Umvu estimation for the cumulative hazard function in a pareto distribution of the first kind

The uniformly minimum variance unbiased estimator of the cumulative hazard function in the Pareto distribution of the first kind is derived. The variance of the estimator is also obtained in an analytic form, and for some cases its values are compared numerically with mean square errors of the maximum likelihood estimator.     

Uniformly minimum variance unbiased estimation in lognormal and related distributions

The uniformly mimimum variance unbiased estimators and their variances from independent samples of lognormal distributions are concisely expressed using the hypergeometric functions     

A cohort analysis of female labor participation rates in the U.S. and Japan

Aggregate data of female labor participation rates in U.S. and Japan, classified by period and by age, are decomposed into age, period, and cohort effects using innovative Bayesian cohort models that were developed to overcome the identification problem in cohort analysis. The main findings are that in both countries, age effects are the largest and period effects are the smallest; in both countries, age effects are roughly consistent with life-cycle movements expected by labor economics, but the negative effects of marriage and/or childbearing on women?’s labor supply in Japan are much larger than those observed in the U.S.; and in both countries, upward movements of cohort effects during 1930s–1960s were found. However, cohort effects are larger for the U.S. than for Japan. All the cohort results are roughly consistent with the marriage squeeze hypothesis and the Easterlin hypothesis.     

On umvu estimators for the multivariate lognormal distribution and their variances

Uniformly minimum variance unbiased estimators of several parameters of the multivariate lognormal distribution are expressed by using the hypergeometric functions of matrix argument. And the variances are given in special cases.     

Cointegration Detection Using Dynamic Factor Models

A method of information-criterion-based cointegration detection using dynamic factor models is proposed. The results of the data-based and non data-based Monte Carlo simulations suggest that this method is as effective as conventional hypothesis-testing methods. In the proposed method, an observed multivariate time series is described in terms of common stochastic trends plus stationary autoregressive cycles. Then the best model is selected from among alternative models obtained by changing the number of common stochastic trends, on the basis of information criteria. Consequently, the cointegration rank is determined on the basis of the selected model. Two advantages of the proposed method are also discussed.     

Simulated real-time detection of multiple structural changes: Evidence from Japanese economic growth

An efficient treatment of practical issues on detecting multiple structural changes is presented. The efficacy of this method is examined by comparing the conventional hypothesis-testing method via comprehensive simulations and empirical applications. The method recommended is a model-selection using the Bayesian information criterion and allowing for heteroscedasticity. Empirical results show that the first structural change of Japanese economic growth occurred in 1974Q2, which was detected in 1979Q4, and that the second structural change occurred in 1992Q2, which was detected in 1998Q1. Two advantages of the model-selection method compared to the hypothesis-testing method are also discussed.     

Incomplete sufficient unbiased estimators in the problem of the nile

A family of incomplete sufficient unbiased esti¬mators is constructed for an arbitrary power of the parameter in the problem of the Nile. The variance is derived in a closed form and compared numerically with the mean square error of the maximum likelihood es t ima tor     

Empirical evidence on intergenerational inequality of tax burdens in the U.S. and Japan

Aggregate data of tax burdens in the U.S. and Japan, classified by period and by age, are decomposed into age, period, and cohort effects using the Bayesian cohort models which were developed to overcome the identification problem in cohort analysis. Main findings are that in both countries the age effects are the largest and the cohort effects are obscure or negligible and that in both countries significant intergenerational inequality is not observed.     

Parameter changes in GARCH model

A new method for detecting the parameter changes in generalized autoregressive heteroskedasticity GARCH (1,1) model is proposed. In the proposed method, time series observations are divided into several segments and a GARCH (1,1) model is fitted to each segment. The goodness-of-fit of the global model composed of these local GARCH (1,1) models is evaluated using the corresponding information criterion (IC). The division that minimizes IC defines the best model. Furthermore, since the simultaneous estimation of all possible models requires huge computational time, a new time-saving algorithm is proposed. Simulation results and empirical results both indicate that the proposed method is useful in analysing financial data.