Kazuhiko Kakamu

Forecasting electricity demand in Japan: A Bayesian spatial autoregressive ARMA approach

Regional electricity demand in Japan and spatial interaction among the regions using a Bayesian approach were examined. A spatial autoregressive (SAR) ARMA model was proposed to consider the features of electricity demand in Japan and a strategy of Markov chain Monte Carlo (MCMC) methods was constructed to estimate the parameters of the model. From empirical results, the spatial autoregressive ARMA (1, 1) model was selected, and it was found that spatial interaction plays an important role in electricity demand in Japan. Moreover, log predictive density showed that this SAR-ARMA model performs better than a univariate ARMA model. It was confirmed that the space-time model improves the performance of forecasting future electricity demand in Japan.

Small-sample Properties of Panel Spatial Autoregressive Models: Comparison of the Bayesian and Maximum Likelihood Methods

Abstract This article considers autoregressive (SAR) models. We method to estimate the parameters of likelihood (ML) method. Our Bayesian by the Monte Carlo studies. We found the efficient as the ML estimators.

Spatial interaction of crime incidents in Japan

We analyze the development of 18 types of criminal records in Japan for the period 1991-2001 across 47 prefectures with spatial lag and spatio-temporal heteroscedasticity. We explore the hypothesis that crime data are related to socio-economic variables in Japan. We extend the Bayesian approach of LeSage [J.P. LeSage, Bayesian estimation of spatial autoregressive models, Int. Regional Sci. Rev. 20 (1997) 113-129] for spatio-temporal Bayesian models. Additionally we analyze unobserved heteroscedasticity in the panel model by variance inflation factors as in Geweke [J. Geweke, Bayesian treatment of the independent Student-t linear model, J. Appl. Econ. 8 (1993) 19-40]. Positive and significant spatial dependencies can be found for 12 types of crimes and the influence of the socio-economic variables varies over the type of crimes.

Productivity convergence of manufacturing industries in Japanese MEA

The study presents (1) the convergence of labour productivity in manufacturing industries in Japanese MEAs during 1985 to 2000, using Markov chain transition matrix and (2) the speed of convergence to an ergodic distribution. The results indicate that (1) the labour productivities of all MEAs converge to different levels in the long-run and (2) its speed is as slow as the Solows growth model.

MULTILEVEL DECOMPOSITION METHODS FOR INCOME INEQUALITY MEASURES

This paper studies the multilevel decomposability of the respective income inequality measures proposed by Theil, Rao and Bahattacharya–Maharanobis. All the methods can be decomposed into multilevels if and only if each lower level subgroup belongs to only one particular higher level group. We found not only analytically but also empirically that the residual in the decomposed Bahattacharya–Maharanobis measure tends to increase when the decomposition levels increase. We conclude that Theils and Raos decompositions have advantages in empirical analysis and that the choice of the decomposition methods depends on the purpose of the analysis.

Model Choice for Panel Spatial Models: Crime Modeling in Japan

This paper considers the spatial patterns of crime incidents in Japan from a Bayesian point of view. We analyzed and compared different models by marginal likelihoods. From our posterior analysis, we found that the spatial pattern is different across crimes but panel SEM is selected in most of the types of crimes.

Approximate Bayesian computation for Lorenz curves from grouped data

This paper proposes a new Bayesian approach to estimate the Gini coefficient from the grouped data on the Lorenz curve. The proposed approach assumes a hypothetical income distribution and estimates the parameter by directly working on the likelihood function implied by the Lorenz curve of the income distribution from the grouped data. It inherits the advantages of two existing approaches through which the Gini coefficient can be estimated more accurately and a straightforward interpretation about the underlying income distribution is provided. Since the likelihood function is implicitly defined, the approximate Bayesian computational approach based on the sequential Monte Carlo method is adopted. The usefulness of the proposed approach is illustrated through the simulation study and the Japanese income data.

Does garbage pricing increase the immoral disposal of household waste

ABSTRACT Some empirical studies have attempted to clarify the mechanism of illegal dumping by examining the degree to which per-bag pricing plays a role. However, previous research on the behaviour of avoiding paying a charge for waste collection has tended to neglect so-called ‘immoral disposal,’ which is less risky than illegal dumping because there is no legal penalty. In this study, we define immoral disposal as the dumping of waste in a manner that is immoral but not illegal. To detect the existence of immoral disposal, we apply a spatial econometric approach, namely an extended panel spatial Durbin model, to identify the actual spillover effect of garbage pricing in neighbouring municipalities on immoral disposal from the total waste. A major finding of this study is that immoral disposal exists in unit-based pricing, two-tiered pricing, and fixed pricing.

Comment on “Measuring the Performance of Nations at Beijing Summer Olympics Using Integer-Valued DEA Model”

We correct the results by Wu et al. Although Wu et al. proposed an integer-valued data envelopment analysis (DEA) model, which is based on Charnes-Cooper-Rhodes (CCR) model, the displayed results are based on an integer-valued DEA model, which is based on Banker-Charnes-Cooper (BCC) model. Therefore, we introduce the true models and corresponding results.