🤖 AI Summary
This study addresses the challenge of accurately estimating demand functions for new products based on consumers’ willingness-to-pay (WTP). It proposes a novel paradigm that directly identifies demand functions from WTP data by constructing a general yet analytically tractable parametric demand model and designing a consistent estimation procedure. Theoretical analysis and Monte Carlo simulations demonstrate that the proposed method effectively recovers the true underlying demand function and exhibits robust performance across a variety of model specifications. Combining theoretical rigor with practical applicability, this approach offers a versatile tool for academic research, business decision-making, and policy evaluation.
📝 Abstract
This paper develops an approach for estimating demand for a new product. Taking willingness to pay (WTP) as primitive, it establishes a general and yet analytically simple demand function, and proposes an estimation procedure that consistently recovers the underlying demand function from the WTP data. Monte Carlo simulations find the estimation procedure works well in identifying the demand function. This approach complements existing methods of demand estimation, and can be applied both within and outside academia, for example in teaching economics, for a business to launch new products, and for policymakers to conduct non-market valuation.