Search

The COVID-19 pandemic has caused significant disruption to global economic activity, global supply chains and international trade. Studies forecasting the impact of the COVID-19 pandemic on South African trade are sparse despite an increase in research on the pandemic. We investigated the effect of COVID-19 on South African export and import values by firstly analysing the effect of other past crises on trade using monthly trade data for South Africa for the period January 2005 to July 2020. Before the forecasts, we validated the forecasting power of ARIMA models in the presence of significant swings in trade. Thereafter forecast values for the period August 2020 to July 2022 were computed. Our findings reveal that the COVID-19 pandemic, like other supply chain disruptions of global proportion, will result in the contraction of South African trade. However, the country is more likely to report a positive trade balance. This will contribute to a positive balance of payments and exchange rates during the forecast period. Weak domestic demand may explain the inferior imports against exports predicted between August 2020 and July 2022. Despite the anticipated positive trade balance, weak domestic demand could also weaken the country‟s economic growth projections. The forecasts in this study may also be used by policymakers to anticipate tariff revenue from the different regions. It is also imperative for policymakers to advance bilateral trade agreements with Asian, European and African countries, the major export destinations. The African Continental Free Trade Area is a welcome strategy that may boost trade between South Africa and other African countries.

Abstract The global environmental concerns associated with the increased energy demand have promoted the use of natural gas as a low-carbon fuel. This caused an increase in LNG supply and demand coupled with the need for useful tools to devise secure LNG trade schemes. While most studies focused either on assessing the security of past/current LNG trade, or on extrapolating LNG trade trends in the future, this study introduces a new simplified coarse-grained model that devises optimized LNG import/export schemes ensuring secure trade for suppliers and markets. The model is based only on four variables: LNG demand, LNG liquefaction capacity, utilization of liquefaction capacity, and transport distance. First, the model was used to generate LNG trade portfolios for Asia Pacific and Europe between 2003 and 2016. The HHI index then showed that the model always devised a more diversified, secure LNG trade. The different import strategies of the two markets and their evolution with time was highlighted, revealing more secure import regulations by AP. Finally, the model was used to forecast LNG import portfolios for AP and EU in 2030, which emphasized a significant change in the market share of conventional exporters with the introduction of new US and Australian LNG. Highlights • A coarse grained model has been developed to devise LNG trade portfolios under secure supply. • The model was validated by comparing its results with real LNG trade portfolios from 2003 to 2016 in Asia Pacific and Europe. • The average deviation from the model in Asia Pacific and Europe was about 2.5% and 5% respectively. • HHI index showed that the model devises a more secure LNG trade throughout the study period. • The model was used to forecast the LNG trade portfolio for Asia Pacific and Europe in 2030. [ABSTRACT FROM AUTHOR]

• Global exports and imports exhibit a long-run equilibrium relationship. • The long-run equilibrium can be demonstrated by their structural economic variables. • A novel ensemble learning approach is proposed for predicting the prevalence of global trade. • Our approach exhibits higher accuracy compared to other forecasting models. • Trade data obtained from 10 countries validate the method's stability. Forecasting global foreign trade is essential for developing government trade policies and management strategies for multinational corporations. However, achieving an accurate trade forecast is challenging because of the complex structural relationships between exports, imports and other economic variables. Many traditional forecasting models, such as time series, econometric, and machine learning, provide less accurate forecasts for trade data. This paper proposes an ensemble learning approach to improve forecasting performance by hybridizing the structural relationships between trade and deep learning models to predict foreign trade for ten major countries. The proposed method first establishes a cointegration relationship between exports and imports and their structural variables. The cointegrated models are then used to predict the future of trade, which is used as a benchmark model for comparison. A hybrid deep learning algorithm uses the cointegrated variables as input variables to predict trade data, and then are compared with time-series forecasts and economic structural models. The experimental results reveal that the ensemble learning method can achieve excellent forecasting performance for the tested periods of trade data. In most cases, the root means square error and mean absolute percentage error values are smaller than the time series and economic structural models. [ABSTRACT FROM AUTHOR]

This article revisits the popular gravity model of trade in light of the increasingly acknowledged findings of spatial econometrics and interprets the results in view of some recent theoretical developments from the economic literature that contribute to its foundation. When the inherent spatial effects are explicitly taken into account, the magnitude of the estimated parameters changes considerably and, with it, the measures on the predicted trade flows. This result is illustrated for the case of predicted trade flows between the European Union and some of its potential members. [ABSTRACT FROM AUTHOR]

• Changes in international trade constitute a crucial topic in economics. • We developed a multivariate LSTM-based economic theory for trade forecasting. • Trade data are considered as multivariate series with a two-way causal relationship. • The multivariate LSTM-based economic theory demonstrates the superior performance. Changes in foreign trade (export and import) constitute a crucial topic in international economics, international business management, and economic development. Numerous academics and industry leaders have sought effective means of forecasting foreign trade. However, with the uncertain nature of trade trends, obtaining accurate forecasts is a challenge. To analyze ten countries' trade data, this study developed an effective foreign trade forecasting method that relies on a neural network with long short-term memory (LSTM); the results validated the effectiveness of the proposed method. This study is based on the economic theory that the two-way causal relationships present in trade data can improve trade forecasting. A multivariate LSTM-based method is proposed and exploited to extract temporal changes from trade data and provide effective trade forecasting. A comparison was conducted to understand the performance of the proposed method against time-series and economic structural models. The empirical results indicate that the method can appropriately model temporal information regarding uncertainty trends in foreign trade data. The method achieved almost perfect forecasting performance for data previously difficult to predict; in most cases, it had smaller values of root mean square error (RMSE) and mean absolute percentage error (MAPE) than did time-series models and economic structural models. On the export forecast, RMSE improved by 17.048% and MAPE by 1.463%, and for imports, RMSE improved by 40.939% and MAPE by 1.806%. This paper demonstrates the feasibility of the theoretical synthesis and provides a theoretical basis for interdisciplinary research in foreign trade forecasting. [ABSTRACT FROM AUTHOR]