Instrumental variable estimation of heteroskedasticity adaptive error component models
The linear panel data estimator proposed by Hausman and Taylor relaxes the hypothesis of exogenous regressors that is assumed by generalized least squares methods but, unlike the Fixed Effects estimator, it can handle endogenous time invariant explanatory variables in the regression equation. One of...
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| Format: | Journal article |
| Language: | English |
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2012
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| _version_ | 1797054058943479808 |
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| author | Fé, E |
| author_facet | Fé, E |
| author_sort | Fé, E |
| collection | OXFORD |
| description | The linear panel data estimator proposed by Hausman and Taylor relaxes the hypothesis of exogenous regressors that is assumed by generalized least squares methods but, unlike the Fixed Effects estimator, it can handle endogenous time invariant explanatory variables in the regression equation. One of the assumptions underlying the estimator is the homoskedasticity of the error components. This can be restrictive in applications, and therefore in this paper the assumption is relaxed and more efficient adaptive versions of the estimator are presented. © 2011 Springer-Verlag. |
| first_indexed | 2024-03-06T18:52:14Z |
| format | Journal article |
| id | oxford-uuid:10a0c979-6ff4-45d1-bf4b-38b7e2a2c2b3 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T18:52:14Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | oxford-uuid:10a0c979-6ff4-45d1-bf4b-38b7e2a2c2b32022-03-26T09:57:27ZInstrumental variable estimation of heteroskedasticity adaptive error component modelsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:10a0c979-6ff4-45d1-bf4b-38b7e2a2c2b3EnglishSymplectic Elements at Oxford2012Fé, EThe linear panel data estimator proposed by Hausman and Taylor relaxes the hypothesis of exogenous regressors that is assumed by generalized least squares methods but, unlike the Fixed Effects estimator, it can handle endogenous time invariant explanatory variables in the regression equation. One of the assumptions underlying the estimator is the homoskedasticity of the error components. This can be restrictive in applications, and therefore in this paper the assumption is relaxed and more efficient adaptive versions of the estimator are presented. © 2011 Springer-Verlag. |
| spellingShingle | Fé, E Instrumental variable estimation of heteroskedasticity adaptive error component models |
| title | Instrumental variable estimation of heteroskedasticity adaptive error component models |
| title_full | Instrumental variable estimation of heteroskedasticity adaptive error component models |
| title_fullStr | Instrumental variable estimation of heteroskedasticity adaptive error component models |
| title_full_unstemmed | Instrumental variable estimation of heteroskedasticity adaptive error component models |
| title_short | Instrumental variable estimation of heteroskedasticity adaptive error component models |
| title_sort | instrumental variable estimation of heteroskedasticity adaptive error component models |
| work_keys_str_mv | AT fee instrumentalvariableestimationofheteroskedasticityadaptiveerrorcomponentmodels |