• Journal of the Korean Statistical Society
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• v.25 no.4
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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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• v.24 no.1
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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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• v.35 no.1
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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.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.257-266
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• 2016

Random Coefficient Autoregressive models (RCA) have attracted increased interest due to the wide range of applications in biology, economics, meteorology and finance. We consider an RCA as an appropriate model for non-linear properties and better than an AR model for linear properties. We study the methods of RCA parameter estimation. Especially we proposed the special case that an random coefficient ${\phi}(t)$ has the initial value ${\phi}(0)$ in the RCA model. In practical study, we estimated the parameters and compared Prediction Error Sum of Squares (PRESS) criterion between AR and RCA using Korean Mumps data.

• Communications for Statistical Applications and Methods
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• v.17 no.5
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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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• v.26 no.4
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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.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.99-109
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• 2019

We compared the confidence intervals of estimators using various bootstrap methods for a Random Coefficient Autoregressive(RCA) model. We consider a Quasi score estimator and M-Quasi score estimator using Huber, Tukey, Andrew and Hempel functions as bounded functions, that do not have required assumption of distribution. A standard bootstrap method, percentile bootstrap method, studentized bootstrap method and hybrid bootstrap method were proposed for the estimations, respectively. In a simulation study, we compared the asymptotic confidence intervals of the Quasi score and M-Quasi score estimator with the bootstrap confidence intervals using the four bootstrap methods when the underlying distribution of the error term of the RCA model follows the normal distribution, the contaminated normal distribution and the double exponential distribution, respectively.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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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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• v.7 no.1
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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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• v.10 no.1
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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.