Name
Abstract | ||
|---|---|---|
We reconsider the classic problem of recovering exogenous variation from an endogenous regressor. Two-stage least squares recovers exogenous variation through presuming the existence of an instrumental variable. We rely instead on the assumption that the regressor is a mixture of exogenous and endogenous observations--say as the result of temporary natural experiments. With this assumption, we propose an alternative two-stage method based on nonparametrically estimating a mixture model to recover a subset of the exogenous observations. We demonstrate that our method recovers exogenous observations in simulation and can be used to find pricing experiments hidden in grocery store scanner data. |
Year | Venue | Field |
|---|---|---|
2018 | AAAI Workshops | Econometrics,Least squares,Instrumental variable,Statistics,Mixture model,Mathematics |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
1 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eliot Abrams | 1 | 0 | 0.34 |
| George Gui | 2 | 0 | 0.68 |
| j anthony cookson | 3 | 6 | 2.67 |