Events

Professor Hiro Kasahara
Professor, Vancouver School of Economics
The University of British Columbia

Commonly used methods of production function and markup estimation assume that a
firm’s output quantity can be observed as data, but typical datasets contain only revenue,
not output quantity. We examine the nonparametric identification of production function
and markup from revenue data when a firm faces a general nonparametric demand function
under imperfect competition. Under standard assumptions, we provide the constructive
nonparametric identification of various firm-level objects: gross production function, total
factor productivity, price markups over marginal costs, output prices, output quantities,
a demand system, and a representative consumer’s utility function. We develop a simple
semiparametric estimator implementable with standard datasets. In simulation, our method
performs as well as standard methods using quantity data.