• Title/Summary/Keyword: ESTIMATOR model

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A Cointegration Test Based on Weighted Symmetric Estimator

  • Son Bu-Il;Shin Key-Il
    • Communications for Statistical Applications and Methods
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    • v.12 no.3
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    • pp.797-805
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    • 2005
  • Multivariate unit root tests for the VAR(p) model have been commonly used in time series analysis. Several unit root tests were developed and recently Shin(2004) suggested a cointegration test based on weighted symmetric estimator. In this paper, we suggest a multivariate unit root test statistic based on the weighted symmetric estimator. Using a small simulation study, we compare the powers of the new test statistic with the statistics suggested in Shin(2004) and Fuller(1996).

Asymptotic Properties of Least Square Estimator of Disturbance Variance in the Linear Regression Model with MA(q)-Disturbances

  • Jong Hyup Lee;Seuck Heum Song
    • Communications for Statistical Applications and Methods
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    • v.4 no.1
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    • pp.111-117
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    • 1997
  • The ordinary least squares estimator $S^2$ for the variance of the disturbances is considered in the linear regression model with sutocorrelated disturbances. It is proved that the OLS-estimator of disturbance variance is asymptotically unbiased and weakly consistent, when the distrubances are generated by an MA(q) process. In particular, the asymptotic unbiasedness and consistency of $S^2$ is satisfied without any restriction on the regressor matrix.

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ALMOST SURE AND COMPLETE CONSISTENCY OF THE ESTIMATOR IN NONPARAMETRIC REGRESSION MODEL FOR NEGATIVELY ORTHANT DEPENDENT RANDOM VARIABLES

  • Ding, Liwang
    • Bulletin of the Korean Mathematical Society
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    • v.57 no.1
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    • pp.51-68
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    • 2020
  • In this paper, the author considers the nonparametric regression model with negatively orthant dependent random variables. The wavelet procedures are developed to estimate the regression function. For the wavelet estimator of unknown function g(·), the almost sure consistency is derived and the complete consistency is established under the mild conditions. Our results generalize and improve some known ones for independent random variables and dependent random variables.

On the Bivariate Dichotomous Choice Model

  • Kim, Hea-Jung
    • Journal of the Korean Statistical Society
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    • v.14 no.1
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    • pp.7-17
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    • 1985
  • Data set generated by teh bivariate dichotomous choice made by individuals often occurs in practice. This paper presents general model of how such data set is generated as well as methods of estimation. The M.L.E. is examined and found to be computationally burdensome. A simpler estimator, the bivariate dichotomous two-stage estimator, is suggested as an alternative. The two-stage estimator is found to be as efficient as the M.L.E.

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Efficient Quasi-likelihood Estimation for Nonlinear Time Series Models and Its Application

  • Kim, Sahmyeong;Cha, Kyungyup;Lee, Sungduck
    • 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.

A Combination Capture-Recapture and Line Transect Model in Clustered Population

  • Choi, Jin-Sik;Pyong, Nam-Kung
    • Communications for Statistical Applications and Methods
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    • v.6 no.3
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    • pp.729-748
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    • 1999
  • In this paper we present combined estimator of capture-recapture and line transect model using bivariate detection function and detection probability according to objects being in cluster population. Here bivariate detection function use distance and cluster size. The simulation shows that combined estimator approaches the more true value the larger size parameter. Therefore this estimator using the bivariate detection function is more efficient in estimate the population size and density by size parameter.

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On Copas′ Local Likelihood Density Estimator

  • Kim, W.C.;Park, B.U.;Kim, Y.G.
    • Journal of the Korean Statistical Society
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    • v.30 no.1
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    • pp.77-87
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    • 2001
  • Some asymptotic results on the local likelihood density estimator of Copas(1995) are derived when the locally parametric model has several parameters. It turns out that it has the same asymptotic mean squared error as that of Hjort and Jones(1996).

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CHANGE-POINT ESTIMATION WITH SAMPLE FOURIER COEFFICIENTS

  • Kim, Jae-Hee
    • Proceedings of the Korean Statistical Society Conference
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    • 2002.11a
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    • pp.109-114
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    • 2002
  • In this paper we propose a change-point estimator with left and right regressions using the sample Fourier coefficients on the orthonormal bases. The asymptotic properties of the proposed change-point estimator are established. The limiting distribution and the consistency of the estimator are derived.

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On-line Parameter Estimator Based on Takagi-Sugeno Fuzzy Models

  • Park, Chang-Woo;Hyun, Chang-Ho;Park, Mignon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.12 no.5
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    • pp.481-486
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    • 2002
  • In this paper, a new on-line parameter estimation methodology for the general continuous time Takagi-Sugeno(T-5) fuzzy model whose parameters are poorly known or uncertain is presented. An estimator with an appropriate adaptive law for updating the parameters is designed and analyzed based on the Lyapunov theory. The adaptive law is designed so that the estimation model follows the plant parameterized model. By the proposed estimator, the parameters of the T-S fuzzy model can be estimated by observing the behavior of the system and it can be a basis for the indirect adaptive fuzzy control. Based on the derived design method, the parameter estimation for controllable canonical T-S fuzzy model is also Presented.

On the Effect of Estimated Mean for the Weighted Symmetric Estimator

  • Key Il Shin;Hee Jeong Kang
    • Communications for Statistical Applications and Methods
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    • v.4 no.3
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    • pp.903-909
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    • 1997
  • The ordinary least squares estimator and the corresponding pivotal statistics have been widely used for the unit test. Recently several test criteria based on maximum likelihood estimators and weighted symmetric estimator have been proposed for testing the unit root hypothesis in the autoregressive processes. Pantula at el. (1994) showed that the weighted symmetric estimator has good power properties. In this article we use an adjusted estimator for mean in the model when we use weighted symmetric estimator. A simulation study shows that for the small samples, this new test criterion has better power properties than the weighted symmetric estimator.

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