• 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).

• Communications for Statistical Applications and Methods
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• 제23권2호
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• pp.179-187
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• 2016

The fixed effects panel probit model faces "incidental parameters problem" because it has a property that the number of parameters to be estimated will increase with sample size. The maximum likelihood estimation fails to give a consistent estimator of slope parameter. Unlike the panel regression model, it is not feasible to find an orthogonal reparameterization of fixed effects to get a consistent estimator. In this note, a hierarchical Bayesian model is proposed. The model is essentially equivalent to the frequentist's random effects model, but the individual specific effects are estimable with the help of Gibbs sampling. The Bayesian estimator is shown to reduce reduced the small sample bias. The maximum likelihood estimator in the random effects model is also efficient, which contradicts Green (2004)'s conclusion.

• 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.

• 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.

• 수산경영론집
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• 제51권1호
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• pp.81-96
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• 2020

The purpose of this paper is to analyze factors affecting the net income of regional and industrial fisheries cooperatives in South Korea using panel data. This paper utilizes linear or GLS regression models such as pooled OLS model, fixed effects model, and random effects model to estimate affecting factors of the net income of regional and industrial fisheries cooperatives. After reviewing various tests, we eventually select random effects model. The results, based on panel data between 2013 and 2018 year and 64 fisheries cooperatives, indicate that capital and area dummy variables have positive effects and employment has negative effect on the net income of regional and industrial fisheries cooperatives as predicted. However, debt are opposite with our predictions. Specifically, it turns out that debt has positive effect on the net income of regional and industrial fisheries cooperatives although it has been increased. Additionally, this paper shows that the member of confreres does not show any significant effect on the net income of regional and industrial fisheries cooperatives in South Korea. This study is significant in that it analyzes the major factors influencing changes in the net income that have not been conducted recently for the fisheries cooperatives by region and industry.

• 에너지공학
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• 제26권2호
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• pp.80-92
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• 2017

본 연구는 1998년부터 2015년까지의 16개 시도별 경유수요량, 경유제품 판매가격(유통단계), 및 총 부가가치생산의 패널데이터를 이용하여, 패널GLS, 고정효과(Fixed Effect), 확률효과(Random Effect) 및 동적패널(Dynamic Panel) 모형을 통해 국내 경유수요함수를 추정하고, 이를 통해 가격탄력성과 소득탄력성을 추정하였다. 단기 가격탄력성은 -0.2146(패널GLS), -0.2886(고정효과), -0.2854(확률효과), -0.1905(동적패널)로 추정되었고, 단기 소득탄력성은 0.7379(패널GLS), 0.4119(고정효과), 0.7260(확률효과), 0.4166(동적패널)로 추정되었는데, 모두 비탄력적인 것으로 나타났다. 장기 가격탄력성과 장기 소득탄력성은 동적패널을 통해 추정하였고, 각각 -0.4784, 1.0461로 유의하게 나타났다. 경유 수요는 소득에 증감에 대해 단기적으로는 비탄력적이나, 장기적으로는 탄력적으로 나타나고 있다. 추가로 서울지역을 기준으로 지역변수를 더미변수(Dummy Variables)로 하여 각 지역의 경유수요로의 효과를 검정하였는데, 10개 지역에서 상대적으로 유의하게 추정되었다.

• Communications for Statistical Applications and Methods
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• 제21권1호
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• pp.45-60
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• 2014

We introduce a MCMC sampling for a generalized linear normal random effects model with the ordered probit link function based on latent variables from suitable truncated normal distribution. Such models have proven useful in practice and we have observed numerically reasonable results in the estimation of fixed effects when the random effect term is provided. Applications that utilize Korean Welfare Panel Study data can be difficult to model; subsequently, we find that an ordered probit model with the random effects leads to an improved analyses with more accurate and precise inferences.

• Structural Engineering and Mechanics
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• 제71권2호
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• pp.197-210
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• 2019

Probabilistic buckling behavior of sandwich panel considering random system parameters using a radial basis function (RBF) approach is presented in this paper. The random system properties result in an uncertain response of the sandwich structure. The buckling load of laminated sandwich panel is obtained by employing higher-order-zigzag theory (HOZT) coupled with RBF and probabilistic finite element (FE) model. The in-plane displacement variation of core as well as facesheet is considered to be cubic while transverse displacement is considered to be quadratic within the core and constant in the facesheets. Individual and combined stochasticity in all elemental input parameters (like facesheets thickness, ply-orientation angle, core thickness and properties of material) are considered to know the effect of different degree of stochasticity, ply- orientation angle, boundary conditions, core thickness, number of laminates, and material properties on global response of the structure. In order to achieve the computational efficiency, RBF model is employed as a surrogate to the original finite element model. The stiffness matrix of global response is stored in a single array using skyline technique and simultaneous iteration technique is used to solve the stochastic buckling equations.

• 유통과학연구
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• 제17권3호
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• pp.85-95
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• 2019

Purpose - The existing marketing studies using Social Network Analysis have assumed that network structure variables are time-invariant. However, a node's network position can fluctuate considerably over time and the node's network structure can be changed dynamically. Hence, if such a dynamic structural network characteristics are not specified for virtual goods purchase model, estimated parameters can be biased. In this paper, by comparing a time-invariant network structure specification model(base model) and time-varying network specification model(proposed model), the authors intend to prove whether the proposed model is superior to the base model. In addition, the authors also intend to investigate whether coefficients of network structure variables are random over time. Research design, data, and methodology - The data of this study are obtained from a Korean social network provider. The authors construct a monthly panel data by calculating the raw data. To fit the panel data, the authors derive random effects panel tobit model and multi-level mixed effects model. Results - First, the proposed model is better than that of the base model in terms of performance. Second, except for constraint, multi-level mixed effects models with random coefficient of every network structure variable(in-degree, out-degree, in-closeness centrality, out-closeness centrality, clustering coefficient) perform better than not random coefficient specification model. Conclusion - The size and importance of virtual goods market has been dramatically increasing. Notwithstanding such a strategic importance of virtual goods, there is little research on social influential factors which impact the intention of virtual good purchase. Even studies which investigated social influence factors have assumed that social network structure variables are time-invariant. However, the authors show that network structure variables are time-variant and coefficients of network structure variables are random over time. Thus, virtual goods purchase model with dynamic network structure variables performs better than that with static network structure model. Hence, if marketing practitioners intend to use social influences to sell virtual goods in social media, they had better consider time-varying social influences of network members. In addition, this study can be also differentiated from other related researches using survey data in that this study deals with actual field data.

• 무역학회지
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• 제44권1호
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• pp.45-56
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• 2019

The purpose of this study is to investigate the relationship between foreign investment and carbon emissions in the Korean electricity sector, the causal relationship between the foreign investment invested in the electric power sector in the 16 regional regions and the carbon emissions in the region, The purpose of this study is to analyze the effects of foreign investment on these sectors and the carbon footprint of these sectors using Panel Random Effect Analysis, Panel VAR and OLS models. A panel analysis of foreign investment and regional carbon emissions showed that there was a causal relationship. Based on this analysis, OLS analysis showed that 7 out of 16 metropolitan areas were foreign investment And carbon emissions were significant. In the remaining six regions except Gwangju, there was a causal relationship between foreign investment in the local power sector and the reduction of carbon emissions. After categorizing the electric power industry by device, process, purpose and number of employees, causality also appeared in relation to foreign investment in these sectors and their carbon emissions. Through this study, the authors suggest that foreign investment can be a way to solve not only the financial burden of carbon emission problem, but also the development of national economy and industry through the inflow of capital and advanced new technology.