WHAT HAPPENS WHEN DEMAND IS ESTIMATED WITH A MISSPECIFIED MODEL?*
通过蒙特卡洛实验，研究了使用错误设定的需求模型（如线性模型、对数线性模型、AIDS和Logit）估计弹性时产生的偏差，发现Logit模型在特定市场潜力下可得到无偏估计，并指出离散选择模型在合并模拟中的优势。
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 a 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.