• 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 the Korean Statistical Society
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• 제24권1호
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• pp.243-248
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• 1995

In this article we show that a class of random coefficient autoregressive processes including the NEAR (New exponential autoregressive) process has the strong mixing property in the sense of Rosenblatt with mixing order decaying to zero. The result can be used to construct model free prediction interval for the future observation in the NEAR processes.

• 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.

• 응용통계연구
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• 제29권1호
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• pp.257-266
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• 2016

비선형 모형인 확률계수 자기회귀 모형의 모수를 추정하기 위해 전체 데이터를 부표본으로 나누어 확률계수 ${\phi}(t)$가 초기값, ${\phi}(0)$를 갖는 특별한 경우를 제안하고 추정하였다. 모의 실험으로 부표본으로 나누어 확률계수 자기회귀 모형을 추정하는 더 바람직함을 확인하였다. 실증분석에서는 한국 Mumps 자료를 선형 모형인 자기회귀 모형과 확률 계수 자기회귀 모형에 각각 적합시켜 모수를 추정하고, PRESS 값을 비교하여 확률계수 자기회귀 모형의 예측이 더 우수함을 보였다.

• Communications for Statistical Applications and Methods
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• 제17권5호
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• pp.639-645
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• 2010

We consider a subdiagonal bilinear model and give sufficient conditions for the associated Markov chain defined by Pham (1985) to be uniformly ergodic and then obtain the $\beta$-mixing property for the given process. To derive the desired properties, we employ the results of generalized random coefficient autoregressive models generated by a matrix-valued polynomial function and vector-valued polynomial function.

• Journal of the Korean Statistical Society
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• 제26권4호
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• pp.531-541
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• 1997

The asymptotic distribution of residual autocorrelation functions from a generalized p-order random coefficient autoregressive process (GRCA(p)) is derived. To this end, we first describe the GRCA(p) models and then consider the normalised residuals after fitting the model. This result can be applied to the residual analysis for the diagonostic purpose.

• 응용통계연구
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• 제32권1호
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• pp.99-109
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• 2019

비선형 시계열인 확률계수 자기회귀(random coefficient autoregressive; RCA) 모형에 대하여 여러 가지 방법을 이용한 추정량의 신뢰구간 비교하였다. RCA 모형에 대하여 자료의 분포를 가정하지 않아도 되는 Quasi 스코어 추정량과 Huber, Tukey, Andrew, Hempal 4가지 유계함수를 이용한 M-Quasi 스코어 추정량을 제시하였다. 이러한 추정량에 대하여 표준 붓스트랩 방법, 백분위수 붓스트랩 방법, 스튜던트화 붓스트랩 방법, 하이브리드 붓스트랩 방법을 이용한 신뢰구간을 구하였다. 모의실험을 통하여 RCA 모형의 오차항의 분포가 정규분포, 오염정규분포, 이중지수분포를 따를 때 Quasi 스코어 추정량과 M-Quasi 스코어 추정량들의 근사적 신뢰구간과 네가지 붓스트랩 방법을 이용한 신뢰구간을 비교하였다.

• Communications for Statistical Applications and Methods
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• 제7권3호
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• pp.731-739
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• 2000

Conditional least square estimators for the parameters of he NLAR(p) time series models are obtained. it is also shown that these estimators are consistent and asymptotically normal.

• Communications for Statistical Applications and Methods
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• 제7권1호
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• pp.87-96
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• 2000

Nonlinear time series prediction is derived and compared between statistic of modeling and neural network method. In particular mean squared errors of predication are obtained in generalized random coefficient model and generalized autoregressive conditional heteroscedastic model and compared with them by neural network forecasting.

• Communications for Statistical Applications and Methods
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• 제10권1호
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• pp.101-113
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• 2003

Quasi likelihood estimators defined by Wedderburn are derived for several nonlinear time series models. And also, the least squared estimator and Quasi-likelihood estimator are compared in sense of asymptotic relative efficiency at those models. Finally, we apply these estimations to a real data on exchanging rate and stock market prices.