• Environmental and Resource Economics Review
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• v.26 no.1
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• pp.37-56
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• 2017

This study analyzed the mutual causal relationship between energy consumption, production, and export for manufacturing industry in Korea. The Korean manufacturing industry was divided into nine industries and panel data was constructed from 1991 to 2013. The panel Granger causality test method developed by Demitrescu and Hurlin (2012) was used along with the Vector Error Correction Model. This analysis showed that there was Granger Causality from production to energy consumption, from exports to energy consumption. However, Granger Causality was not established in the opposite direction. Therefore, this result supports the conservation hypothesis of Qzturk (2010) that energy-saving policies in the manufacturing sector can be implemented without adverse effects on production or exports in short-run. There is a long-run cointegrating relationship between production, energy consumption, exports, labor, and capital in the Korean manufacturing sector. Furthermore, the energy consumption contributes to the increasing of production in long-run equilibrium relationship.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.136-151
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• 2016

Flexible large-scale WLANs are now widely deployed in crowded and highly mobile places such as campus, airport, shopping mall and company etc. But network management is hard for large-scale WLANs due to highly uneven interference and throughput among links. So the traffic is difficult to predict accurately. In the paper, through analysis of traffic in two real large-scale WLANs, Granger Causality is found in both scenarios. In combination with information entropy, it shows that the traffic prediction of target AP considering Granger Causality can be more predictable than that utilizing target AP alone, or that of considering irrelevant APs. So We develops new method -Granger Causality and Vector Auto-Regression (GCVAR), which takes APs series sharing Granger Causality based on Vector Auto-regression (VAR) into account, to predict the traffic flow in two real scenarios, thus redundant and noise introduced by multivariate time series could be removed. Experiments show that GCVAR is much more effective compared to that of traditional univariate time series (e.g. ARIMA, WARIMA). In particular, GCVAR consumes two orders of magnitude less than that caused by ARIMA/WARIMA.

• Journal of Korea Port Economic Association
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• v.23 no.4
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• pp.216-227
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• 2007

The aim of this paper is to find out the nature of causality between the two ocean freights employing the Granger method. That is because the Baltic freights tend to move very closely and seem to be behave like one time series. The Granger causality test, however, is very sensitive to the number of lags used in the analysis. This means that one has to be very careful in implementing the Granger causality test. This paper, hence, uses more rather than the lags which the Akaike Information Criterion and the Schwarz Information Criterion suggest. This study shows that BPI does not "Granger-cause" BCI and BSI, but BCI and BSI Granger-cause BPI. I also discover that BHSI does not "Granger-cause" BPI and BSI, but BPI and BSI Granger-cause BHSI. I, hence, model and estimate the ocean freight function and show that the Baltic ocean freight market is inefficient and the biased estimator of the other freight.

• Journal of Technology Innovation
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• v.24 no.2
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• pp.91-113
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• 2016

The purpose of this study is to examine the causal relationship among R&D spending and variables of technology trade, and to explore promoting R&D activities and revitalizing technology trade. To analyze the causal relationship, we built a multivariate model that consists of government R&D spending, private R&D spending, technical importation and export of techniques, and employed the Granger-causality test based on an error correction model. The results show that there are five Granger-causality relationship among them in the short run, as well as there are eleven Granger-causality relationship among a total of twelve causal relationship, excluding only a unidirectional causality relationship from the government R&D spending to the export of techniques, in the long run. Besides, we attempted the impulse-response analysis on them to observe the reaction of any dynamic system in response to some external change. The significance of this paper is to make sure the causal relationship between R&D investments and the technology trade by analyzing empirically, and to suggest several implications for promoting the R&D activities and revitalizing the technology trade.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.677-682
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• 2020

To investigate the nexus between urban population, gross capital formation, and economic growth in the Kingdom of Saudi Arabia, yearly data was collected from the World Bank for the period 1974- 2018. Basic statistics test and correlation matrix was used to investigate the causal effect among the tested parameters, followed by Augmented Dickey-Fuller (ADF) stationary test, co-integration analysis by Johansen test after that Vector Auto-Correction Model for both short-run and long-run and finally the Granger-Causality tests. Result of unit root test analysis shows that the urban population became stationary at I (0) level while economic growth and gross capital formation became stationary at I (1). Johansen co-integration analysis indicates that there is presence of both long-run and short-run relationship between the three variables in the Kingdom of Saudi Arabia. The result of the VECM Model reflects that both economic growth and gross capital formation have a negative impact on urban population in the short run. According to the Granger-Causality tests, there is unidirectional causality with the urban population by both gross capital formation and economic growth. Also, the result of the Granger Causality tests show that there is unidirectional causality between economic growth and gross capital formations.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.149-156
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• 2021

