• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.61-73
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• 2016

We consider the wild bootstrap Ljung-Box (LB) test for autocorrelation in residuals of fitted multivariate time series models. The asymptotic chi-square distribution under the IID assumption is traditionally used for the LB test; however, size distortion tends to occur in the usage of the LB test, due to the conditional heteroskedasticity of financial time series. In order to overcome such defects, we propose the wild bootstrap LB test for autocorrelation in residuals of fitted vector autoregressive and error correction models. The simulation study and real data analysis are conducted for finite sample performance.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1085-1092
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• 2009

The ozone data is one of the important environmental data for measurement of the atmospheric condition of the country. In this article, the Autoregressive Error (ARE) model and Neural Networks (NN) model have been considered for analyzing the ozone data at the northern part of the Gyeonggi-Do, Paju monitoring site in Korea. In the both ARE model and NN model, seven meteorological variables and four pollution variables are used as the explanatory variables for the ozone data set. The seven meteorological variables are daily maximum temperature, wind speed, relative humidity, rainfall, dew point temperature, steam pressure, and amount of cloud. The four air pollution explanatory variables are Sulfur dioxide ($SO_2$), Nitrogen dioxide ($NO_2$), Cobalt (CO), and Promethium 10 (PM10). The result showed that the NN model is generally better suited for describing the ozone concentration than the ARE model. However, the ARE model will be expected also good when we add the explanatory variables in the model.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1041-1049
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• 2010

In Korean professional baseball, attendance is the largest source of revenue for development of professional baseball and the highest concern of professional baseball teams. So, if there is demand forecasting model, it will be helpful for pennant chasers to work out the strategies for drawing attendance. For this reason, this research intends to suggest the model which estimates Korean professional baseball's attendance and uses all usable variables which have an effect on attendance in limited circumstances. We supposed that dependent variable is attendance as well as several independent variables and error term are homoscedastic variance. And then, we compared the models which assume conditional heteroscedastic variance like GARCH and EGARCH with GARCH-t models which use the assumption that error term's distribution follows student-t distribution. In result of that, we could confirm that the models which were made by using GARCH(1,1)-t made estimates the most accurately among the several models considered.

• Proceedings of the Korea Water Resources Association Conference
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• 2003.05b
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• pp.791-794
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• 2003

• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.791-799
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• 2015

In random walk hypothesis, we assume that current change of financial time series is independent of past values. It is interpreted as an existency of a unit root in ARMA models and many researches have been focused on whether ${\rho}$ < 1 or not. If some financial data are generated from an explosive autoregressive model, the chance of a bubble economy increases. We have to find the symptoms of it in advance. Since some well-known parameter estimators contain the parameter itself and other statistic is constructed under a specific parameter structure assumption, those are difficut to be adopted. In this paper we investigate a test for explosive autoregressive models using slope signs. We found the properties of the slope sign test statistic under both independent error and correlated error conditions, mainly by simulations.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.1039-1047
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• 2014

An autoregressive model with normal errors is a natural model that attempts to fit time series data. More flexible models that include normal distribution as a special case are necessary because they can cover normality to non-normality models. The skewed exponential power distribution is a possible candidate for autoregressive models errors that may have tails lighter(platykurtic) or heavier(leptokurtic) than normal and skewness; in addition, the use of skewed exponential power distribution can reduce the influence of outliers and consequently increases the robustness of the analysis. We use SIR algorithm and grid method for an efficient Bayesian estimation.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.605-619
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• 1999

본 논문은 회귀분석에서 오차항의 1차 자기상관 존재 여부 및 그 값을 검정하는 방법을 베이지안 접근법으로 제안하였다. 이 방법은 모수공간의 다중분할로 인해 얻어진 여러 가설들에 대한 다중결정문제를 다중 베이즈요인에 관한 이론과 일반화 Savage-Dickey 밀도비를 이용한 사후확률 추정법을 합성하여 개발되었다. 이 방법은 기존의 검정법들에서 가능한 검정 뿐 아니라 이들이 해결할 수 없는 자기상관에 대한 다중결정문제에도 사용이 가능한데 그 효용성이 있다. 모의실험을 통하여 제안된 검정법의 유효성을 평가하였다.

• Korea Real Estate Review
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• v.24 no.1
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• pp.7-14
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• 2014

This study, as the temporal and spatial data for the real price apartment in Seoul from January 2006 to June 2013, empirically compared and analyzed the estimation result of apartment price using OLS by hedonic price model for the problem of space-time correlation, temporal autoregressive model (TAR) considering temporal effect, spatial autoregressive model (SAR) spatial effect and spatiotemporal autoregressive model (STAR) spatiotemporal effect. As a result, the adjusted R-square of STAR model was increased by 10% compared that of OLS model while the root mean squares error (RMSE) was decreased by 18%. Considering temporal and spatial effect, it is observed that the estimation of apartment price is more correct than the existing model. As the result of analyzing STAR model, the apartment price is affected as follows; area for apartment(-), years of apartment(-), dummy of low-rise(-), individual heating (-), city gas(-), dummy of reconstruction(+), stairs(+), size of complex(+). The results of other analysis method were the same. When estimating the price of real estate using STAR model, the government officials can improve policy efficiency and make reasonable investment based on the objective information by grasping trend of real estate market accurately.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.839-847
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• 2011

In this study, we derive an estimator based on autocovariance for the regression coefficients vector in the multiple linear regression model. This method is suggested by Park (2009), and although this method does not seem to be intuitively attractive, this estimator is unbiased for the regression coefficients vector. When the vectors of exploratory variables satisfy some regularity conditions, under mild conditions which are satisfied when errors are from autoregressive and moving average models, this estimator has asymptotically the same distribution as the least squares estimator and also converges in probability to the regression coefficients vector. Finally we provide a simulation study that the forementioned theoretical results hold for small sample cases.