Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/247535 
Year of Publication: 
2019
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 7 [Issue:] 3 [Publisher:] MDPI [Place:] Basel [Year:] 2019 [Pages:] 1-22
Publisher: 
MDPI, Basel
Abstract: 
This paper introduces an estimation procedure for a random effects probit model in presence of heteroskedasticity and a likelihood ratio test for homoskedasticity. The cases where the heteroskedasticity is due to individual effects or idiosyncratic errors or both are analyzed. Monte Carlo simulations show that the test performs well in the case of high degree of heteroskedasticity. Furthermore, the power of the test increases with larger individual and time dimensions. The robustness analysis shows that applying the wrong approach may generate misleading results except for the case where both individual effects and idiosyncratic errors are modelled as heteroskedastic.
Subjects: 
Gauss&#x2013
Hermite quadrature
heteroskedasticity
Monte Carlo simulation
panel data
probit
JEL: 
C23
C61
C63
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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