WHAT HAPPENS WHEN DEMAND IS ESTIMATED WITH A MISSPECIFIED MODEL?

We conduct Monte Carlo experiments to investigate the biases of assuming a misspecified demand model. We Study continuous models (linear, log-linear and AIDS), and discrete choice models (logit) in the context of differentiated products and aggregate data. Estimating demand with the 'wrong' model yields varying degrees of bias in estimated elasticities, but the logit model can yield unbiased estimates for it certain size of the assumed market potential. Merger simulations confirm the key importance of market potential in logit estimation suggesting that a discrete choice model may be preferable even when the discreteness of the purchase decision is questionable.