• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.243-255
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• 2016

The developed composite index has limits to estimate and predict economic status due to economic pattern change and the response change of explanatory variables. A higher precedence individual indicators should be selected to predict the future accurately. In this study, effectiveness of Jeju Island precedence indicators consists of constituents in the area, the consumer price index, services production index, mining and manufacturing production index. The average temperature of Seogwipo and credit card purchase amount is reviewed as an economic turning point consideration and time lag correlation analysis with real data. In addition, we suggest the proper reference cycle in Jeju composite precedence index and evaluate the configuration in leading indicators for Jeju by comparing national economic indicators. Based on the derived results, the current problems of Jeju Island precedence indicators will be illustrated and the improvement methods to estimate a regional composite index will be suggested.

• Journal of Korean Society of Transportation
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• v.33 no.1
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• pp.61-69
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• 2015

This study compares the forecasting accuracy of five econometric models on domestic total freight volume in South Korea. Applied five models are as follows: Ordinary Least Square model, Partial Adjustment model, Reduced Autoregressive Distributed Lag model, Vector Autoregressive model, Time Varying Parameter model. Estimating models and forecasting are carried out based on annual data of domestic freight volume and an index of industrial production during 1970~2011. 1-year, 3-year, and 5-year ahead forecasting performance of five models was compared using the recursive forecasting method. Additionally, two forecasting periods were set to compare forecasting accuracy according to the size of future volatility. As a result, the Time Varying Parameter model showed the best accuracy for forecasting periods having fluctuations, whereas the Vector Autoregressive model showed better performance for forecasting periods with gradual changes.