• Title, Summary, Keyword: panel regression

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Dual Generalized Maximum Entropy Estimation for Panel Data Regression Models

  • Lee, Jaejun;Cheon, Sooyoung
    • Communications for Statistical Applications and Methods
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    • v.21 no.5
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    • pp.395-409
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    • 2014
  • Data limited, partial, or incomplete are known as an ill-posed problem. If the data with ill-posed problems are analyzed by traditional statistical methods, the results obviously are not reliable and lead to erroneous interpretations. To overcome these problems, we propose a dual generalized maximum entropy (dual GME) estimator for panel data regression models based on an unconstrained dual Lagrange multiplier method. Monte Carlo simulations for panel data regression models with exogeneity, endogeneity, or/and collinearity show that the dual GME estimator outperforms several other estimators such as using least squares and instruments even in small samples. We believe that our dual GME procedure developed for the panel data regression framework will be useful to analyze ill-posed and endogenous data sets.

An Analysis of the Determinants of Employment Productivity in Korean Transportation Industry Using Korea Labor and Income Panel Study (한국노동패널자료를 활용한 국내 운송업 고용생산성 결정요인 분석)

  • So, Ae-rim;Shin, Seung-sik
    • Journal of Korea Port Economic Association
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    • v.35 no.1
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    • pp.57-76
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    • 2019
  • This study deals with the determinants of employment productivity of transportation labor, who are the main agents of the transportation industry that has made significant contributions to our country's industrial development. The study selected the determinants of employment productivity using the Korea Labor and Income Panel Study data, and analyzed the effects of various factors using panel logistic regression, panel OLS model, and panel robust regression. The results were as follows. First, a more positive effect was shown when employees held a regular job, had a "high level of education", "joining the labor union" and "experiencing vocational training". Second, in the case of job security, having a "high level of education" and "joining the labor union" showed a more positive effect; further, job security was higher for employees who worked in a "big company" or were "married". Third, in the case of higher income productivity, higher values of "age", "academic ability" and "company size" had a more positive effect, whereas larger values of "education" and "health condition except job training" had a negative one. Fourth, in the case of job satisfaction, "female", "joining the labor union" and having a higher "income" or "job security" led to higher satisfaction and a better "health condition compared to an average person". Further, a higher "overall life satisfaction" and "economic level" led to lower job satisfaction. The analysis of the determinants of employment productivity of transportation business and seeking for improvement plan is expected to improve the employment productivity in the transportation business.

Capability Analysis of Consistency with Panel Flatness & Black Matrix for Screen Printing (스크린 프린팅 적용을 위한 패널 평탄도와 BM 일치성의 공정능력 분석)

  • 이도경;장성호;고남제
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.27 no.1
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    • pp.32-37
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    • 2004
  • A new display device is required, which has concepts of flatness and slimness. FED can be one of the solutions. When we use flat panel, we can save the raw material and reduce the production time by eliminating the printing process, drying process, and washing process. In this case, good panel flatness and consistency with panel flatness and black matrix is the precondition. Therefor, we analyzed process capability of panel flatness and regression between panel flatness and BM position by experiments.

Restricted maximum likelihood estimation of a censored random effects panel regression model

  • Lee, Minah;Lee, Seung-Chun
    • Communications for Statistical Applications and Methods
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    • v.26 no.4
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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.

Test of Linearity in Panel Regression Model (패널회귀모형에서 선형성검정)

  • 송석헌;최충돈
    • The Korean Journal of Applied Statistics
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    • v.16 no.2
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    • pp.351-364
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    • 2003
  • This paper derives Lagrange multiplier tests based on Double-Length Artificial Regression and Outer-Product Gradient for testing linear and log-linear panel regressions against Box-Cox alternatives. The proposed DLR based LM tests are easy to implement in an error component model. From the Monte Carlo study, the DLR based LM tests are recommended for testing functiona forms.

Bayesian Inference for Censored Panel Regression Model

  • Lee, Seung-Chun;Choi, Byongsu
    • Communications for Statistical Applications and Methods
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    • v.21 no.2
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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.

Panel data analysis with regression trees (회귀나무 모형을 이용한 패널데이터 분석)

  • Chang, Youngjae
    • Journal of the Korean Data and Information Science Society
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    • v.25 no.6
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    • pp.1253-1262
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    • 2014
  • Regression tree is a tree-structured solution in which a simple regression model is fitted to the data in each node made by recursive partitioning of predictor space. There have been many efforts to apply tree algorithms to various regression problems like logistic regression and quantile regression. Recently, algorithms have been expanded to the panel data analysis such as RE-EM algorithm by Sela and Simonoff (2012), and extension of GUIDE by Loh and Zheng (2013). The algorithms are briefly introduced and prediction accuracy of three methods are compared in this paper. In general, RE-EM shows good prediction accuracy with least MSE's in the simulation study. A RE-EM tree fitted to business survey index (BSI) panel data shows that sales BSI is the main factor which affects business entrepreneurs' economic sentiment. The economic sentiment BSI of non-manufacturing industries is higher than that of manufacturing ones among the relatively high sales group.

Asymptotic Properties of the Disturbance Variance Estimator in a Spatial Panel Data Regression Model with a Measurement Error Component

  • Lee, Jae-Jun
    • Communications for Statistical Applications and Methods
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    • v.17 no.3
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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.

The Effect of First Observation in Panel Regression Model with Serially Correlated Error Components

  • Song, Seuck-Heun
    • Communications for Statistical Applications and Methods
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    • v.6 no.3
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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.

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Comparisons of Imputation Methods for Wave Nonresponse in Panel Surveys (패널조사 웨이브 무응답의 대체방법 비교)

  • Kim, Kyu-Seong;Park, In-Ho
    • Survey Research
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    • v.11 no.1
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    • pp.1-18
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    • 2010
  • We compare various imputation methods for compensating wave nonresponse that are commonly adopted in many panel surveys. Unlike the cross-sectional survey, the panel survey is involved a time-effect in nonresponse in a sense that nonresponse may happen for some but not all waves. Thus, responses in neighboring waves can be used as powerful predictors for imputing wave nonresponse such as in longitudinal regression imputation, carry-over imputation, nearest neighborhood regression imputation and row-column imputation method. For comparison, we carry out a simulation study on a few income data from the Korean Welfare Panel Study based on two performance criteria: predictive accuracy and estimation accuracy. Our simulation shows that the ratio and row-column imputation methods are much more effective in terms of both criteria. Regression, longitudinal regression and carry-over imputation methods performed better in predictive accuracy, but less in estimation accuracy. On the other hand, nearest neighborhood, nearest neighbor regression and hot-deck imputation show higher performance in estimation accuracy but lower predictive accuracy. Finally, the mean imputation shows much lower performance in both criteria.

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