• Communications for Statistical Applications and Methods
• /
• 제30권3호
• /
• pp.273-289
• /
• 2023

In this paper, we develop a new time series model for predicting IPO (initial public offering) data with non-negative integer value. The proposed model is based on integer-valued autoregressive (INAR) model with a Poisson thinning operator. Just as the heterogeneous autoregressive (HAR) model with daily, weekly and monthly averages in a form of cascade, the integer-valued heterogeneous autoregressive (INHAR) model is considered to reflect efficiently the long memory. The parameters of the INHAR model are estimated using the conditional least squares estimate and Yule-Walker estimate. Through simulations, bias and standard error are calculated to compare the performance of the estimates. Effects of model fitting to the Korea's IPO are evaluated using performance measures such as mean square error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) etc. The results show that INHAR model provides better performance than traditional INAR model. The empirical analysis of the Korea's IPO indicates that our proposed model is efficient in forecasting monthly IPO volumes.

• Journal of the Korean Data and Information Science Society
• /
• 제23권6호
• /
• pp.1093-1102
• /
• 2012

본 연구에서는 다변량시계열모형인 VAR (vector autoregressive regression)모형에 의하여 금리 스프레드의 시계열예측을 수행하였다. 국내외 거시경제변수들 중에서 교차상관분석 및 그랜져인과 검정을 통하여 상호간에 설명력이 있는 변수들을 추출하여 VAR모형의 시계열변수로 사용하였다. 마지막 12개월의 예측치에 대한 MAPE (mean absolute percentage error)와 RMSE (root mean square error)에 근거하여 모형의 예측력을 단일변량 시계열모형인 AR (autoregressive regression) 모형과 비교하였다.

• Journal of Communications and Networks
• /
• 제13권6호
• /
• pp.621-624
• /
• 2011

With the rapid growth of internet traffic, accurate and reliable prediction of internet traffic has been a key issue in network management and planning. This paper proposes an autoregressive-generalized autoregressive conditional heteroscedasticity (AR-GARCH) error model for forecasting internet traffic and evaluates its performance by comparing it with seasonal autoregressive integrated moving average (ARIMA) models in terms of root mean square error (RMSE) criterion. The results indicated that the seasonal AR-GARCH models outperformed the seasonal ARIMA models in terms of forecasting accuracy with respect to the RMSE criterion.

• 한국산림과학회지
• /
• 제97권2호
• /
• pp.140-143
• /
• 2008

This study compared the plywood demand prediction accuracy of econometric and vector autoregressive models using Korean data. The econometric model of plywood demand was specified with three explanatory variables; own price, construction permit area, dummy. The vector autoregressive model was specified with lagged endogenous variable, own price, construction permit area and dummy. The dummy variable reflected the abrupt decrease in plywood consumption in the late 1990's. The prediction accuracy was estimated on the basis of Residual Mean Squared Error, Mean Absolute Percentage Error and Theil's Inequality Coefficient. The results showed that the plywood demand prediction can be performed more accurately by econometric model than by vector autoregressive model.

• Journal of applied mathematics & informatics
• /
• 제9권1호
• /
• pp.289-301
• /
• 2002

This paper considers the Bayesian analysis of the regression model wish autoregressive errors. The Bayesian approach for finding the order p of autoregressive error is proposed and the proposed method can be simplified by generalized Savage-Dicky density ratio(Verdinelli and Wasser-man, [18]). And the Markov chain Monte Carlo method(Gibbs sample, [7]) is used in order to overcome the difficulty of Bayesian computations. Final1y, several examples are used to illustrate our proposed methodology.

• 수산경영론집
• /
• 제46권1호
• /
• pp.93-107
• /
• 2015

This study aims to analyze causalities among Hairtail prices by distribution channel using a vector autoregressive model. This study applies unit-root test for stability of data, uses Granger causality test to know interaction among Hairtail Prices by distribution channel, and employes the vector autoregressive model to estimate statistical impacts among t-2 period variables used in model. Analyzing results of this study are as follows. First, ADF, PP, and KPSS tests show that the change rate of Hairtail price by distribution channel differentiated by logarithm is stable. Second, a Granger causality test presents that the producer price of Hairtail leads the wholesale price and then the wholesale price leads the consumer price. Third, the vector autoregressive model suggests that the change rate of Hairtail producer price of t-2 period variables statistically, significantly impacts change rates of own, wholesale, and consumer prices at current period. Fourth, the impulse response analysis indicates that impulse responses of the structural shocks with a respectively distribution channel of the Hairtail prices are relatively more powerful in own distribution channel than in other distribution channels. Fifth, a forecast error variance decomposition of the Hairtail prices points out that the own price has relatively more powerful influence than other prices.

