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BEGIN:VEVENT
DTSTAMP:20260429T131541Z
DTSTART;VALUE=DATE-TIME:20250521T140000
DTEND;VALUE=DATE-TIME:20250521T153000
SUMMARY:Econometrics Seminar - Lorenzo Magnolfi (Wisconsin)
TZID:Europe/London
UID:20250521-8ac672c795a816850195ae23fa955036@warwick.ac.uk
CREATED:20250515T075205Z
DESCRIPTION:Title: Market Counterfactuals with Nonparametric Supply: An M
 L/AI Approach (with Harold Chiang\, Jack Collison\, and Chris Sullivan).
  Abstract: This paper develops a flexible approach to perform market cou
 nterfactuals using machine learning methods and nonparametric structure 
 from economics. While standard structural methods rely on restrictive as
 sumptions about firm conduct and cost\, we propose a data-driven framewo
 rk that relaxes these constraints when rich market data are available. B
 uilding on the identification results of Berry and Haile (2014) we devel
 op a nonparametric model of supply that nests traditional conduct specif
 ications while allowing for more complex competitive interactions. We ad
 apt the Variational Method of Moments (VMM) (Bennett and Kallus\, 2023) 
 to estimate this flexible model\, addressing the endogeneity of market s
 hares and the high dimensionality of the problem. Our approach enables a
  wide range of counterfactual exercises including tax policy analysis\, 
 product regulation\, and merger simulation. Monte Carlo simulations demo
 nstrate that our method substantially outperforms standard approaches\; 
 applied to the American Airlines-US Airways merger\, our method produces
  more accurate post-merger price predictions.
LOCATION:S0.13
CATEGORIES:Econometrics Seminar
LAST-MODIFIED:20250515T075205Z
ORGANIZER;CN=Gill Gudger:
END:VEVENT
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