Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/64713 
Year of Publication: 
2012
Series/Report no.: 
cemmap working paper No. CWP19/12
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
This paper develops asymptotic theory for estimated parameters in differentiated product demand systems with a fixed number of products, as the number of markets T increases, taking into account that the market shares are approximated by Monte Carlo integration. It is shown that the estimated parameters are √T consistent and asymptotically normal as long as the number of simulations R grows fast enough relative to T. Monte Carlo integration induces both additional variance as well additional bias terms in the asymptotic expansion of the estimator. If R does not increase as fast as T, the leading bias term dominates the leading variance term and the asymptotic distribution might not be centered at 0. This paper suggests methods to eliminate the leading bias term from the asymptotic expansion. Furthermore, an adjustment to the asymptotic variance is proposed that takes the leading variance term into account. Monte Carlo results show that these adjustments, which are easy to compute, should be used in applications to avoid severe undercoverage caused by the simulation error.
Subjects: 
demand estimation
differentiated products
many markets
asymptotic theory
simulation error
bias correction
adjusted standard errors
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
File
Size
387.46 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.