• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.8
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• pp.845-852
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• 2018

Mayer-Schönberger and Cukier(2013) explain why big data is important for our life, while showing many cases in which analysis of big data has great significance for our life and raising intriguing issues on the analysis of big data. The two authors claim that correlation is in many ways practically far more efficient and versatile in the analysis of big data than causality. Moreover, they claim that causality could be abandoned since analysis and prediction founded on correlation must prevail. I critically examine the two authors' accounts of causality and correlation. First, I criticize that corelation is sufficient for our analysis of data and our prediction founded on the analysis. I point out their misunderstanding of the distinction between correlation and causality. I show that spurious correlation misleads our decision while analyzing Simpson paradox. Second, I criticize not only that causality is more inefficient in the analysis of big data than correlation, but also that there is no mathematical theory for causality. I introduce the mathematical theories of causality founded on structural equation theory, and show that causality has great significance for the analysis of big data.

• Nuclear Engineering and Technology
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• v.54 no.7
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• pp.2510-2518
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• 2022

The main purpose of collecting and analyzing operating experience events is to look for insights to improve safety. The underlying assumption is the correlation and causality between events with different safety significances. The analysis of this assumption could be valuable to optimize operating experience feedback programs and to enhance safety. This paper analyses the correlation between events with different safety significances. Groups of events from six nuclear energy related databases are considered. The findings are that a correlation exists but with various levels and not as large or consistent as might be expected across different databases. These results might be the basis for further work to analysis causality, to find out how a similarity in causes influences the correlation, and finally to improve the operating experience program.

• Journal of the Korean Data and Information Science Society
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• v.20 no.4
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• pp.655-660
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• 2009

As the global economic crisis, the Korean foreign exchange market appears unstable with large fluctuations in exchange rate. Inevitably, there is growing attention on price variables such as exchange rate and interest rates and also on corelation between the factors. This is an empirical study on the causality of fluctuation between exchange rate and interest rate in the Korean market under global economic crisis. The fluctuations in won/dollar exchange rate and call rate are described and followed by analysis of lead-lag relationship between the two variables using Cross-correlation function and Granger causality test.

• Journal of Climate Change Research
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• v.9 no.2
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• pp.171-179
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• 2018

The causal relationship between El Nino-Southern Oscillation (ENSO) and winter climate variability in Korea is tested by analyzing Korea Meteorological Administration Automatic Synoptic Observing System datasets for the past 59 years. Consistent with previous studies, positive phase of ENSO (El Nino) tends to cause warmer temperature and heavier precipitation in Korea in early winter with three-week lead time. This causality is quantified by performing Granger causality test. It turns out that ENSO explains an additional 9.25% of the variance of early-winter temperature anomalies in Korea, beyond that already provided by temperature itself. Likewise, 22.18% additional information is gained to explain early-winter precipitation variance by considering ENSO. This result, which differs from simple lead-lag correlation analysis, suggests that ENSO needs to be considered in predicting early-winter surface climate variability in Korea.

• The Journal of Asian Finance, Economics and Business
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• v.3 no.3
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• pp.79-91
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• 2016

This paper is the first of its kind using a non-linear approach based on cross-correlation function (CCF) to investigate the information arrival hypothesis in crude palm oil (CPO) futures market. Based on daily data from 1986 to 2010, our empirical results reveal that: First, the volume of volatility is not a proxy of information flow. Second, dependence causality running from current return to future volume in conditional variance exhibit an asymmetric pattern of time span with different signs of correlation between price and volume series. This finding indicates the presence of noise traders' hypothesis of price-volume interaction in CPO futures market. Both findings suggest that this futures market is weak-form inefficiency. In terms of investors' behavior, they tend to change their expectations on current return based on errors made in previous trade in generating abnormal volume in the subsequent period. As implied, it is advisable for the investors devise their future trading strategies according to time span and changes of return.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.192-200
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• 2019

This paper is analyzed causality using cointegration test and impact response after deriving a causality between direct overseas purchasing and sale and macroeconomic variables. The model used for the empirical analysis is the vector error correlation model. The model is used the macroeconomic variables such as the consumer price index and the GDP, and e-commerce variables such as direct overseas purchasing, direct overseas sales and online shopping amount. According to empirical analysis, the direct overseas purchasing has the causality with the consumer price index, and GDP has the causality with direct overseas purchasing and online. According to the impact response analysis of the VECM, the direct overseas purchasing has a positive effect on the CPI and GDP, but the direct overseas sales has a negative effect on the CPI and GDP. In addition, both direct overseas purchasing and sales have a negative effect on online shopping, but it has been shown that the direct overseas purchasing has a bigger negative effect on online shopping.

• 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.

• Journal of Environmental Science International
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• v.25 no.7
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• pp.937-950
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• 2016

Algal blooming in 4 major rivers introduces substantial impacts to water front activity. Concentrations of algae are increasing at major points along the Geum River. Ecosystem food webs can be affected by algal blooming because blue-green algae release toxic materials. Even though there have been many studies on blue-green algae, its causality to environmental factors has not been completely determined yet. This study analyzed the exclusive correlation between various hydrometeorological, water quality, and hydrologic variables and the cell number of cyanobacteria to understand causality of blue-green algae in the Geum River. A prewhitening process was introduced to remove the autocorrelation structure and periodicity, which is useful to evaluate the effective relationship between two time series.

• The Journal of Fisheries Business Administration
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• v.46 no.3
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• pp.119-127
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• 2015

This study is to analyse the causality and volatility spillover between farming fish species in consumption replacement relation using flatfish(oliver flounder) and rockfish's wholesale market price data from September 2006 to July 2015. For the analysis, VAR(5) model and bivariate asymmetric GARCH-BEKK model are employed. The empirical results of this study are summarized as follows: First, the price volatility of flatfish and rockfish is very large without the trend during the sample period. Second, the correlation coefficient between flatfish and rockfish wholesale markets has positive 0.1059 value. Third, causality relation is unidirectional from rockfish market to flatfish market. Fourth, conditional volatility spillover effect is unidirectional from rockfish market to flatfish market, but asymmetric volatility effect is bidirectional between flatfish and rockfish markets that implies the bad news arising from flatfish wholesale market impact on rockfish market's volatility and the bad news arising from rockfish wholesale market impact on flatfish market's volaltilty. Consequently, based on the thus results, the volatility spillover effect interacts and is bidirectional between flatfish and rockfish wholesale markets.

• Journal of the Economic Geographical Society of Korea
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• v.16 no.4
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• pp.631-646
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• 2013

This paper empirically explores the pattern of causality between bank lending and housing prices in Korea over a period of the early 1990s to the end of 2000s by employing a long term cointegration and short-term time series regression analysis. Although the contemporaneous correlation between bank lending and housing prices is large, the analysis shows that the intense interaction between credit growth and bank lending to household arises from a growth in banking lending responding to an increase in housing prices. In addition, the regulatory change such as the introduction of financial constraints on bank loans such as LTV and DTI in the early and mid-2000s has played a significant role in stabilizing financial and real estate markets.