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- P-ISSN1738-3110
- E-ISSN2093-7717
- SCOPUS, ESCI
ISSN : 1738-3110
Prediction and Causality Examination of the Environment Service Industry and Distribution Service Industry
Lee, Choong-Hyo
Abstract
Purpose - The world now recognizes environmental disruption as a serious issue when regarding growth-oriented strategies; therefore, environmental preservation issues become pertinent. Consequently, green distribution is continuously emphasized. However, studying the prediction and association of distribution and the environment is insufficient. Most existing studies about green distribution are about its necessity, detailed operation methods, and political suggestions; it is necessary to study the distribution service industry and environmental service industry together, for green distribution. Research design, data, and methodology - ARIMA (auto-regressive moving average model) was used to predict the environmental service and distribution service industries, and the Granger Causality Test based on VAR (vector auto regressive) was used to analyze the causal relationship. This study used 48 quarters of time-series data, from the 4th quarter in 2001 to the 3rd quarter in 2013, about each business type's production index, and used an unchangeable index. The production index about the business type is classified into the current index and the unchangeable index. The unchangeable index divides the current index into deflators to remove fluctuation. Therefore, it is easy to analyze the actual production index. This study used the unchangeable index. Results - The production index of the distribution service industry and the production index of the environmental service industry consider the autocorrelation coefficient and partial autocorrelation coefficient; therefore, ARIMA(0,0,2)(0,1,1)4 and ARIMA(3,1,0)(0,1,1)4 were established as final prediction models, resulting in the gradual improvement in every production index of both types of business. Regarding the distribution service industry's production index, it is predicted that the 4th quarter in 2014 is 114.35, and the 4th quarter in 2015 is 123.48. Moreover, regarding the environmental service industry's production index, it is predicted that the 4th quarter in 2014 is 110.95, and the 4th quarter in 2015 is 111.67. In a causal relationship analysis, the environmental service industry impacts the distribution service industry, but the distribution service industry does not impact the environmental service industry. Conclusions - This study predicted the distribution service industry and environmental service industry with the ARIMA model, and examined the causal relationship between them through the Granger causality test based on the VAR Model. Prediction reveals the seasonality and gradual increase in the two industries. Moreover, the environmental service industry impacts the distribution service industry, but the distribution service industry does not impact the environmental service industry. This study contributed academically by offering base line data needed in the establishment of a future style of management and policy directions for the two industries through the prediction of the distribution service industry and the environmental service industry, and tested a causal relationship between them, which is insufficient in existing studies. The limitations of this study are that deeper considerations of advanced studies are deficient, and the effect of causality between the two types of industries on the actual industry was not established.
- keywords
- Environment Service Industry, Distribution Service Industry, ARIMA Model, VAR Model
Reference
Chang, Moon-Chul (2010). A Study on the Structural Analysis and Time-Series Forecasting in five years of Korean Export-import Logistics Volume. Journal of Korean Distribution and Management, 13(4), 177-199.
Cho, Sung-Ho, & Chang, Se-Jun (2009). Study on Forecasting Hotel Banquet Revenue by Utilizing ARIMA Model. The Korean Journal of Culinary Research, 15(2), 231-242.
Cho, Sung-Woo (2013). A Case Study on Green Logistics Management in Europe from the Perspective of Sustainable Development. The Journal of Contemporary European Studies, 31(3), 267-296.
Choi, Jae-Il, & Chung, Yong-Rak (2010). Prediction of the Number of the Korea Professional Soccer Spectators Using Time-series Data Analysis : 2009-2015. Journal of Sport and Leisure Studies, 39(2), 921-928.
Hsiao, M. C. (1987). Tests of Casuality and Exogeneity between Exports and Economic Growth : The case of Asian NICs. Journal of Economic Development, 12(2), 143-159.
Hwang, Young-Jin (2013). Forecasting Interest Rates Using Qual VAR and Evaluation of Monetary Policy. Journal of Money & Finance, 27(4), 139-171.
Jeong, Dong-Bin (2009). SPSS PASW Time Series Forecasting Ⅰ. Seoul, Korea: Hannarea Academy.
Jeong, Hyo-June, & Lee, Hong-Keun (2002). Comparison of the BOD Forecasting Ability of the ARIMA model and the Artificial Neural Network Model. Journal of Environmental Health Sciences, 28(3), 19-25.
