Byoung Cheol Jung

The unbalanced nested error component regression model

Abstract This paper considers a nested error component model with unbalanced data and proposes simple analysis of variance (ANOVA), maximum likelihood (MLE) and minimum norm quadratic unbiased estimators (MINQUE)-type estimators of the variance components. These are natural extensions from the biometrics, statistics and econometrics literature. The performance of these estimators is investigated by means of Monte Carlo experiments. While the MLE and MINQUE methods perform the best in estimating the variance components and the standard errors of the regression coefficients, the simple ANOVA methods perform just as well in estimating the regression coefficients. These estimation methods are also used to investigate the productivity of public capital in private production.

SIMPLE LM TESTS FOR THE UNBALANCED NESTED ERROR COMPONENT REGRESSION MODEL

ABSTRACT This paper derives several Lagrange Multiplier tests for the unbalanced nested error component model. Economic data with a natural nested grouping include firms grouped by industry; or students grouped by schools. The LM tests derived include the joint test for both effects as well as the test for one effect conditional on the presence of the other. The paper also derives the standardized versions of these tests, their asymptotic locally mean most powerful version as well as their robust to local misspecification version. Monte Carlo experiments are conducted to study the performance of these LM tests.

Testing for overdispersion in a censored Poisson regression model

In this article, we investigate the efficiency of score tests for testing a censored Poisson regression model against censored negative binomial regression alternatives. Based on the results of a simulation study, score tests using the normal approximation, underestimate the nominal significance level. To remedy this problem, bootstrap methods are proposed. We find that bootstrap methods keep the significance level close to the nominal one and have greater power uniformly than does the normal approximation for testing the hypothesis.

Comparison of designs for the three-fold nested random model

The quality of estimation of variance components depends on the design used as well as on the unknown values of the variance components. In this article, three designs are compared, namely, the balanced, staggered, and inverted nested designs for the three-fold nested random model. The comparison is based on the so-called quantile dispersion graphs using analysis of variance (ANOVA) and maximum likelihood (ML) estimates of the variance components. It is demonstrated that the staggered nested design gives more stable estimates of the variance component for the highest nesting factor than the balanced design. The reverse, however, is true in case of lower nested factors. A comparison between ANOVA and ML estimation of the variance components is also made using each of the aforementioned designs.

Score Tests for Testing Independence in the Zero-Truncated Bivariate Poisson Models

In this study, score test statistics for testing independence in the zero-truncated bivariate Poisson distributions are proposed. The Monte Carlo study shows that the score tests proposed in this article keep the significance level close to the nominal one, but the LR and Wald tests over-reject the null hypothesis when it is true. The score tests for testing independence in the zero-truncated bivariate Poisson regression models are also derived in this study.

BLUP in the nested panel regression model with serially correlated errors

Abstract The best linear unbiased predictor for the panel data regression model with serially correlated nested error components is derived. Furthermore, performance of the predictor is compared with the other predictors using the study of productivity of public capital in private production based on a panel of 28 states over the period 1970–1986. The estimators whose predictions are compared include OLS, nested effects ML estimator ignoring serial correlation and nested effects ML estimator accounting for the serial correlation. Based on prediction mean square error (PMSE) forecast performance, it is crucial to take into account nested effects as well as serial correlation.

LM Tests for the Unbalanced Nested Panel Data Regression Model with Serially Correlated Errors

This paper derives several Lagrange Multiplier tests for the unbalanced nested error component model with serially correlated remainder disturbances. The problems of overtesting and undertesting for serial correlation and zero random group and nested subgroup effects are considered. The joint test extends the earlier work of Breush and Pagan [1980] and King and Wu [1997] to the unbalanced nested error component regression model with serially correlated errors. Additionally, conditional LM tests, asymptotically local mean most powerful (LMMP) tests; modified Rao-Score tests that guard against local misspecification are proposed for this model. These generalize the work of Baltagi and Li [1995], Rahman and King [1998] and Bera and Yoon [1993]. Finally, Monte Carlo experiments are conducted to study the performance of these LM tests.

Score tests for zero-inflation and overdispersion in two-level count data

In a Poisson regression model in which observations are either clustered or represented by repeated measurements of counts, the number of observed zero counts is sometimes greater than the expected frequency by the Poisson distribution and overdispersion may remain even after modeling excess zeros. The zero-inflated negative binomial (ZINB) mixed regression model is suggested to analyze such data. Previous studies have proposed score statistics for testing zero-inflation and overdispersion separately in correlated count data. Here, we also deal with simultaneous score tests for zero-inflation and overdispersion in two-level count data by using the ZINB mixed regression model. Score tests are suggested for (1) zero-inflation in the presence of overdispersion, (2) overdispersion in the presence of zero-inflation, and (3) zero-inflation and overdispersion simultaneously. The level and power of score test statistics are evaluated by a simulation study. The simulation results indicate that score test statistics may occasionally underestimate or overestimate the nominal significance level due to variation in random effects. This study proposes a parametric bootstrap method to overcome this problem. The simulation results of the bootstrap test indicate that score tests hold the nominal level and provide good power.

Sample Based Algorithm for k-Spatial Medians Clustering

As an alternative to the k-means clustering the k-spatial medians clustering has many good points because of advantages of spatial median. However, it has not been used a lot since it needs heavy computation. If the number of objects and the number of variables are large the computation time problem is getting serious. In this study we propose fast algorithm for the k-spatial medians clustering. Practical applicability of the algorithm is shown with some numerical studies.

Score tests for overdispersion in the bivariate negative binomial models

In this paper, we derive the score test statistic for testing overdispersion and covariance parameter in the bivariate negative binomial (BNB) models. The testing procedures are extended to the BNB regression models. Two empirical examples with and without covariates are provided to illustrate the results.