Abstract

Analysis of spatial panel data is of great importance and inter est in spatial econometrics. Here we consider cigarette demand in a spatial panel of 46 states of the US over a 30-year period. We construct a de mand equation to examine the elasticity of per pack cigarette price and per capita disposable income. The existing spatial panel models account for both spatial autocorrelation and state-wise heterogeneity, but fail to account for temporal autocorrelation. Thus we propose new spatial panel models and adopt a fully Bayesian approach for model parameter inference and predic tion of cigarette demand at future time points using MCMC. We conclude that the spatial panel model that accounts for state-wise heterogeneity, spa tial dependence, and temporal dependence clearly outperforms the existing models. Analysis based on the new model suggests a negative cigarette price elasticity but a positive income elasticity.

Highlights

  • In econometric terms, panel data are observations aggregated on a crosssection over multiple time periods

  • We considered a demand equation to examine the effect of price and income on cigarette demand, based on a spatial panel of 46 states of the US over a 30-year period

  • We showed that the analysis results are comparable between maximum likelihood estimates (MLE) in Baltagi and Li (2004) and our Bayesian inference, based on the existing spatial panel models that do not account for temporal dependence

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Summary

Introduction

Panel data are observations aggregated on a crosssection over multiple time periods. Baltagi and levin (1986) were the first to consider this spatial panel from 1963 to 1980 and constructed a dynamic demand equation for cigarettes to address several major policy issues Their data analysis yielded a significant negative effect of cigarette price on cigarette consumption with a price elasticity of −0.2, while there was no effect of income on cigarette consumption with an insignificant income elasticity. Baltagi and Li (2004) considered the same spatial panel with the time period updated to range from 1963 to 1992 They constructed a simple demand equation for cigarettes to examine the elasticity of cigarette price and that of income. They modeled the bootlegging effect but as a part of spatial dependence, which may be thought of as an improvement of the explanatory variable approach taken in Baltagi and Levin (1986). Model comparisons based on an information criterion and prediction performance are given in Section 5, followed by a brief conclusion in

Bayesian Inference for the Existing Spatial Panel Models
Homogeneous model
Heterogeneous model
Fixed-effects model
Random-effects model
New Spatial Panel Models and Bayesian Inference
The existing spatial panel models
The new spatial panel models
Model selection based on DIC
Model validation based on prediction
Conclusion and Discussion
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