Prediction in the Panel Data Model with Spatial Correlation
The econometrics of spatial models have focused mainly on estimation and testing of hypotheses, see Anselin (1988b), Anselin et al. (1996) and Anselin and Bera (1998) to mention a few. In this chapter we focus on prediction in spatial models based on panel data. In particular, we consider a simple demand equation for cigarettes based on a panel of 46 states over the period 1963–1992. The spatial autocorrelation due to neighboring states and the individual heterogeneity across states is taken explicitly into account. In order to explain how spatial autocorrelation may arise in the demand for cigarettes, we note that cigarette prices vary among states, primarily due to variation in state taxes on cigarettes. For example, in 1988, state excise taxes ranged from 2 cents per pack in a producing state like North Carolina, to 38 cents per pack in the state of Minnesota. In 1997, these state taxes varied from a low of 2.5 cents per pack for Virginia to $1.00 per pack in Alaska and Hawaii. Since cigarettes can be stored and are easy to transport, these varying taxes result in casual smuggling across neighboring states. For example, while New Hampshire had a 12 cents per pack tax on cigarettes in 1988, neighboring Massachusetts and Maine had a 26 and 28 cents per pack tax. Border effect purchases not explained in the demand equation can cause spatial autocorrelation among the disturbances.1
KeywordsRoot Mean Square Error Spatial Autocorrelation Fixed Effect Elasticity Estimate Random Effect
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