Estimating Separable Matching Models
In this paper we propose two simple methods to estimate models of matching with transferable and separable utility introduced in Galichon and Salani\'e (2022). The first method is a minimum distance estimator that relies on the generalized entropy of matching. The second relies on a reformulation of the more special but popular Choo and Siow (2006) model; it uses generalized linear models (GLMs) with two-way fixed effects.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
The Econometrics and Some Properties of Separable Matching Models
We present a class of one-to-one matching models with perfectly transferable utility. We discuss identification and inference in these separable models, and we show how their comparative statics are readily analyzed.
EconometricsGeneralizing Graph Matching beyond Quadratic Assignment Model
Graph matching has received persistent attention over decades, which can be formulated as a quadratic assignment problem (QAP). We show that a large family of functions, which we define as Separable Functions, can approx…
Graph MatchingmodelSeparable Computation of Information Measures
We study a separable design for computing information measures, where the information measure is computed from learned feature representations instead of raw data. Under mild assumptions on the feature representations, w…
Representation LearningEstimating Nonseparable Selection Models: A Functional Contraction Approach
We propose a novel method for estimating nonseparable selection models. We show that, given the selection rule and the observed selected outcome distribution, the potential outcome distribution can be characterized as th…
Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization
An increasing number of models require the control of the spectral norm of convolutional layers of a neural network. While there is an abundance of methods for estimating and enforcing upper bounds on those during traini…
image-classificationImage Classification