Abstract
Nearly all theoretically motivated models of consumer demand for multiple goods assume additive separability in preferences, i.e. the consumption utility of each good x is independent of the quantity demanded of another good y. This is a strong restriction that makes the solution of the consumer’s utility maximization problem computationally tractable. This paper shows that assuming preferences are weakly separable yields a similar simplification. It offers a theoretically founded model of consumer demand for continuous quantities of related goods. It also proposes a Bayesian estimation approach with a parsimonious parameterization that allows for corner solutions. It is the first structural model of individual demand for multiple goods which relaxes additive separability and does not suffer from a curse of dimensionality in the number of chosen goods. The model is estimated using data on Chilean advertisers’ television audience purchases. We find that advertisers prefer spreading expenditures across time blocks more so than spreading expenditures across networks within a time block. The model nests additively separable preferences as a special case, but the data reject this case. We illustrate how a television network could use the model to assess the consequences of different advertisement pricing policies conditional on competitive response assumptions.
Published Version
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