Gambling negatively affects the economy, and it brings unwanted financial, social, and health outcomes to gamblers. On the one hand, unemployment is argued to be a leading cause of gambling. On the other hand, gambling can cause unemployment in the second-order via gambling-induced poor health, falling productivity, and crime. In terms of significant effects, previous studies were able to establish an association, but not causality. The current study examines the time-sequence and contemporaneous causalities between lottery gambling and unemployment in Thailand. The Granger causality and directed acyclic graph (DAG) tests employ time-series data on gambling- and unemployment-related Google Trends indexes from January 2004 to April 2021 (208 monthly observations). These tests are based on the estimates from a vector autoregressive (VAR) model. Granger causality is a way to investigate causality between two variables in a time series. However, this approach cannot detect the contemporaneous causality among variables that occurred within the same period. The contemporaneous causal structure of gambling and unemployment was identified via the data-determined DAG approach. The use of time-series Google Trends indexes in gambling studies is new. Based on this data set, unemployment is found to contemporaneously cause gambling, whereas gambling Granger causes unemployment. The causalities are circular and last for four months.

• Industrial Engineering and Management Systems
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• v.14 no.1
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• pp.104-110
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• 2015

Every year governments spend their national budget on public health in order to reduce financial burden of individuals on health. Although it has been widely believed that the increase of public expenditure on health decreases private health expenditure, it has not been proved by analysis with real data. For better understanding, we conducted an empirical study on the real data of 17 OECD countries-Australia, Austria, Canada, Denmark, Finland, Germany, Iceland, Ireland, Japan, Korea, New Zealand, Norway, Portugal, Spain, Sweden, the United Kingdom, and the United States. The panel Granger-causality test is used to verify the cause-and-effect relationship between the two expenditures. As a result, public expenditure on health has a 3 to 4 year-lagged negative effect on private health expenditure in the cases of the 16 countries except for the United States.

• The Journal of Information Systems
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• v.29 no.1
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• pp.289-301
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• 2020

Purpose This study analyzes whether Internet search of cryptocurrency has a causal relationship to return and volatility of cryptocurrency. Design/methodology/approach Google Trend was used as a measure of the level of Internet search, and the parametric tests of Granger causality in the 1st moment and the 2nd moment were adopted as the analysis method. We used Bitcoin's dollar-based price, which is the No. 1 market value among cryptocurrency. Findings The results showed that the Internet search measured by Google Trends has a causal relationship to cryptocurrency in both average and volatility, while there is a difference in causality and its degree according to the search area and category that Google Trend user should set. Because the Granger causality is based on the improvement of prediction, the analysis results of this study indicate that Internet search can be used as a leading indicator in predicting return and volatility of cryptocurrency.

• Journal of the military operations research society of Korea
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• v.34 no.1
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• pp.79-98
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• 2008

The purpose of this paper is to find the desirable R&D policies in defense area by analyzing causality between GDP and R&D investments in government, private, defense sectors. We have five variables which are composed of GDP, total R&D investment, R&D investments in government, private and defense sectors to figure out the causality between R&D investment in defense sector and other components. In the course of analysis on causality, we took the unit root test of variables to prevent spurious regression. Also we need to take cointegration test about non-stationary variables before the causality test. According to these test results, we took the causality test using ECM(Error Correction Model) for the models which have cointegrating relations. And we took ordinary Granger causality test for model which doesn't have a long-run stationary relationship. As a result of the causality test, it was shown that there exists the long-nu causality to GDP and R&D investments in government and private sectors from other variables. However, there doesn't exist the causality to defense R&D investment from other variables. We found that there doesn't exist the causality between R&D investments in defense and private sectors, and that they are independent.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.837-848
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• 2021

Evaluating or predicting the effectiveness of economic policies is an important issue, but it is difficult to find an economic variable which causes a significant result because there are numerous variables that cannot be taken into account. A randomized controlled experiment is the best way to investigate causality, but it is not realistically possible to control through randomization and intervention in time series data such as macroeconomic data. Although some analysis methods have been proposed to find causality, the methods such as Granger causality method and Chow test are insufficient to explain causality. Recently, Pfister et al. (2019) proposed invariant causal prediction methods which can be applicable in time series data. In this paper, we introduce the method of Pfister et al. (2019) and use the method to find macroeconomic variables invariantly affecting the won-dollar exchange rate.