본 연구에서는 시계열 모형을 이용하여 물류서비스 산업의 예측과 함께 다른 산업과의 인과성을 분석하였다. 물류서비스 산업의 예측은 ARIMA 모형 (auto-regressive moving average model)을 활용하였으며, 인과관계 분석은 벡터자기회귀모형(VAR : Vector Auto Regressive)의 Granger 인과관계 검정을 이용하였다. 예측분석결과 물류서비스 산업은 계절성을 띄며 점진적으로 상승하는 추세를 보였으며, 인과성 분석결과 과학 및 기술서비스업과 운수업은 모든 시차에서 물류서비스업과 강한 인과관계를 갖고 있었으며, 도매 및 소매업과 부동산 및 임대업은 장기시차에서 물류서비스업에 영향을 주고 있었다. 또한 금융 및 보험업은 단기시차에서 물류서비스업에 영향을 주는 산업이었다.
With the use of time-series model, this work was intended to predict the logistics service industry and analyze the causal relation between the industry and other industries. For prediction of the logistics service industry, auto-regressive moving average (ARIMA) model was used. For the analysis of the causal relation, the Granger Causality test of Vector Auto Regressive (VAR) was used. The prediction and analysis result revealed that the logistics service industry was affected by seasons and was gradually on the increase. Also the causal relation results showed that the scientific and technological service industry and the transportation industry had strong causal relation with the logistics service industry in all time-series, that the wholesale and retail sale industry and the real estate and rental industry affected the logistics service industry in long time-series, and that the finance and insurance industry affected the logistics industry in short time-series. It is expected that this work can be used as a fundamental material for setting up the direction of the logistics service, establishing its relevant policies, and making decisions, and will contribute to choosing the prediction methods in the logistics industry.