• 한국수자원학회논문집
• /
• 제37권1호
• /
• pp.77-86
• /
• 2004

본 연구에서는 농업용 저수지에서 저수량 예측모형과 함께 저수지의 목표저수량 및 한계저수량을 유지하기 위한 저수지 운영방안을 제시하였다. 대상저수지인 금강저수지에서 1990년부터 200l년까지의 저수량 자료를 이용하여 갈수빈도해석을 적용하고, 2년빈도 한발저수량을 목표저수량(target storage)으로, 10년빈도 한발저수량을 한계저수량(critical storage)으로 설정하였다. 농업용 저수지의 운영의 효율화를 위해서는 우선 합리적인 방법을 통하여 장래 저수량을 예측하여야 한다. 예측된 저수량은 저수지 운영에 관한 계획을 수립하는데 기초자료로 활용될 수 있다. 본 연구에는 저수량 예측모형으로 ARIMA 모형과 자기회귀오차모형을 적용하였다. ARIMA 모형은 과거 저수량 자료만을 근거로 하여 저수량을 예측함으로서 예측정도가 상대적으로 낮은 것으로 나타난 반면, 자기회귀오차모형은 저수량과 관련 있는 설명변수들을 이용함으로써 예측의 효과를 높일 수 있었다. 농업용 저수지의 저수량은 이전 저수량, 강수량, 평균온도, 최고온도, 관개면적, 풍속, 습도의 영향을 받으므로 자기회귀오차모형을 적용하여 저수량과 저수량에 영향을 미치는 요인과의 관계를 분석하였다. 자기회귀오차모형에 의한 저수량 예측 관계식은 저수지의 연속방정식과 유사한 관계식으로 유도되어 실제 적용성이 높을 것으로 판단되며, 금광저수지에서 예측된 2002년도 저수량과 관측된 저수량을 비교한 결과, 양호한 예측결과를 보여 주었다.

• Communications for Statistical Applications and Methods
• /
• 제8권3호
• /
• pp.777-786
• /
• 2001

The autoregressive models have been used to describe a wade variety of time series. Then the problem of determining the order in the times series model is very important in data analysis. We consider the Bayesian approach for finding the order of autoregressive(AR) error models using the latent variable which is motivated by Tanner and Wong(1987). The latent variables are combined with the coefficient parameters and the sequential steps are proposed to set up the prior of the latent variables. Markov chain Monte Carlo method(Gibbs sampler and Metropolis-Hasting algorithm) is used in order to overcome the difficulties of Bayesian computations. Three examples including AR(3) error model are presented to illustrate our proposed methodology.

• 응용통계연구
• /
• 제35권1호
• /
• pp.93-104
• /
• 2022

S&P 500과 RUSSELL 2000, DJIA, Nasdaq 100 4가지 미국 주가지수의 실현변동성(realized volatility, RV)을 예측하는데 있어서 사람들의 관심 지표로 삼을 수 있는 인터넷 검색량(search volume, SV) 지수와 내재변동성(implied volatility, IV)를 이용하여 LSTM 딥러닝(deep learning) 방법으로 RV의 예측력을 높이고자하였다. SV을 이용한 LSTM 방법의 실현변동성 예측력이 기존의 기본적인 vector autoregressive (VAR) 모형, vector error correction (VEC)보다 우수하였다. 또한, 최근 제안된 RV와 IV의 공적분 관계를 이용한 vector error correction heterogeneous autoregressive (VECHAR) 모형보다도 전반적으로 예측력이 더 높음을 확인하였다.

• Journal of the Korean Data and Information Science Society
• /
• 제27권5호
• /
• pp.1225-1239
• /
• 2016

We investigate pediatric tumor incidence data collected by the Florida Association for Pediatric Tumor program using various models commonly used in disease mapping analysis. Particularly, we consider Poisson normal models with various conditional autoregressive structure for spatial dependence, a zero-in ated component to capture excess zero counts and a spatio-temporal model to capture spatial and temporal dependence, together. We found that intrinsic conditional autoregressive model provides the smallest Deviance Information Criterion (DIC) among the models when only spatial dependence is considered. On the other hand, adding an autoregressive structure over time decreases DIC over the model without time dependence component. We adopt weighted ranks squared error loss to identify high risk regions which provides similar results with other researchers who have worked on the same data set (e.g. Zhang et al., 2014; Wang and Rodriguez, 2014). Our results, thus, provide additional statistical support on those identied high risk regions discovered by the other researchers.