Hideo Noda

Bayesian analysis of cross‐prefectural production function with time varying structure in Japan

A cross‐prefectural production function (CPPF) in Japan is constructed in a set of Bayesian models to examine the performance of Japan’s post‐war economy. The parameters in the model are estimated by using the procedure of a Monte Carlo filter together with the method of maximum likelihood. The estimated results are applied to regional and historical analysis of the Japanese economy.

Bayesian estimation of dynamic matching function for U-V analysis in Japan

In this paper we propose a Bayesian method for analyzing unemployment dynamics. We derive a Beveridge curve for unemployment and vacancy (U-V) analysis from a Bayesian model based on a labor market matching function. In our framework, the efficiency of matching and the elasticities of new hiring with respect to unemployment and vacancy are regarded as time varying parameters. To construct a flexible model and obtain reasonable estimates in an underdetermined estimation problem, we treat the time varying parameters as random variables and introduce smoothness priors. The model is then described in a state space representation, enabling the parameter estimation to be carried out using Kalman filter and fixed interval smoothing. In such a representation, dynamic features of the cyclic unemployment rate and the structural-frictional unemployment rate can be accurately captured.

Bayesian analysis of the dynamic structure in China’s economic growth

To analyze the dynamic structure in China’s economic growth during the period 1952–1998, we introduce a model of the aggregate production function for the Chinese economy that considers total factor productivity (TFP) and output elasticities as time‐varying parameters. Specifically, this paper is concerned with the relationship between the rate of economic growth in China and the trend in TFP. Here, we consider the time‐varying parameters as random variables and introduce smoothness priors to construct a set of Bayesian linear models for parameter estimation. The results of the estimation are in agreement with the movements in China’s social economy, thus illustrating the validity of the proposed methods.

Do commercial sales move coincidentally with business cycles in Japan? a dynamic two-mode regression approach

ABSTRACT This paper examines the validity of including wholesale and retail commercial sales in the coincident index of business conditions in Japan. Specifically, we consider the movement of gross domestic product (GDP) as a reference cycle, and investigate whether commercial sales move coincidentally with GDP by applying the dynamic two-mode regression approach. The results show that wholesale commercial sales are not justified as a component of the Japanese coincident index, but that retail commercial sales may be considered a legitimate component of the Japanese coincident index.

A new approach to data envelopment analysis and its application to industries in Japanese prefectures

In this paper, we propose a new approach for determining the unknown quantities in Banker-Charnes-Cooper models for data envelopment analysis by developing the marginal model synthesization algorithm. In this algorithm, several marginal fractional programming models are first constructed based on a simple numeric optimization. Then, a set of synthetic Banker-Charnes-Cooper models is obtained by compounding the marginal fractional programming models. The marginal model synthesization algorithm is straightforward and computationally efficient. We also present an application of the proposed approach for analyzing the efficiency of industries in Japanese prefectures.

Bayesian analysis of unemployment dynamics in Japan

In this paper, we investigate the unemployment dynamics in Japan within the framework of Bayesian modelling. To consider structural changes in a model for the matching function specified in Cobb-Douglas form, we regard not only the matching efficiency but also the elasticities of new hiring with respect to unemployment and with respect to vacancies as time-varying parameters. Then, from a Bayesian perspective, these are treated as random variables and smoothness priors are introduced. In addition, a set of models for the matching function and the smoothness priors is described in a state space representation. The parameter estimation is carried out using Kalman filter and fixed-interval smoothing. The average for the period between January 2009 and December 2010 suggests that 60% of the total unemployment rate was a result of structural and frictional factors and that 40% was attributable to a labour demand deficiency. Further, in terms of matching efficiency, the Japanese labour market is not viewed as functioning effectively even in the late 2000s.