A Framework for Analyzing Rank Ordered Panel Data with Application to Automobile Demand
In this paper we develop a framework for analyzing panel data with observations on rank ordered alternatives that allows for correlated random taste shifters across time and across alternatives. As a special case we obtain a nested logit model type for rank ordered alternatives. We have applied this framework to estimate several model versions for household demand for conventional and alternative fuel automobiles in Shanghai based on rank ordered data obtained from a stated preference survey. The preferred model is then used to calculate demand probabilities and elasticities and the willingness-to-pay for alternative fuel vehicles.
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