the scope of application of grey system theory.

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Estimating the Impact of ASEAN China Free Trade Agreement on Indonesia’s Trade Balance

Customs Research and Applications Journal ◽

10.31092/craj.v1i1.10 ◽

2019 ◽

Vol 1 (1) ◽

pp. 18-37

Author(s):

Muhammad Arif Junaidi

Keyword(s):

Gravity Model ◽

Free Trade ◽

Significant Role ◽

Trade Balance ◽

Free Trade Agreement ◽

Trade Agreement ◽

Trade Flows ◽

Insignificant Effect ◽

Asean Countries ◽

The Impact

Using trade flows data of ASEAN countries and China from 2002 to 2017, this studyestimates the impact of ACFTA on ASEAN countries and China’s trade balance in general,and also for Indonesia’s trade balance in specific by elaborating the impact of ACFTA onthe trade flows both exports and imports. Using the gravity model and estimating by OLSand PPML, this paper finds that the impact of tariffs elimination due to the implementationof ACFTA increased exports and imports for ASEAN countries and China in general, andfor Indonesia in particular. However, the aggregate trade balances of ASEAN membercountries and China is zero since the impact of ACFTA on imports offset the impact ofACFTA on exports. Tariff’s elimination due to the implementation of ACFTA on Indonesiashows a negative and statistically insignificant effect on imports and exports. Thus, tariffshave not played significant role on increasing Indonesia’s exports and imports. As a result,the impact of ACFTA on Indonesia’s trade balance cannot be quantified clearly since theimpact of tariffs on exports and imports are not significant.

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A comparative study of univariate models for container throughput forecasting of major ports in Asia

Proceedings of the Institution of Mechanical Engineers Part M Journal of Engineering for the Maritime Environment ◽

10.1177/14750902211023662 ◽

2021 ◽

pp. 147509022110236

Author(s):

Juan Huang ◽

Ching-Wu Chu ◽

Hsiu-Li Hsu

Keyword(s):

Regression Model ◽

Moving Average ◽

Forecasting Model ◽

Short Term ◽

Autoregressive Integrated Moving Average ◽

Port Authorities ◽

Grey Forecasting ◽

Grey Forecasting Model ◽

Short Term Forecasting ◽

Trigonometric Regression

This study aims to make comparisons on different univariate forecasting methods and provides a more accurate short-term forecasting model on the container throughput for rendering a reference to relevant authorities. We collected monthly data regarding container throughput volumes for three major ports in Asia, Shanghai, Singapore, and Busan Ports. Six different univariate methods, including the grey forecasting model, the hybrid grey forecasting model, the multiplicative decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, and the seasonal autoregressive integrated moving average (SARIMA) model, were used. We found that the hybrid grey forecasting model outperforms the other univariate models. This study’s findings can provide a more accurate short-term forecasting model for container throughput to create a reference for port authorities.

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