• The Journal of Asian Finance, Economics and Business
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• v.9 no.1
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• pp.167-176
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• 2022

By analyzing the impact of oil prices on economic growth, this study has shown a new insight into the link between oil price inflation and economic growth. The primary goal of this study is to determine if oil prices are pro-growth or anti-growth. To provide empirical proof, the series data for both the core and control variables from 1972 to 2020 was used to justify the association on empirical grounds. To account for the presence of a unit root, the Augmented Dickey-Fuller Test was used, and after making the series compatible for co-integration, the Autoregressive distributed lag model was used to determine the empirical estimate. Additionally, the empirical models were used to diagnose heteroscedasticity and autocorrelation. The reference point model reveals that in developing nations like Pakistan, economic growth is anti-growth with an increase in prices, and it responds negatively to economic growth in the long and short run. As a result, oil price inflation in Pakistan fails to have a significant beneficial impact on economic growth in both the long and short run, but it does raise the general price level in the economy.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.925-937
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• 2008

In this study, we considered the clustering analysis for stock return traded in the stock market. Most of financial time-series data, for instance, stock price and exchange rate have conditional heterogeneous variability depending on time, and, hence, are not properly applied to the autoregressive moving-average(ARMA) model with assumption of constant variance. Moreover, the variability is font and center for stock investors as well as academic researchers. So, this paper focuses on the generalized autoregressive conditional heteroscedastic(GARCH) model which is known as a solution for capturing the conditional variance(or volatility). We define the metrics for similarity of unconditional volatility and for homogeneity of model structure, and, then, evaluate the performances of the metrics. In real application, we do clustering analysis in terms of volatility and structure with stock return of the 11 Korean companies measured for the latest three years.

• Environmental and Resource Economics Review
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• v.13 no.2
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• pp.175-194
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• 2004

Volumes of research have been implemented to estimate and predict the oil price. These models, however, fail in accurately predicting oil price as a model composed of only a few observable variables is limiting. Unobservable variables and news that have been overlooked in past research, yet have a high likelihood of affecting the oil price. Hence, this paper analyses the news impact on the price. The standard GARCH model fails in capturing some important features of the data. The estimated news impact curve for the GARCH model, which imposes symmetry on the conditional variances, suggests that the conditional variance is underestimated for negative shocks and overestimated for positive shocks. Hence, this paper introduces the asymmetric or leverage volatility models, in which good news and bad news have different impact on volatility. They include the EGARCH, AGARCH, and GJR models. The empirical results showed that negative shocks introduced more volatility than positive shocks. Overall, the AGARCH and GJR were the best at capturing this asymmetric effect. Furthermore, the GJR model successfully revealed the shape of the news impact curve and was a useful approach to modeling conditional heteroscedasticity.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.393-405
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• 2000

Box and Draper( 19(5) listed some properties of a design that should be considered in design selection. But it is impossible that one design criterion from optimal experimental design theory reflects many potential objectives of an experiment, because the theory was originally based on the underlying model and its strict assumption about the error structure. Therefore, when it is neces::;ary to implement multi-objective experimental design. it is common practice to balance out the several optimal design criteria so that each design criterion involved benefits in terms of its relative "high" efficiency. In this study, we proposed several composite design criteria taking the case of heteroscedastic model. WVhen the heteroscedasticity is present in the model. the well known equivalence theorem between 1)- and C-optimality no longer exists and furthermore their design characteristics are sometimes drastically different. We introduced three different design criteria for this purpose: constrained design, combined design, and minimax design criteria. While the first two methods do reflect the prior belief of experimenter, the last one does not take it into account. which is sometimes desirable. Also we extended this method to the case when there are uncertainties concerning the error structure in the model. A simple algorithm and concluslOn follow.On follow.

• The Korean Journal of Applied Statistics
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• v.31 no.3
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• pp.397-408
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• 2018

Various methods for accurate parameter estimation have been developed in a sample survey and it is also common to use a ratio estimator or the regression estimator using auxiliary information. The ratio-type estimator has been used in many recent studies and is known to improve the accuracy of estimation by adjusting the ratio estimator. However, various studies are under way to solve it since the ratio-type estimator is biased. In this study, we propose a generalized ratio-type estimator with a new parameter added to the ratio-type estimator to remove the bias. We suggested a method to apply this result to the parameter estimation under the error assumption of heteroscedasticity. Through simulation, we confirmed that the suggested generalized ratio-type estimator gives good results compared to conventional ratio-type estimators.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.547-557
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• 2017

The health-related quality of life data are commonly skewed and bounded with spike at the perfect health status, and the variance tended to be heteroscedastic. In this study, we have developed a prediction model for EQ-5D using linear regression model, beta regression model, and extended beta regression model with mean and precision submodel, and also compared the predictive accuracy. The extended beta regression model allows to model skewness and differences in dispersion related to covariates. Although the extended beta regression model has higher prediction accuracy than the linear regression model, the overlapped confidence intervals suggested that the extended beta regression model was superior to the linear regression model. However, the expended beta regression model could explain the heteroscedasticity and predict within the bounded range. Therefore, the expended beta regression model are appropriate for fitting the health-related quality of life data such as EQ-5D.

