Work

Counts with an endogenous binary regressor: A series expansion approach

Year: 2005

Type: article

Abstract: We propose an estimator for count data regression models where a binary regressor is endogenously determined. This estimator departs from previous approaches by using a flexible form for the conditional probability function of the counts. Using a Monte Carlo experiment we show that our estimator improves the fit and provides a more reliable estimate of the impact of regressors on the count when compared to alternatives which do restrict the mean to be linear‐exponential. In an application to the number of trips by households in the United States, we find that the estimate of the treatment effect obtained is considerably different from the one obtained under a linear‐exponential mean specification.

Source: Econometrics Journal

DOI: 10.1111/j.1368-423x.2005.00148.x

Authors: Andrés Romeu, Marcos Vera‐Hernández

Institutions: University of Alicante, Institute for Fiscal Studies

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: Universidad de Alicante, Universidad de La Laguna, University College London, Generalitat de Catalunya, Universitat de Barcelona

OpenAlex ID: W2054907758

FWCI: 2.186

Cites: 32

Cited by: 17

Related to: 10