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A common problem in estimation of discrete choice models is that the complete choice set is very large. A good example is supermarket consumer goods, like breakfast cereal, where there are often a hundred or more varieties (SKUs or UPCs) to choose from. In that case, estimation of complex discrete choice models where choice probabilities have no closed form(More)
Recent Monte Carlo work on choosing experimental designs for discrete choice experiments seemed to greatly simplify this choice for applied researchers. It suggested that (a) commonly used designs can generate unbiased estimates for indirect utility function specifications with main effects only and main effects plus higher order terms, and (b) random(More)
In recent years it has become common to use stated preference (SP) discrete choice experiments (DCEs) to study and/or predict consumer demand. SP is particularly useful when revealed preference (RP) data is unobtainable or uninformative (e.g., to predict demand for a new product with an attribute not present in existing products, to value non-traded goods).(More)
☆ Thefirst authorwould like to acknowledge research su Council grant DP 0774142 and UTS Faculty of Business Re University of Technology Sydney. Jordan Louviere provide mental design, Yen-Ching Sung provided assistance on in and Edward Wei provided invaluable assistance in Panpiemras and participants at several conference and s helpful comments on earlier(More)
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