This paper considers the identification and estimation of hedonic models. We establish that in an additive version of the hedonic model, technology and preferences are generically identified up to affine transformations from data on demand and supply in a single hedonic market. For a very...

Consider a bipartite network where <i>N</i> consumers choose to buy or not to buy <i>M</i> different products. This paper considers the properties of the logistic regression of the <i>N</i> × <i>M</i> array of "i-buys-j" purchase decisions, <i>[Y_ij]_{1≤i≤N,≤j≤M}</i>, onto known functions of consumer and product attributes...

Consider a bipartite network where N consumers choose to buy or not to buy M different products. This paper considers the properties of the logistic regression of the N ×M array of "i-buys-j" purchase decisions, [Yij ] 1≤i≤N,1≤j≤M, onto known functions of consumer and product attributes...