A negative binomial model and moment conditions for count panel data

This paper proposes some moment conditions associated with an appropriate specification of negative binomial model for count panel data, which is proposed by Hausman et al. (1984). The newly proposed moment conditions enable researchers to conduct the consistent estimation of the model under much weaker assumptions than those configured by Hausman et al. (1984). In some Monte Carlo experiments, it is shown that the GMM estimators using the new moment conditions perform well in the DGP configurations conforming to the specification above.count panel data, predetermined explanatory variable, linear feedback model, overdispersion, negative binomial model, implicit operation, cross-linkage moment conditions, GMM, Monte Carlo experiments

Size of economic activity and occurrence of fatal traffic accidents: a count panel data analysis on Fukuoka prefecture in Japan

In this paper, the investigation is conducted on the relationship between the number of fatalities by dint of traffic accidents and the gross municipal product, by using the panel data whose crosssectional units are composed of municipalities in Fukuoka Prefecture in Japan. It turns out that the conventional quasi-differenced GMM estimator gives unconvincing results, while some of the GMM estimators proposed by Kitazawa (2007) give convincing results. The convincing results suggest that the diseconomy of scale is recognized in the occurrence of traffic fatalities.traffic accident, number of fatalities, gross municipal product, count panel data, GMM, diseconomy of scale

A forward demeaning transformation for a dynamic count panel data model

In this note, a forward demeaning transformation is proposed for the linear feedback model with the explanatory variables being strictly exogenous on count panel data. This transformation is analogous to that proposed by Arellano and Bover (1995) for the ordinary dynamic panel data model.forward demeaning; linear feedback model; strictly exogenous explanatory variables; count panel data

Equidispersion and moment conditions for count panel data model

This paper proposes some new moment conditions under the assumption of the equidispersion in count panel data model. These are obtained by using the association between variances and covariances in the disturbance. Some Monte Carlo experiments configured for the Poisson model show that the GMM estimators using the new moment conditions perform better than the conventional quasi-differenced GMM estimator and some gains are recognized in using the new moment conditions.count panel data, linear feedback model, equidispersion, implicit operation, crosslinkage moment conditions, GMM, Monte Carlo experiments

"Global Warming and Transport Policies"

The Japanese government has advocated a wide range of policy measures to reduce greenhouse gas emissions from transportation, e.g. improvements of gas mileage, development of alternative fuel vehicles, shifts to walking, bicycles and public transportation for passenger transportation and to trains and ships for cargos, greening of highways. The details of these policies and their effectiveness are not clear, however. Furthermore, virtually no analysis has been provided on the costs and benefits of these policy measures. Unfortunately, the Japanese government has been slow to develop the data infrastructure needed for such an evaluation. This article reviews the studies conducted in the U.S. and Europe, and examines what sort of research is necessary in Japan.

A hyperbolic transformation for a fixed effects logit model

In this paper, a simple transformation is proposed for the fixed effects logit model, using which some valid moment conditions including the first-order condition for one of the conditional MLE proposed by Chamberlain (1980) can be generated. Some Monte Carlo experiments are carried out for the GMM estimator based on the transformation.fixed effects logit; conditional logit estimator; hyperbolic transformation; moment conditions; GMM; Monte Carlo experiments

Average elasticity in the framework of the fixed effects logit model

This note proposes the average elasticity of the logit probabilities with respect to the exponential functions of explanatory variables in the framework of the fixed effects logit model. The average elasticity is able to be calculated using the consistent estimators of parameters of interest and the average of binary dependent variables, regardless of the fixed effects.average elasticity; fixed effects logit model

A note on kernel principal component regression

Kernel principal component regression (KPCR) was studied by Rosipal et al. [18, 19, 20], Hoegaerts et al. [7], and Jade et al. [8]. However, KPCR still encounters theoretical difficulties in the procedure for constructing KPCR and in the choice rule for the retained number of principal components. In this paper, we revise the method of KPCR to overcome the difficulties. The performance of the revised method is compared to linear regression, nonlinear regression based on Gompertz function, and nonparametric Nadaraya-Watson regression, and gives better results than those of the three methods