Jeong, Hye-Yeon, & Chung, Jung-Chae (2011). The Effect of Environmentally Conscious Logistics on Logistics Performance. Korean Journal of Logistics, 18(2), 5-20.
Jung, Heon-Yong (2007). An Analysis of the Korean Commercial Banks Loans Using VAR Model. Korean Corporation Management Review, 14(1), 129-140.
Jung, Hun-Bae, & Lee, Il-Han (2005). Study on the Firm`s Implementation of Environmental Logistics. Korean Journal of Logistics, 13(1), 19-40.
Kang, Sung-Man (2011). Case Study about the Advanced Foreign Enterprises for Revitalization Plans of the Green Logistics of Logistics Enterprises in Korea. Journal of Korean Distribution and Management, 14(1), 143-164.
Kim, Bae-Geun (2011). Estimating the Effects of Fiscal Policy Using Structural VARs and Tax Rates. The Korean Economic Review, 59(3), 5-52.
Kim, Bo-Mi, & Kim, Jae-Hee (2013). Time Series Models for Daily Exchange Rate Data. The Korean Journal of Applied Statistics, 26(1), 14-27.
Kim, Hyeon-Soo (2004). A Study of the Reverse Logistics Information Factors for Environmental Conscious Logistics System. Journal of society of Korea industrial and systems engineering, 27(4), 59-68.
Kim, Hyeon-Soo (2009). Environmental Conscious Logistics Management. Postal Service Information, 79, 5-24.
Kim, Jae-Gyeong (2013). An Empirical Analysis on the Relationship between Stock Price, Interest Rate, Price Index and Housing Price using VAR Model. Journal of Distribution Science, 11(10), 63-72.
Kim, Ji-Hyun (2013). Logistics laws. Seoul, Korea: Sinjiwon Publisher.
Kim, Jun-Hong, Jin, Dal-Lae, Lee, Ji-Sun, Kim, Su-Ji, & Son, Young-Sook (2012). Prediction of the interest spread using VAR model. Journal of the Korean Data &Information Sciences Society, 23(6), 1093-1102.
Kim, Kyung-Mi (2009, January). Monthly Maritime Korea, Seoul, Korea: Korea Maritime Institute, 98-104.
Kim, Sang-Su (2013). A Study on the Impact of Oil Price Volatility on Korean Macro Economic Activity : An EGARCH and VECM Approach. Journal of Distribution Science, 11(10), 73-79.
Kim, Sei-Wan, & Park, Ki-Jeong (2006). Study on Real Estate Market Factors` Relative Effect. The Korean Journal of Economics, 13(2), 171-198.
Kim, Seung-Sub (2011, June). Monthly Maritime Korea, Seoul, Korea: Korea Maritime Institute, 132-136.
Kim, Tae-Gu, & Kang, Young-Sig (2010). A Study on Forecasting of Accident Rates in the Service Industry :Focused on the Warehousing and Transportation Division. Journal of the Korean Institute of Plant Engineering, 15(2), 103-108.
Kim, Tae-Hwan, & Ryu, Seong-Kyun (2012). A Study on the Green Growth Policy and Government Support :Focusing on the Green Logistics. International Commerce and Information Review, 14(1), 315-344.
Korea Environment Institute (2000). Analysis of the effects of environmental services and expand market liberalization countermeasures. Sejong, Korea: Ministry of Environment, 30-31.
Korea Institute for International Economic Policy (2009). Market Analysis and entry into service domestic environment APEC Study. Sejong, Korea: Ministry of Environment, 2-4.
Lee, Hyoung-Wook, & Lee, Ho-Byung (2009). Comparative Analysis for Predictability of Housing Price Index by Model in Seoul. Korea Real Estate Academy Review, 38, 215-235.
Lim, Kyu-Chae (2007). The empirical study on the construction properties of the real estate market in korea. Daegu, Korea: Thesis for Doctorate in Yeungnam University.
Mo, Soo-Won, & Lee, Kwang-Bae (2013). Causality Analysis between Port Trading Volume and Industrial Activity. Journal of Shipping and Logistics, 29(2), 221-235.
