Intergenerational earnings mobility in Japan among sons and daughters: levels and trends

This paper estimates the extent of intergenerational income mobility in Japan among sons and daughters born between 1935 and 1975. Our estimates rely on a two-sample instrumental variables approach using representative data from the Japanese Social Stratification and Mobility surveys, collected between 1965 and 2005. Father’s income is predicted on the basis of a rich set of variables, and we discuss changes in the Japanese earnings structure for cohorts born between the early 1900s and the 1960s. Our main results indicate that the intergenerational income elasticity (IGE) for both sons and daughters in Japan lies around 0.35, which is an intermediate value, by international standards. We discuss the sensitivity of the IGE to using either personal or family income as the income variable for both fathers and children. We also examine changes across cohorts in the IGE. Results indicate that intergenerational mobility has been roughly stable over the last decades.     

On small sample properties of the mixed regression predictor under misspecification

This paper relaxes the Mittelhammer's (1981) assumption that the value of the true variance is known in the mixed regression model and examines the small sample, properties of the feasible mixed regression predictor under misspecification. The paper shows that the feasible mixed regression predictor is not always superior to the ordinary least squares predictor in terms of the weak mean square error when there exist omitted variables in the model. Further it shows that misspecificstion works favorably for the ordinary least squares predictor.     

Nonparametric regression for dependent data in the errors-in-variables problem

We consider the nonparametric estimation of the regression functions for dependent data. Suppose that the covariates are observed with additive errors in the data and we employ nonparametric deconvolution kernel techniques to estimate the regression functions in this paper. We investigate how the strength of time dependence affects the asymptotic properties of the local constant and linear estimators. We treat both short-range dependent and long-range dependent linear processes in a unified way and demonstrate that the long-range dependence (LRD) of the covariates affects the asymptotic properties of the nonparametric estimators as well as the LRD of regression errors does.     

Information criteria in identifying regression models

This paper proposes to use information criteria to discriminate the standard regression model from error components models, heteroskedastic models, or models with autocorrelated errors.     

Consequences of organizational commitment in abolished company sports team - a case study in Japan

The purpose of this study was to show that how the abolishment of company sports team influenced the organizational commitment in employees. In this study, Three-Component Model of Organizational Commitment (Meyer and Allen, 1997) was tested with 16 employees (10 males, 6 females) of T Company in NAGANO prefecture. The average age of the participants was 44, 50 years (SD=±0.85). And from 16 employees, 3 male employees were measured on organizational commitment with interview test. According to the analysis, the relation between organizational commitment in employees and the abolishment of company sports team was not positive significant correlation. Furthermore, results of interview test did not show the relation between organizational commitment in employees and the abolishment of company sports team. However, results of interview test showed the relation with organizational commitment of players in T Company sports team. Consequently, the goal to possess a sports team in T Company was not to boost organizational commitment in employees. In addition, it is necessary to reconsider the correlation among employees engaged in T Company in the future.     

Bias reduction of the maximum-likelihood estimator for a conditional Gaussian MA(1) model

In this paper, we consider an estimation for the unknown parameters of a conditional Gaussian MA(1) model. In the majority of cases, a maximum-likelihood estimator is chosen because the estimator is consistent. However, for small sample sizes the error is large, because the estimator has a bias of O(n^{? 1}). Therefore, we provide a bias of O(n^{? 1}) for the maximum-likelihood estimator for the conditional Gaussian MA(1) model. Moreover, we propose new estimators for the unknown parameters of the conditional Gaussian MA(1) model based on the bias of O(n^{? 1}). We investigate the properties of the bias, as well as the asymptotical variance of the maximum-likelihood estimators for the unknown parameters, by performing some simulations. Finally, we demonstrate the validity of the new estimators through this simulation study.     

Root-n-consistent semiparametric estimation of partially linear models for weakly dependent observations

We consider estimation of the linear part in a partially linear model for absolutely regular observations. The estimator using random weights are proposed and the asymptotic normality of the estimator is established without compact support assumption.