• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.419-430
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• 2014

An accurate prediction of electricity supply and demand is important for daily life, industrial activities, and national management. In this paper electricity sales is predicted by time series modelling. Real data analysis shows the transfer function model with cooling and heating days as an input time series and a pulse function as an intervention variable outperforms other time series models for the root mean square error and the mean absolute percentage error.

• Journal of the Korean Society of Child Welfare
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• no.40
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• pp.201-228
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• 2012

The purpose of this research was to investigate the mediation effect of bullying on the associations between children's overweight and obesity problem and mental health problems. Data from a subsample of 2,306 adolescents, who participated in the "child-youth synthesize survey" was utilized. The measurement and structural models were estimated using structural equation modeling. Partial and full mediation models were compared, and X2 difference test was conducted between the two models. The study results show that children's overweight and obesity problem have a direct effect on mental health problems. In addition, bullying was found to mediate the association between children's overweight and obesity problem and mental health problems. The analytic results confirmed that the model fit for the full mediation model was better than the partial mediation model when examining the mediating effect of bullying on the relations between children's overweight and obesity problem and mental health problems. Efforts to prevent mental health problems may require interventions for children's with overweight and obesity problems as well as inventions for reducing bullying in general.

• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.237-252
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• 2013

An estimation model for premiums and components is essential to determine reasonable insurance premiums. In this study, we introduce diverse models for the estimation of property damage premiums(premium, depth and frequency) that include a regression model using a dummy variable, additive independent variable model, autoregressive error model, seasonal ARIMA model and intervention model. In addition, the actual property damage premium data was used to estimate the premium, depth and frequency for each model. The estimation results of the models are comparatively examined by comparing the RMSE(Root Mean Squared Errors) of estimates and actual data. Based on real data analysis, we found that the autoregressive error model showed the best performance.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.173-185
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• 2019

Causal questions are prevalent in scientific research, for example, how effective a treatment was for preventing an infectious disease, how much a policy increased utility, or which advertisement would give the highest click rate for a given customer. Causal inference theory in statistics interprets those questions as inferring the effect of a given intervention (treatment or policy) in the data generating process. Causal inference has been used in medicine, public health, and economics; in addition, it has received recent attention as a tool for data-driven decision making processes. Many recent datasets are observational, rather than experimental, which makes the causal inference theory more complex. This review introduces key concepts and recent trends of statistical causal inference in observational studies. We first introduce the Neyman-Rubin's potential outcome framework to formularize from causal questions to average treatment effects as well as discuss popular methods to estimate treatment effects such as propensity score approaches and regression approaches. For recent trends, we briefly discuss (1) conditional (heterogeneous) treatment effects and machine learning-based approaches, (2) curse of dimensionality on the estimation of treatment effect and its remedies, and (3) Pearl's structural causal model to deal with more complex causal relationships and its connection to the Neyman-Rubin's potential outcome model.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.257-267
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• 2000

The purpose of this study is to analyse the economic effects of General Elections in Korea, in other words, to test the Political Business Cycle hypothesis using ARIMA-Intervention Analysis.

• 한국사회복지학회:학술대회논문집
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• 1994.11a
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• pp.111-121
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• 1994

• Korean Journal of Social Welfare
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• v.30
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• pp.165-198
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• 1996

• Journal of Korea Port Economic Association
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• v.26 no.1
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• pp.222-233
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• 2010

This paper aims at predicting the BDI from Jan. to Dec. 2010 using such econometric techniues of the univariate time series as stochastic ARIMA-type models and Hodrick-Prescott filtering technique. The multivariate cause-effect econometric model is not employed for not assuring a higher degree of forecasting accuracy than the univariate variable model. Such a cause-effect econometric model also fails in adjusting itself for the post-sample. This article introduces the two ARIMA models and five Intervention-ARIMA models. The monthly data cover the period January 2000 through December 2009. The out-of-sample forecasting performance is compared between the ARIMA-type models and the random walk model. Forecasting performance is measured by three summary statistics: root mean squared error (RMSE), mean absolute error (MAE) and mean error (ME). The RMSE and MAE indicate that the ARIMA-type models outperform the random walk model And the mean errors for all models are small in magnitude relative to the MAE's, indicating that all models don't have a tendency of overpredicting or underpredicting systematically in forecasting. The pessimistic ex-ante forecasts are expected to be 2,820 at the end of 2010 compared with the optimistic forecasts of 4,230.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.4
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• pp.18-26
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• 2015

This study is a fundamental research to suggest a forecasting model for short-term railway passenger demand focusing on major lines (Gyeungbu, Honam, Jeonla, Janghang, Jungang) of Saemaeul rail and Mugunghwa rail. Also the author tried to verify the potential application of the proposed models. For this study, SARIMA model considering characteristics of seasonal trip is basically used, and daily mean forecasting models are independently constructed depending on weekday/weekend in order to consider characteristics of weekday/weekend trip and a legal holiday trip. Furthermore, intervention events having an impact on using the train such as introduction of new lines or EXPO are reflected in the model to increase reliability of the model. Finally, proposed models are confirmed to have high accuracy and reliability by verifying predictability of models. The proposed models of this research will be expected to utilize for establishing a plan for short-term operation of lines.

• Journal of Korea Port Economic Association
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• v.27 no.4
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• pp.1-11
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• 2011

In much the same way as the US Lehman crisis of 2008-2009 severely impacted the European economy through financial market dislocation, a European banking crisis would materially impact the US economy through a generalized increase in global risk aversion. A deepening of the European crisis could very well derail the US economic recovery and have a harmful impact on the Asian economies. This kind of vicious circle could be a bad news to the shipping companies. The purpose of the study is to predict the Baltic Dry Index representing the shipping business during the period of 2012 using the ARIMA-type models. This include the ARIMA and Intervention-ARIMA models. This article introduces the four ARIMA models and six Intervention-ARIMA models. The monthly data cover the period January 2000 through October 2011. The out-of-sample forecasting performance is also calculated. Forecasting performance is measured by three summary statistics: root mean squared percent error, mean absolute percent error and mean percent error. The root mean squared percent errors, however, are somewhat higher than normally expected. This reveals that it is very difficult to predict the BDI The ARIMA-type models show that the shipping market will be bearish in 2012. These pessimistic ex-ante forecasts are supported by the Hodrick-Prescott filtering technique.