Modified maximum likelihood estimation of Tobit models with fixed effects: theory and an application to earnings equations

Gabriel Jimenez

Investigaciones Economicas, 2005, vol. 29, issue 3, 575-607

Abstract: This paper starts from the orthogonalization method proposed by Cox and Reid which is aplied to the Tobit model panel for data with fixed effects. Neyman and Scott showed that, generally, the maximum likelihood estimator is inconsistent (the incidental parameter problem). The methodology explained here recovers the use of the log-likelihood function to solve this problem taking advantage of the time-series dimension of panel data. For the Tobit model we show when is it possible to recover the orthogonal parameters, and study the characteristics of the estimators obtained with simulation methods. Also, an illustration for earnings equations has been performed.

Keywords: Orthogonal parameters; modified profile likelihood; panel data; Tobit model (search for similar items in EconPapers)
JEL-codes: C15 C23 C24 (search for similar items in EconPapers)
Date: 2005
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.fundacionsepi.es/investigacion/revista ... /Sep2005/v29i3a5.pdf Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:iec:inveco:v:29:y:2005:i:3:p:575-607

Ordering information: This journal article can be ordered from
https://www.fundacio ... tas/presentacion.asp

Access Statistics for this article

Investigaciones Economicas is currently edited by Antonio Cabrales and Pedro Mira

More articles in Investigaciones Economicas from Fundación SEPI Investigaciones Economicas Fundación SEPI Quintana, 2 (planta 3) 28008 Madrid Spain.
Bibliographic data for series maintained by Isabel Sánchez-Seco ( this e-mail address is bad, please contact ).