• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.553-561
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• 2012

Various statistical models to Estimate GDP (measured as a nation's economic situation) have been developed. In this paper an autoregressive distributed lag model, factor model, and a Bayesian VAR model estimate quarterly GDP as a single model; the combined estimates were evaluated to compare a single model. Subsequently, we suggest that some combined models are better than a single model to estimate quarterly GDP.

• Journal of Korean Society of Forest Science
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• v.84 no.2
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• pp.121-130
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• 1995

Forecasting accuracy is examined in the context of Michigan travel demand. Eight different annual models are used to forecast up to two years ahead, and nine different quarterly models up to four quarters. In the evaluation of annual models' performance, multiple regression performed better than the other methods in both the one year and two year forecasts. For quarterly models, Winters exponential smoothing and the Box-Jenkins method performed better than naive 1 s in the first quarter ahead, but these methods in the second, third, and fourth quarters ahead performed worse than naive 1 s. The sophisticated models did not outperform simpler models in producing quarterly forecasts. The best model, multiple regression, performed slightly better when fitted to quarterly rather than annual data: however, it is not possible to strongly recommend quarterly over annual models since the improvement in performance was slight in the case of multiple regression and inconsistent across the other models. As one would expect, accuracy declines as the forecasting time horizon is lengthened in the case of annual models, but the accuracy of quarterly models did not confirm this result.

• International Commerce and Information Review
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• v.16 no.3
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• pp.73-96
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• 2014

The purpose of this research is to evaluate a short-term export demand forecasting model reflecting individual passenger vehicle brands and market characteristics by using Vector Autoregressive (VAR) models that are based on multivariate time-series model. The short-term export demand forecasting model was created by discerning theoretical potential factors that affect the short-term export demand of individual passenger vehicle brands. Quarterly short-term export demand forecasting model for two Korean small vehicle brands (Accent and Avante) were created by using VAR model. Predictive value at t+1 quarter calculated with the forecasting models for each passenger vehicle brand and the actual amount of sales were compared and evaluated by altering subject period by one quarter. As a result, RMSE % of Accent and Avante was 4.3% and 20.0% respectively. They amount to 3.9 days for Accent and 18.4 days for Avante when calculated per daily sales amount. This shows that the short-term export demand forecasting model of this research is highly usable in terms of prediction and consistency.

• Journal of Families and Better Life
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• v.20 no.1
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• pp.45-56
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• 2002

The main purposes of this article are to introduce the theoretical backgrounds and empirical application methods of two different Models for the function of expenditure, and to compare the goodness-o(-fit of the two models: a single-equation model and a complete-system-of-demand-equation model. For the empirical analysis of the single-equation model, a linear formula and a double-leg formula were employed. In order to test the complete-system-of-demand-equation model empirically, the \"Linear Approximation/Almost Ideal Demand System (LA/AIDS)" was used. The independent variables were the total living expense and expenditure categories Price index. The data used in this study were obtained from the quarterly statistics of "The Annual Report on the Urban Family Income and Expenditure Survey (Dosigagyeyonbo)" and "The Annual Report on the Consumer Price Index (Sobijamulgajaryo)," for the years 1994 to 1997. The goodness-of-fit (R-square) was higher with the complete-system-of-demand-equation model than with the single-equation model for the budget share on food (excluding eating-out expenses) and for the share on cultural and recreational activities. However, there was no difference between the two models in terms of the proportion of the expenditure on automobile fuel.fuel.

• The Journal of Asian Finance, Economics and Business
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• v.3 no.2
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• pp.33-39
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• 2016

This study empirically evaluates the impact of exchange rate volatility, foreign direct investment, terms of trade, inflation, and industrial production and foreign exchange reserves on Pakistani trade volume over the period of 1975-2010 using quarterly data set. The study employs financial econometrics methods such as Augmented Dickey Fuller (ADF) test GARCH (1, 1) technique and Almon Polynomial Distributed Lag (APDL) models to estimate the relationship of variables. Findings of the study are in accordance with theoretical relationships presented by Clark, Tamirisa, Wei, Sadikov, & Zeng (2004), McKenzie (1999), Dellas & Zilberfarb (1993) and Côté (1994). These findings are also in accordance with the empirical studies which support positive relationship of exchange rate volatility and exports presented by Hsu & Chiang (2011), Chit (2008), Feenstra & Kendall (1991), Esquivel & Larraín (2002) and Onafowora & Owoye (2008). Findings of the study in terms of imports are supported by the studies such as Lee (1999), Alam & Ahmad (2011) and Arize (1998). The study also recommends some very important policy prescriptions.

• The Journal of Asian Finance, Economics and Business
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• v.1 no.3
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• pp.17-28
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• 2014

The paper deals with the impact of the product distribution and information technology sectors on energy resource use, carbon emissions and economic growth by examining the long-run equilibrium relationships and Granger causal relationships among these variables in South Korea. The quarterly time series data from the first quarter of 1970 to the third quarter of 2010 (163 observations) are collected and retrieved from the Bank of Korea database. The paper examines the long-run equilibrium relationships using cointegration techniques and Granger causality using vector error correction models. Test results indicate a long-run equilibrium relationship exists among these variables. In testing directional causality, both the product distribution and the information technology sectors show direct effects on economic growth but only marginal effects on carbon emissions.

• Journal of Integrative Natural Science
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• v.1 no.2
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• pp.127-132
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• 2008

This project follows the heterogeneous agent market segmented model of Landon-Lane and Occhino (2007) with using Korean data, M1 and GDP deflator from 1882:I to 2007:II. This paper estimates parameters with Monte Carlo Markov Chain. The fraction of traders, ${\lambda}$, in Korea is 15.64%. The quarterly preferences discount factor's, ${\beta}$, posterior mean is 0.9922. The posterior mean of the inverse of the elasticity of the labor supply to the real wage, ${\varphi}$, is 0.0316. The elasticity of the labor supply to the real wage has a very large value. By Hansen (1985) and Christiano and Eichenbaum (1992) and Cooley and Hansen (1989), models having large elasticity of the aggregate labor supply better match macroeconomic data.

• Journal of Distribution Science
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• v.10 no.6
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• pp.7-15
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• 2012

The paper deals with the impact of information and communications technology on carbon emissions and economic growth in South Korea. The quarterly time series data from the first quarter of 1970 to the third quarter of 2010 (163 observations) are collected and retrieved from the Bank of Korea database. The paper examines long-run equilibrium relationships using cointegration techniques and Granger causality with vector error correction models. In directional causality tests, information and communications technology shows highly significant positive effects on economic growth and marginal effect on carbon emissions. Carbon emissions and economic growth exhibit an inverse relationship with each other; that is, carbon emissions have an inverse relation to economic growth and economic growth does not significantly affect carbon emissions in South Korea. We also note possible implications regarding growth policies and the information communications technology and "green" technology sectors for economies in the range represented by Korea's 1970 - 2010 data.

• Journal of the Korea Safety Management & Science
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• v.19 no.4
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• pp.87-94
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• 2017

The purpose of this study is to provide basic data to help ensure the safety and enhance industrial competitiveness of plant construction projects by analyzing the safety management status of, mainly, chemical engineering plant construction projects, and proposing specific measures and models to reduce human/educational, technical/systemic, institutional disasters. This study was done using literature research and case study/empirical study methods. The results of this study are summarized as follows. First, we classified the major disasters from the quarterly released 'major diaster cases in construction business' from Korea Occupational Safety and Health Agency according to the type of construction and presented the causes and prevention measures.

• Korean Security Journal
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• no.30
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• pp.139-169
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• 2012

The purpose of this study is to contribute to establishing the scientific policing policies through deriving the time series models that can forecast the occurrence of major crimes such as murder, robbery, burglary, rape, violence and identifying the occurrence of major crimes using the models. In order to achieve this purpose, there were performed the statistical methods such as Generation of Time Series Model(C) for identifying the forecasting models of time series, Generation of Time Series Model(C) and Sequential Chart of Time Series(N) for identifying the accuracy of the forecasting models of time series on the monthly incidence of major crimes from 2002 to 2010 using IBM PASW(SPSS) 19.0. The following is the result of the study. First, murder, robbery, rape, theft and violence crime's forecasting models of time series are Simple Season, Winters Multiplicative, ARIMA(0,1,1)(0,1,1), ARIMA(1,1,0 )(0,1,1) and Simple Season. Second, it is possible to forecast the short-term's occurrence of major crimes such as murder, robbery, burglary, rape, violence using the forecasting models of time series. Based on the result of this study, we have to suggest various forecasting models of time series continuously, and have to concern the long-term forecasting models of time series which is based on the quarterly, yearly incidence of major crimes.