• 응용통계연구
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• 제16권2호
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• pp.351-364
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• 2003

본 논문에서는 오차성분을 가지는 패널회귀모형에서 모형의 선형성을 검정 할 수 있는 검 정통계량을 제시하고, 유도한 검정통계량의 계산을 위하여 인공회귀방법을 이용하려한다. 모의실험 결과, Double-Length Artificial Resression(DLR)을 이용한 LM 검정통계량은 명목유의 수준을 잘 유지하고 있는 것으로 나타났으며 검정력에 있어서도 기존의 검정에 비하여 높게 나타났다.

• Communications for Statistical Applications and Methods
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• 제6권3호
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• pp.667-676
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• 1999

We investigate the effects of omission of initial observations in each individuals in the panel data regression model when the disturbances follow a serially correlated one way error components. We show that the first transformed observation can have a relative large hat matrix diagonal component and a large influence on parameter estimates when the correlation coefficient is large in absolute value.

• Communications for Statistical Applications and Methods
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• 제26권4호
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• pp.371-383
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• 2019

Panel data sets have been developed in various areas, and many recent studies have analyzed panel, or longitudinal data sets. Maximum likelihood (ML) may be the most common statistical method for analyzing panel data models; however, the inference based on the ML estimate will have an inflated Type I error because the ML method tends to give a downwardly biased estimate of variance components when the sample size is small. The under estimation could be severe when data is incomplete. This paper proposes the restricted maximum likelihood (REML) method for a random effects panel data model with a censored dependent variable. Note that the likelihood function of the model is complex in that it includes a multidimensional integral. Many authors proposed to use integral approximation methods for the computation of likelihood function; however, it is well known that integral approximation methods are inadequate for high dimensional integrals in practice. This paper introduces to use the moments of truncated multivariate normal random vector for the calculation of multidimensional integral. In addition, a proper asymptotic standard error of REML estimate is given.

• Communications for Statistical Applications and Methods
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• 제21권2호
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• pp.193-200
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• 2014

It was recognized by some researchers that the disturbance variance in a censored regression model is frequently underestimated by the maximum likelihood method. This underestimation has implications for the estimation of marginal effects and asymptotic standard errors. For instance, the actual coverage probability of the confidence interval based on a maximum likelihood estimate can be significantly smaller than the nominal confidence level; consequently, a Bayesian estimation is considered to overcome this difficulty. The behaviors of the maximum likelihood and Bayesian estimators of disturbance variance are examined in a fixed effects panel regression model with a limited dependent variable, which is known to have the incidental parameter problem. Behavior under random effect assumption is also investigated.

• Communications for Statistical Applications and Methods
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• 제17권3호
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• pp.349-356
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• 2010

The ordinary least squares based estimator of the disturbance variance in a regression model for spatial panel data is shown to be asymptotically unbiased and weakly consistent in the context of SAR(1), SMA(1) and SARMA(1,1)-disturbances when there is measurement error in the regressor matrix.

• 응용통계연구
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• 제19권3호
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• pp.461-479
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• 2006

본 논문에서는 이원오차성분을 갖는 패널회귀모형에서 모형식별을 위하여 LM 검정통계량을 유도하고 검정통계량의 연산을 위하여 인공회귀방법(Double-Length Artificial Regression, DLR)을 이용한다. 모의 실험 결과, 소표본의 경 우에는 Outer-Product Gradient(OPG)에 근거한 LM 검정통계량은 유위수준이 과대기각하는 경향을 보인 반면 DLR에 근거한 LM 검정통계량은 명목유의수준을 잘 유지하고 검정력도 높게 나타났다.

• Asian Journal of Business Environment
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• 제12권2호
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• pp.1-12
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• 2022

Purpose: This study aims to analyze the inclusive growth in Central Sulawesi Province, an eastern province of Indonesia, up to the districts/cities level. The inclusive growth is analyzed by using Ramos, Ranieri, and Lammens' index that has three indicators which are employment, poverty, and income inequality. Research design, data, and methodology: This study uses panel data of 13 districts/cities in Central Sulawesi Province from 2015 to 2019. The statistical regression used is the panel regression method to analyze the determinants of inclusive growth there. Results: The study found that the average inclusive growth of districts/cities in Central Sulawesi is increasing from the low-level in 2015 to mid-level in 2019. The panel's data regression using fixed effect model FGLS-SUR found Investment (GFCF), Road Infrastructure, HDI, and Processing Industry have a significant positive effect. Regional minimum wage (RMW) has a significant negative effect. Government Expenditure on Education and Health Function has no significant positive effect on inclusive growth. Conclusions: throughout the study period, gini coefficient and poverty rate is slowly decreasing, while employment to population ratio remains volatile in districts/cities of Central Sulawesi.

• Journal of the Korean Statistical Society
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• 제30권4호
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• pp.541-550
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• 2001

This paper considers a panel data regression model in which the disturbances follow a nested error components with serial correlation. Given this model, this paper derives several Lagrange Multiplier(LM) testis for the presence of serial correlation as well as random individual effects, nested effects, and for existence of serial correlation given random individual and nested effects.

• 응용통계연구
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• 제14권1호
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• pp.121-135
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• 2001

본 논문에서는 이원오차성분을 가지는 패널회귀모형에서 미래시점에 대한 다양한 예측량들을 유도하고, 예측량들의 효율성을 모의실험을 통하여 비교하였다. 모의실험 결과, FGLS추정량을 이용한 예측량들은 참 GLS를 이용한 예측량과 효율성에서 서로 큰차이를 보이지 않았다. 또한 계산상 매우 복잡한 ML과 REML을 이용한 예측량과도 거의 비슷한 효율성을 보여주었다.

• Journal of the Korean Statistical Society
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• 제28권4호
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• pp.489-501
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• 1999

In this paper, we consider the panel data regression model in which the disturbances have nested error component. We derive a Lagrange Multiplier(LM) test which is jointly testing for the presence of random individual effects and nested effects under the normality assumption of the disturbances. This test extends the earlier work of Breusch and Pagan(1980) and Baltagi and Li(1991). Further, it is shown that this LM test has the same asymptotic distribution without normality assumption of the disturbances.