This paper develops a set of empirically tractable and flexible sieve estimators for semi-nonparametric multidimensional matching models with transferable utility, focusing on worker-job matching. We generalize the parametric quadraticGaussian framework employed by Bojilov and Galichon (2016)...

This paper proposes empirically tractable multidimensional matching models, focusing on worker-job matching. We generalize the parametric model proposed by Lindenlaub (2017), which relies on the assumption of joint normality of observed characteristics of workers and jobs. In our paper, we allow...

This paper proposes empirically tractable multidimensional matching models, focusing on worker-job matching. We generalize the parametric model proposed by Lindenlaub (2017), which relies on the assumption of joint normality of observed characteristics of workers and jobs. In our paper, we allow...

This book uniquely explores popular optimization, presenting fundamental problems from diverse sources. It aims to cultivate a non-traditional problem-solving approach applicable to both optimization and broader contexts. The author introduces optimization models for well-known problems and...