Balance of payments and power: assessing China's global and regional interdependence relationship

Chen, Ian Tsung-yen

doi:10.1093/irap/lcu001

Cited by 2 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These facts, together with the Lacey´s Act on the ban of illegal trade with animals, affected the balance of payments in several branches. According to Chen (2014), the USA is the world´s largest consumer of goods. In recent years, the USA imports more technologically advanced products (Wang, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…The current pace of the PRC growth may cause that the USA will lose its leading position. In the case of primary purchased PRC goods, the current price on the global market is set according to the needs of the PRC, which improves its position on the global markets (Chen, 2014). In recent years, the PRC balance of payments has been increasingly destabilizing.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Considering seasonal fluctuations in equalizing time series by means of artificial neural networks for predicting development of USA and People´s Republic of China trade balance

Vrbka¹,

Šuleř²,

Machová³

et al. 2020

View full text Add to dashboard Cite

Balance of payments is an accounting identity of each country. The ability to make a qualified and accurate prediction of trade balance of huge world economies such as the USA and the People´s Republic of China economies can have influence on the world´s economy. An enormous expansion and advancement of artificial intelligence offers a possibility to measure and predict also this indicator. The aim of the contribution is to propose a methodology for taking into account the seasonal fluctuations in equalizing time series by means of artificial neural networks on the example of the USA and People´s Republic of China trade balance. For the analysis, the trade balance data of the two countries from the period between 1985 and 2018 are used. Regression by means of neural structures is carried out in two alternatives, where the second calculation takes into account the monthly seasonality of the time series. The result indicates that the additional variable in the form of the month in which the value was measured enables more order and accuracy. The other experimental calculation indicates that especially the fourth and the fifth retained neural networks are able to capture the whole course of the trade balance. They are thus able to identify and maintain local fluctuations of the time series, that is, to maintain its seasonal course. An interesting fact is also that in the case of the first alternative, the most successful networks were only the radial basis function neural networks, while in the second alternative those were only the multilayer perceptron networks.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Considering seasonal fluctuations in equalizing time series by means of artificial neural networks for predicting development of USA and People´s Republic of China trade balance

Vrbka¹,

Šuleř²,

Machová³

et al. 2020

View full text Add to dashboard Cite

show abstract

Comparison of neural networks and regression time series in estimating the Czech Republic and China trade balance

2019

View full text Add to dashboard Cite

Foreign trade has been and is considered to be very important.Trade balance measurement provides one of the best analyzes of a country's external economic relations. It serves as a monetary expression of economic transactions between a certain country and its foreign partners over a certain period. The aim of this paper is to compare the accuracy of time series alignment by means of regression analysis and neural networks on the example of the trade balance of the Czech Republic and the People's Republic of China. Trade balance data between the Czech Republic and the People's Republic of China is used. This is a monthly balance starting in 2000 and ending in July 2018. First, a linear regression is made followed by regression using artificial neural networks. A comparison of both methods at expert level and experience of the evaluator, the economist, is performed. Optically, the LOWESS curve appears to be best out of the linear regression and the fifth preserved RBF 1-24-1 network seems the mot appropriate out of neural networks.

show abstract

Balance of payments and power: assessing China's global and regional interdependence relationship

Cited by 2 publications

References 14 publications

Considering seasonal fluctuations in equalizing time series by means of artificial neural networks for predicting development of USA and People´s Republic of China trade balance

Considering seasonal fluctuations in equalizing time series by means of artificial neural networks for predicting development of USA and People´s Republic of China trade balance

Comparison of neural networks and regression time series in estimating the Czech Republic and China trade balance

Contact Info

Product

Resources

About