• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.25-34
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• 2011

The transition probability can be used for the estimation of subpopulation total in panel data analysis. In this paper a real data analysis is performed and the sensitivity of the sample size allocated in the subpopulation is examined by small simulation studies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.3
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• pp.1035-1041
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• 2012

This paper examines the effects of fundamental variables on stock returns. Therefore, the major purpose of this study is to identify fundamental variables having a systematical effect on the stock return. The paper uses panel data analysis. We find that the results of regressions say that firm size, book-to-market ratio(B/M), earning-to-price ratio(E/P), cash flow-to-price ratio(C/P) can explain the differences in average returns across stocks.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.521-529
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• 2013

The acceptance of the test of the homogeneity for panel time series models allows for the pooling of the series to achieve parsimony. In this paper, we introduce a panel bilinear time series model as well as derive the stationary condition and the limiting distribution of the test statistic of the homogeneity test for the model. For the applications study, we use Korea Mumps data from January 2001 to December 2008. Finally, we perform test of homogeneity for the panel data with 8 independent bilinear time series.

• Communications for Statistical Applications and Methods
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• v.17 no.6
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• pp.899-907
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• 2010

Several non-response imputation methods are suggested, however, mainly cross-sectional imputations are studied and applied to this analysis. A simple and common imputation method for panel data is the cross-wave regression imputation or carry-over imputation as a special case of cross-wave regression imputation. This study suggests a multiple imputation method combined time series analysis and cross-sectional multiple imputation method. We compare this method and the cross-wave regression imputation method using MSE, MAE, and Bias. The 2008 monthly labor survey data is used for this study.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.123-132
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• 2016

We can achieve the principle of parsimony and efficiency if homogeneity for panel time series model is satisfied. We suggest a Rao test statistic and a Wald test statistic for the test of homogeneity for panel AR(1) and derived the limit distribution. We performed a simulation to examine statistics with the same chisquare distribution when number of the individual is small and in common with large. We also simulated to compare the empirical power of the statistics in a small panel. In application, we fit panel AR(1) model using regional monthly economical active population data and test homogeneity for panel AR(1). It is satisfied homogeneity, so it could be fitted AR(1) using the sample mean at the time point. We also compare the power of prediction between each individual and pooled model.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.377-390
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• 2017

When conducting a survey, item nonresponse occurs if the respondent does not respond to some items. Since analysis based only on completely observed data may cause biased results, imputation is often conducted to analyze data in its complete form. The panel study is a survey method that examines changes of responses over time. In panel studies, there has been a preference for using information from response values of previous waves when the imputation of item nonresponses is performed; however, limited research has been conducted to support this preference. Therefore, this study compares the performance of imputation methods according to whether or not information from previous waves is utilized in the panel study. Among imputation methods that utilize information from previous responses, we consider ratio imputation, imputation based on the linear mixed model, and imputation based on the Bayesian linear mixed model approach. We compare the results from these methods against the results of methods that do not use information from previous responses, such as mean imputation and hot deck imputation. Simulation results show that imputation based on the Bayesian linear mixed model performs best and yields small biases and high coverage rates of the 95% confidence interval even at higher nonresponse rates.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.521-534
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• 2006

This paper considers a panel regression model with ill-posed data and proposes the generalized maximum entropy(GME) estimator of the unknown parameters. These are natural extensions from the biometries, statistics and econometrics literature. The performance of this estimator is investigated by using of Monte Carlo experiments. The results indicate that the GME method performs the best in estimating the unknown parameters.

• Asia Marketing Journal
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• v.2 no.1
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• pp.83-98
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• 2000

본 논문은 국내소비자들을 대상으로 상표애호도를 측정하고 고객의 애호도 수준과 가격판촉에 대한 반응간의 관계를 분석하려는 탐색적 연구의 하나이다. 이를 위해 국내시장에서 수집된 비내구소비재에 대한 패널자료 중 탄산음료와 씨리얼제품을 대상으로 분석을 실시하였다. 실증분석 결과 각 제품에 높은 애호도를 보이는 고객의 인구통계적 특성은 다소 차이가 있는 것으로 밝혀졌다. 상표애호도와 가격판촉간의 관계에 있어서 상표애호도가 높은 고객집단이 낮은 집단에 비해 선호하는 상표에 대해 높은 가격을 지불하며 가격판촉에 덜 민감하게 반응하는 것으로 나타났다.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1397-1405
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• 2014

Usually unit-nonresponse or item-nonresponse occurs in the survey. In case the rate of nonresponse is high, the analysis ignoring nonresponse may cause the wrong effect. The characterization of nonresponse is required. In a cross-sectional data, it is possible to study the characteristics of item-nonresponse but it is hard to study the characteristics of the unit-nonresponse. In order to identify the characteristics of the unit-nonresponse, this study used the first-year student of middle schools in the Korea children and youth panel survey (KCYPS) data. We investigated the handling situation of nonresponse and analyzed the characteristics of the unit-nonresponse. Most of the papers applied the way of getting rid of nonresponse, so that there was little paper using weights. In this paper, we compared the results of the analyses depending on whether the weight is used or not. The method of using weights showed statistically significant results much more than that of removing. More discussion will be needed.

• Korea journal of population studies
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• v.30 no.1
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• pp.1-24
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• 2007

인구고령화의 진전과 함께 노인들의 거주형태에 대한 사회적. 정책적 관심이 증기하고 있으며, 그에 대한 논의와 연구들 또한 많이 늘어나고 있다. 그러나 이들 연구 대부분이 횡단적 자료(단일 시점이든 여러 시점이든)와 분석에 의존함으로써 노인지 거주형태가 생애주기를 따라 변하는 모습을 충분히 보여주지 못하고 있다. 이 연구는 한국노동연구원의 제1차 및 제6차 노동패널자료를 이용해 노년기 거주형태의 유동성을 경험적으로 제시하려는 목적을 가진다. 이들 위해 거주형태의 출현율(prevalence rate)과 전이율(transition rate)을 개념적으로 구분하고 자녀동거여부에 대한 분석을 실시하였다. 두 시점에 대한 횡단폭 분석결과는 노인들의 사회인구학적 특성에 따른 자녀별거경향의 차이를 보여주더라도 생애주기에 따른 역동성을 보여주기에는 한계가 많음이 확인되었다. 두 시점 간의 거주형태 변화에 대한 패널분석에서는 다수 노인들의 거주형태가 주어진 기간 동안 안정적으로 나타났다. 그러나 거주형태의 변화를 경험하는 데에는 연령증가와 배우자 상태변화 등이 중요한 요인임이 확인되었다. 이러한 생애주기적 변화의 효과는 대부분의 계량적 연구에서 유추되는 수준이거나 질적 연구에서만 보고되어 왔던 것이다. 이 연구결과는 노년기 거주형태의 지속성을 보여주는 한편 변화 가능성과 요인을 파악함으로써 노년기 거주형태에 대한 개념적 이해론 공고히 할 것으로 기대된다. 또한 이 연구에서 제시된 방법론적 논의와 접근방식은 생애주기별 변화에 초점을 두고자 하는 다른 연구영역에서도 적용 가능할 것이라 기대된다.