• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 추계학술대회 논문집 학회본부 A
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• pp.184-186
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• 2000

This paper proposes the introduction of TAR(Threshold Autoregressive) model for short-term load forecasting including temperature variable. TAR model is a piecewise linear autoregressive model. In the scatter diagram of daily peak load versus daily maximum or minimum temperature, we can find out that the load-temperature relationship has a negative slope in lower regime and a positive slope in upper regime due to the heating and cooling load, respectively. In this paper, daily peak load was forecasted by applying TAR model using this load-temperature characteristic in these regimes. The results are compared with those of linear and quadratic regression models.

• Communications for Statistical Applications and Methods
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• 제29권1호
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• pp.41-51
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• 2022

We forecast the US oil consumption level taking advantage of google trends. The google trends are the search volumes of the specific search terms that people search on google. We focus on whether proper selection of google trend terms leads to an improvement in forecast performance for oil consumption. As the forecast models, we consider the least absolute shrinkage and selection operator (LASSO) regression and the structured regularization method for large vector autoregressive (VAR-L) model of Nicholson et al. (2017), which select automatically the google trend terms and the lags of the predictors. An out-of-sample forecast comparison reveals that reducing the high dimensional google trend data set to a low-dimensional data set by the LASSO and the VAR-L models produces better forecast performance for oil consumption compared to the frequently-used forecast models such as the autoregressive model, the autoregressive distributed lag model and the vector error correction model.

• 응용통계연구
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• 제18권3호
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• pp.627-637
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• 2005

시계열 모형과 공간통계 모형이 결합된 공간 시계열 모형에 관한 많은 연구가 발표되고 있다. 최근 de Luna와 Genton(2003, 2005)은 공간 시계열 모형을 이용한 실업률의 소지역 추정을 발표하는 등 공간 시계열 기법을 이용한 소지역 추정방법의 연구도 빠르게 진행되고 있다. 본 논문에서는 공간 시계열 모형을 이용한 소지역 추정기법을 설명하고 이 기법과 신기 일과 이상은(2003)이 발표한 공간모형을 이용한 소지역 추정을 비교하였다.

• 한국컴퓨터정보학회논문지
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• 제24권7호
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• pp.153-160
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• 2019

This study aims to analyze the mutual influences between adolescents' multicultural acceptability and school adjustment. Research problems for research are as follows. First, is multicultural acceptability and school adjustment stable over time? Second, what is the longitudinal impact of school adjustment on multicultural acceptability over time? Third, what is the longitudinal impact of multicultural acceptability on school adjustment over time? The results of analyzing the research problems by applying the autoregressive cross-lagged model are as follows. First, the autoregressive model of school adjustment has a significant effect on the future time point and is stable over time. Second, the autoregressive model of multicultural acceptability have a significant effect on the future time point and is stable over time. Third, cross-lagged effect from school adjustment to multicultural acceptability has a statistically significant effect on the multicultural acceptability at a later time, and is stable over time. Fourth, cross-lagged effect from multicultural acceptability to school adjustment was not statistically significant at the time of multicultural acceptability, and there was no change with time. This study is meaningful to provide the theoretical and practical implications by verifying the influence of the three - year term data over time.

• Journal of the Korean Statistical Society
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• 제22권2호
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• pp.259-269
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• 1993

A moving average model, LMA(q) and an autoregressive-moving average model, NLARMA(p, q), with Laplacian marginal distribution are constructed and their properties are discussed; Their autocorrelation structures are completely analogus to those of Gaussian process and they are partially time reversible in the third order moments. Finally, we study the mixing property of NLARMA process.

• Journal of the Korean Statistical Society
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• 제25권4호
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• pp.529-544
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• 1996

This paper deals with the problem of estimating the autoregressive random coefficient of a first-order random coefficient autoregressive time series model applied to panel data of time series. The autoregressive random coefficients across individual units are assumed to be a random sample from a truncated normal distribution with the space (-1, 1) for stationarity. The estimates of random coefficients are obtained by an empirical Bayes procedure using the estimates of model parameters. Also, a Monte Carlo study is conducted to support the estimation procedure proposed in this paper. Finally, we apply our results to the economic panel data in Liu and Tiao(1980).

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

• Journal of the Korean Statistical Society
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• 제35권1호
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• pp.79-90
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• 2006

We consider the subdiagonal bilinear model and ARMA model with subdiagonal bilinear errors. Sufficient conditions for geometric ergodicity of associated Markov chains are derived by using results on generalized random coefficient autoregressive models and then strict stationarity and ,a-mixing property with exponential decay rates for given processes are obtained.

• 농업과학연구
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• 제38권2호
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• pp.383-391
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• 2011

Traditional international trade theory assumes that import goods and domestically produced goods of the same industry are equal in quality. However the substitutability of the two goods is imperfect. This article estimated the import functions of pulp and paper using econometric and vector autoregressive models, and calculated the elasticities of substitution between imported and domestically produced pulp and paper. The import of pulp is inelastic to import price and domestic price, and elastic to national income in econometric model. And it is inelastic to import price, domestic price and national income in vector autoregressive model. On the other hand, the import of paper is inelastic to domestic price, and elastic to import price and national income in econometric model. And it is inelastic to import price and domestic price, and elastic to national income in vector autoregressive model. The elasticity of substitution between imported and domestically produced pulp was positive, and the elasticity was respectively 0.42 and 0.20 in econometric and vector autoregressive models. This may be because of the high proportion of imports. On the other hand, the elasticity of substitution between imported and domestically produced paper was positive, and the elasticity was respectively 0.75 and 0.81 in econometric and vector autoregressive models. This may be because the quality of imported paper is different from that of domestically produced paper.

• Smart Structures and Systems
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• 제17권6호
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• pp.1017-1030
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• 2016

This study focuses on the system identification of reinforced concrete bridges using vector autoregressive model (VAR). First, the time series output response from a bridge establishes the autoregressive (AR) models. AR models are one of the most accurate methods for stationary time series. Burg's algorithm estimates the autoregressive coefficients (ARCs) at p-lag by reducing the sum of the forward and the backward errors. The computed ARCs are assembled in the state system matrix and the eigen-system realization algorithm (ERA) computes: the eigenvector matrix that contains the vectors of the mode shapes, and the eigenvalue matrix that contains the associated natural frequencies. By taking advantage of the characteristic of the AR model with ERA (ARMERA), civil engineering can address problems related to damage detection. Operational modal analysis using ARMERA is applied to three experiments. One experiment is coupled with an artificial neural network algorithm and it can detect damage locations and extension. The neural network uses a specific number of ARCs as input and multiple submatrix scaling factors of the structural stiffness matrix as output to represent the damage.