Mun, Beung-Geun, & Kim, Sung-Ok (2012). An Analysis on Dynamic Relationship between Local Government Educational Expenditure and Human Capital with Regional Economic Growth. The Journal of Korean Public Policy, 14(2), 103-123.
Nam, Jun-Woo, & Lee, Han-Sik (2011). Econometrics, Third Edition. Seoul, Korea: Hongmoon Publisher.
Oh, Se-Young, & Lee, Shin-Mo (2001). A Pilot Study on the Environmental Logistics. Journal of Shipping and Logistics, 9(2), 31-49.
Park, Chu-Hwan, & Han, Jin-Mi(2008). A Study for the Co-Relationship among Birth, Women Employment, and Economic Growth by the VAR Approaches. Quarterly Journal of Labor Policy, 8(1), 1-26.
Park, Heon-Soo, & An, Ji-A (2009). The Sources of Regional Real Estate Price Fluctuations. Korea Real Estate Review, 19(1), 27-49.
Park, Hun-Ju, & Park, Chul(2001). A Study on the Forecasting in Land Market using Time Series Model. Housing Studies Review, 9(1) 27-55.
Park, Jun-Yong, Jang, yu-sun, & Han, Sang-Beom (2008). Economic Time Series Analysis. Seoul, Korea:Kyeongmoon Publisher.
Park, Seog-Ha (2005). Effect of Environmental Logistics Activity on Logistics Performance of Enterprises. Journal of Shipping and Logistics, 46, 47-70.
Park, Song-Choon, & Cho, Yeong-Suk (2009). Analysis on Macroeconomic Variables Affect on the RP Rate. Korean Corporation Management Review, 16(1), 167-182.
Rho, Hyung-Jin (2011), Time Series Analysis friendly. Seoul, Korea: Hakhyeon Publisher.
Rodrigue, J. P., Slack, B., & Comtois, C. (2001). The handbook of Logistics and supply chain Management. London, UK:Pergamon Press.
Ryu, Byong-Ro, & Han, Yang-Su (1998). Forecasting of Stream Water Quality by ARIMA Model. Journal of the Korean Environmental Sciences Society, 7(4), 433-440.
Schwert, G. W. (1989). Why Does Stock Market Volatility Change over Time ?. Journal of Finance, 44(4), 1115~1153.
Sim, Kyu-Won, & Kwon, Heon-Gyo (2011). A Study on Forecasting Visit Demands of Korea National Park Using Seasonal ARIMA Model. Journal of Korean Forest Society, 100(1), 124-130.
Sims, C. A.(1980). Macroeconomics and Reality. Econometrica, 48, 1-48.
Son, Eun-Ho, & Park, Duk-Byeong (2012). Forecasting of Yeongdeok Tourist by Seasonal ARIMA Model. Journal of Agricultural Extension & Community Development, 19(2), 301-320.
Sul, Min-Sin, & Park, Doo-Yong (2011). A Prediction of Demand for female sport participants by Using Seasonal ARIMA Model. Journal of Korean Physical Education Association for Girls and Woman, 25(3), 179-192.
Sun, Il-Suck (2010). A Political Proposal for the Reverse Logistics Activation. Journal of Channel and Retailing, 15(5), 61-79.
Sun, Il-Suck, & Kwon, Jea-Hyen (2013). Correlation Analysis on Wholesale and Retail Market and Macroeconomic Variables. Journal of Korean Distribution and Management, 16(4), 75-83.
Sun, Il-Suck, Lee, Won-Dong & Park, Jong-Sam (2013). A Time-Series Prediction of the Logistics Service Industry and an Analysis on the Casual Relation between the Industry and Other Industries. Korea Logistics Review, 23(2), 5-23.
Wu, Haw-Jan, & Dunn, S. S. (1995). Environmentally Responsible Logistics System. International Journal of Physical Distribution & Logistics Management, 25(2), 22-25.
Yoon, Jong-In (2005). Testing the Stationarity of the Inflation Rate and Nominal Interest Rate. Korean Journal of Money and Finance, 10(2), 143-164.
Yu, Byung-Chul, & Cho, Chan-Hyouk (2008). An Empirical Study on the Traffic Volume of Busan Port and Shanghai Using VAR Model. Korea Logistics Review, 18(3), 189-208.
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