We study a simple exogeneity test in count data models with possibly endogenous multinomial treatment. The test is based on Two Stage Residual Inclusion (2SRI), an estimation method which has been proved to be consistent for a general class of nonlinear parametric models. Results from a broad set of simulation experiments provide novel evidence on important features of this approach. We find differences in the finite sample performance of various likelihood-based tests, analyze their robustness to misspecification arising from neglected over-dispersion or from incorrect specification of the first stage model, and uncover that standardizing the variance of the first stage residuals leads to better results. An original application to testing the endogeneity status of insurance in a model of healthcare demand corroborates our Monte Carlo findings.

Geraci, A., Fabbri, D., Monfardini, C. (2018). Testing Exogeneity of Multinomial Regressors in Count Data Models: Does Two-stage Residual Inclusion Work?. JOURNAL OF ECONOMETRIC METHODS, 7(1), 1-19 [10.1515/jem-2014-0019].

Testing Exogeneity of Multinomial Regressors in Count Data Models: Does Two-stage Residual Inclusion Work?

GERACI, ANDREA;Fabbri, Daniele;Monfardini, Chiara
2018

Abstract

We study a simple exogeneity test in count data models with possibly endogenous multinomial treatment. The test is based on Two Stage Residual Inclusion (2SRI), an estimation method which has been proved to be consistent for a general class of nonlinear parametric models. Results from a broad set of simulation experiments provide novel evidence on important features of this approach. We find differences in the finite sample performance of various likelihood-based tests, analyze their robustness to misspecification arising from neglected over-dispersion or from incorrect specification of the first stage model, and uncover that standardizing the variance of the first stage residuals leads to better results. An original application to testing the endogeneity status of insurance in a model of healthcare demand corroborates our Monte Carlo findings.
2018
Geraci, A., Fabbri, D., Monfardini, C. (2018). Testing Exogeneity of Multinomial Regressors in Count Data Models: Does Two-stage Residual Inclusion Work?. JOURNAL OF ECONOMETRIC METHODS, 7(1), 1-19 [10.1515/jem-2014-0019].
Geraci, Andrea; Fabbri, Daniele; Monfardini, Chiara
File in questo prodotto:
File Dimensione Formato  
ag_df_cm_jem-2014-0019_pub.pdf

Open Access dal 09/11/2017

Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione - Non commerciale - Non opere derivate (CCBYNCND)
Dimensione 1.02 MB
Formato Adobe PDF
1.02 MB Adobe PDF Visualizza/Apri
jem-2014-0019_suppl.pdf

accesso aperto

Descrizione: Supplemental Material
Tipo: File Supplementare
Licenza: Licenza per Accesso Aperto. Altra tipologia di licenza compatibile con Open Access
Dimensione 153.83 kB
Formato Adobe PDF
153.83 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/627291
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact