Technological progress, income inequality, and fertility

This paper constructs an overlapping-generations model with two different types of technology: modern, which can be accessed only by the skilled, and traditional, which can be accessed by the unskilled. The model described in this paper shows that a rise in the wage premium for skilled workers caused by skill-biased technological changes explains the following key stylized facts: with economic development, the fraction of skilled people increases, the fertility rate declines, and income inequality rises and then falls. The model also explains the observed gradual rises in income inequality in developed countries.      

The exact distribution and density functions of a pre-test estimator of the error variance in a linear regression model with proxy variables

We consider a linear regression model when some independent variables are unobservable, but proxy variables are available instead of them. We derive the distribution and density functions of a pre-test estimator of the error variance after a pre-test for the null hypothesis that the coefficients for the unobservable variables are zeros. Based on the density function, we show that when the critical value of the pre-test is unity, the coverage probability in the interval estimation of the error variance is maximum.     

Risk comparison of the Stein-rule estimator in a linear regression model with omitted relevant regressors and multivariate<Emphasis Type="Italic">t</Emphasis> errors under the Pitman nearness criterion

In this paper we consider a linear regression model with omitted relevant regressors and multivariatet error terms. The explicit formula for the Pitman nearness criterion of the Stein-rule (SR) estimator relative to the ordinary least squares (OLS) estimator is derived. It is shown numerically that the dominance of the SR estimator over the OLS estimator under the Pitman nearness criterion can be extended to the case of the multivariatet error distribution when the specification error is not severe. It is also shown that the dominance of the SR estimator over the OLS estimator cannot be extended to the case of the multivariatet error distribution when the specification error is severe. This research is partially supported by the Grants-in-Aid for 21st Century COE program.     

The general expressions for the moments of lawless and wang's ordinary ridge regression estimator

In this paper, we derive the exact general expressions for the moments of an ordinary ridge regression (ORR) estimator for individual regression coefficients in a different way from Firinguetti (1987). Using the derived expressions, we evaluate numerically the first four moments of the ORR estimator, and examine its bias, mean square error, skewness and kurtosis. Further, Monte Carlo experiments are carried out in order to examine the shape of the density function of the ORR estimator.     

Analysis of Job Satisfaction: The Case of Japanese Private Companies

The scope of economic theory expands if we consider that working provides people with both pecuniary compensation for its disutility and job satisfaction. This study empirically analyzes the job satisfaction of employees in Japanese private companies using a multivariate ordered probit model. In particular, we examine the effects of a big economic shock, such as the Lehman shock, on job satisfaction, as well as the effects of differences in gender and employment status. We estimate the model using a Bayesian analysis and a multivariate ordered probit model using the Markov chain Monte Carlo method. We show that non‐pecuniary aspects of jobs play an important role in job satisfaction.     

ON THE USE OF THE STEIN VARIANCE ESTIMATOR IN THE DOUBLE k-CLASS ESTIMATOR IN REGRESSION

This paper investigates the predictive mean squared error performance of a modified double k-class estimator by incorporating the Stein variance estimator. Recent studies show that the performance of the Stein rule estimator can be improved by using the Stein variance estimator. However, as we demonstrate below, this conclusion does not hold in general for all members of the double k-class estimators. On the other hand, an estimator is found to have smaller predictive mean squared error than the Stein variance-Stein rule estimator, over quite large parts of the parameter space.     

The density function and the MSE dominance of the pre-test estimator in a heteroscedastic linear regression model with omitted variables

In this paper, we consider a heteroscedastic linear regression model with omitted variables. We derive the density function of the pre-test estimator consisting of the two-stage Aitken estimator (2SAE) and the ordinary least squares estimator (OLSE) after the pre-test for homoscedasticity. We also derive the first two moments based on the density function and show the sufficient condition for the pre-test estimator to dominate the 2SAE in terms of the MSE. Our numerical evaluations show that when this sufficient condition does not hold and when the magnitude of the specification error is large, the pre-test estimator can be dominated by the 2SAE, and further, the 2SAE can be dominated by the OLSE.     

Strength-training behavior and perceived environment among Japanese older adults

The authors examined the relationship between strength-training behavior and perceived environment in older Japanese adults. An Internet-based survey was conducted of 293 adults age 68.2 ± 2.8 yr. The dependent variable was regular strength-training behavior. The IPAQ environment module, access to facilities for strength training, and home equipment for strength training were environmental factors. Logistic-regression analysis was employed. After demographic variables (gender, age, educational background, household income, body-mass index, self-rated health status, smoking habit, and residential area) were adjusted for, home equipment for strength training (OR = 2.14, 95% CI = 1.50-3.06), access to facilities for strength training (OR = 2.53, 95% CI = 1.32-4.85), and observing active people (OR = 2.20, 95% CI = 1.06-4.58) were positively correlated with regular strength-training behavior. In conclusion, environmental factors associated with strength-training behavior were access to facilities for strength training, having home equipment for strength training, and observing active people.     

A pragmatic dose‐finding approach using short‐term surrogate efficacy outcomes to evaluate binary efficacy and toxicity outcomes in phase I cancer clinical trials

There is a growing need for study designs that can evaluate efficacy and toxicity outcomes simultaneously in phase I or phase I/II cancer clinical trials. Many dose‐finding approaches have been proposed; however, most of these approaches assume binary efficacy and toxicity outcomes, such as dose‐limiting toxicity (DLT), and objective responses. DLTs are often defined for short time periods. In contrast, objective responses are often defined for longer periods because of practical limitations on confirmation and the criteria used to define ‘confirmation’. This means that studies have to be carried out for unacceptably long periods of time. Previous studies have not proposed a satisfactory solution to this specific problem. Furthermore, this problem may be a barrier for practitioners who want to implement notable previous dose‐finding approaches. To cope with this problem, we propose an approach using unconfirmed early responses as the surrogate efficacy outcome for the confirmed outcome. Because it is reasonable to expect moderate positive correlation between the two outcomes and the method replaces the surrogate outcome with the confirmed outcome once it becomes available, the proposed approach can reduce irrelevant dose selection and accumulation of bias. Moreover, it is also expected that it can significantly shorten study duration. Using simulation studies, we demonstrate the positive utility of the proposed approach and provide three variations of it, all of which can be easily implemented with modified likelihood functions and outcome variable definitions. Copyright © 2013 John Wiley & Sons, Ltd.     

The exact distribution and density functions of the stein-type estimator for normal variance

In this paper, we derive the exact distribution and density functions of the Stein-type estimator for the normal variance. It is shown by numerical evaluation that the density function of the Stein-type estimator is unimodal and concentrates around the mode more than that of the usual estimator.