• Wind and Structures
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• v.31 no.3
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• pp.269-285
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• 2020

The statistical characteristics of typhoon wind speed records tend to have a considerable time-varying trend; thus, the stationary wind model may not be appropriate to estimate the wind characteristics of typhoon events. Several nonstationary wind speed models have been proposed by pioneers to characterize wind characteristics more accurately, but comparative studies on the applicability of the different wind models are still lacking. In this study, three landfall typhoons, Ampil, Jongdari, and Rumbia, recorded by ultrasonic anemometers atop the Shanghai World Financial Center (SWFC), are used for the comparative analysis of stationary and nonstationary wind characteristics. The time-varying mean is extracted with the discrete wavelet transform (DWT) method, and the time-varying standard deviation is calculated by the autoregressive moving average generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model. After extracting the time-varying trend, the longitudinal wind characteristics, e.g., the probability distribution, power spectral density (PSD), turbulence integral scale, turbulence intensity, gust factor, and peak factor, are comparatively analyzed based on the stationary wind speed model, time-varying mean wind speed model and time-varying standard deviation wind speed model. The comparative analysis of the different wind models emphasizes the significance of the nonstationary considerations in typhoon events. The time-varying standard deviation model can better identify the similarities among the different typhoons and appropriately describe the nonstationary wind characteristics of the typhoons.

• Korea Journal of Hospital Management
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• v.22 no.2
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• pp.58-69
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• 2017

Recently, with the increase in the number of private health insurance subscribers, interest in overuse of the medical service is increasing. This study analyzed the impacts of private health insurance (PHI) on medical institution selection in outpatient service utilization among persons with arthritis. In order to control patients' health status, we extracted outpatient episodes with the same disease (KCD6, M13) from Korea Health Panel. The unit of analysis was an outpatient visit with arthritis in 2014 (n=23,363). In the light of insurance coverage, we redefined three type of private health insurance (ex, indemnity, fixed benefit, and non-insured) as a test variable and two type of medical institution (ex, hospital and physician visit) as a dependent variable. We conducted a probit regression analysis to identify the impacts of PHI on medical institution selection controlling for heteroscedasticity. The results of this study showed that the insured with indemnity were more likely to choose hospital departments than clinics (marginal effect=0.0475, p=0.000). The impact of participation of fixed benefit PHI was not as clear as that of indemnity type (marginal effect=0.0162, p=0.047). In conclusion, this study confirmed that PHI, particularly indemnity type has a significant impact on the selection of medical institutions. Healthcare policy makers should consider that PHI not only affects the overall quantitative increase in healthcare utilization, but also influences the selection of medical institutions.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.2
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• pp.173-179
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• 2018

Estimating reliability of a non-repairable system using the degradation data, variance assumption such as homogeneity (constant) or heteroscedasticity (time-variant) could affect accuracy of reliability estimation. This paper showed reliability estimation and comparison results under normal conditions using accelerated degradation data obtained from destructive measurements, according to variance assumption of the data at each measurement time. Degradation data from three accelerated conditions with stress factors of temperature and humidity were used to estimate reliability. The $B_{10}$ lifetime was estimated as 1243.8 years by constant variance assumption, and 18.9 years by time-variant variance. And variance assumption provided different analysis results of important stresses to reliability. Thus, accurate assumption of variance at each measurement time is required when estimating reliability using degradation data of a non-repairable system.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.1013-1024
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• 2015

We compare the performance of two representative estimation methods for the dynamic conditional correlation (DCC) GARCH model. The first method is the pairwise estimation which exploits partial information from the paired series, irrespective to the time series dimension. The second is the multi-dimensional estimation that uses full information of the time series. As a simulation for the comparison, we generate a multivariate time series similar to those observed in real markets and construct a DCC GARCH model. As an empirical example, we constitute various portfolios using real KOSPI 200 sector indices and estimate volatility and VaR of the portfolios. Through the estimated dynamic correlations from the simulation and the estimated volatility and value at risk (VaR) of the portfolios, we evaluate the performance of the estimations. We observe that the multi-dimensional estimation tends to be superior to pairwise estimation; in addition, relatively-uncorrelated series can improve the performance of the multi-dimensional estimation.