Inference and estimation in small sample dynamic panel data models

We study the finite sample properties of the most important methods of estimation of dynamic panel data models in a special class of small samples: a two-sided small sample (i.e., a sample in which the time dimension is not that short but the cross-section dimension is not that large). This case is...

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Detalles Bibliográficos
Autores principales: Galiani, Sebastián, González-Rozada, Martín
Formato: Documento de trabajo acceptedVersion
Lenguaje:Inglés
Publicado: Universidad Torcuato Di Tella. Escuela de Negocios. Centro de Investigaciones en Finanzas (CIF) 2018
Materias:
Inferencia estadística
Muestreo
Análisis financiero
Acceso en línea:http://repositorio.utdt.edu/handle/utdt/10758
Aporte de:
Repositorio Digital Universidad Torcuato Di Tella de Universidad Torcuato Di Tella
id I57-R16320.500.13098-10758
record_format dspace
institution Universidad Torcuato Di Tella
institution_str I-57
repository_str R-163
collection Repositorio Digital Universidad Torcuato Di Tella
language Inglés
orig_language_str_mv eng
topic Inferencia estadística
Muestreo
Análisis financiero
spellingShingle Inferencia estadística
Muestreo
Análisis financiero
Galiani, Sebastián
González-Rozada, Martín
Inference and estimation in small sample dynamic panel data models
description We study the finite sample properties of the most important methods of estimation of dynamic panel data models in a special class of small samples: a two-sided small sample (i.e., a sample in which the time dimension is not that short but the cross-section dimension is not that large). This case is encountered increasingly in applied work. Our main results are the following: the estimator proposed by Kiviet (1995) outperforms all other estimators con- sidered in the literature. However, standard statistical inference is not valid for any of them. Thus, to assess the true sample variability of the parameter estimates, bootstrap standard er- rors have to be computed. We find that standard bootstrapping techniques work well except when the autoregressive parameter is close to one. In this last case, the best available solution is to estimate standard errors by means of the Grid-t bootstrap estimator due to Hansen (1999).
format Documento de trabajo
acceptedVersion
author Galiani, Sebastián
González-Rozada, Martín
author_facet Galiani, Sebastián
González-Rozada, Martín
author_sort Galiani, Sebastián
title Inference and estimation in small sample dynamic panel data models
title_short Inference and estimation in small sample dynamic panel data models
title_full Inference and estimation in small sample dynamic panel data models
title_fullStr Inference and estimation in small sample dynamic panel data models
title_full_unstemmed Inference and estimation in small sample dynamic panel data models
title_sort inference and estimation in small sample dynamic panel data models
publisher Universidad Torcuato Di Tella. Escuela de Negocios. Centro de Investigaciones en Finanzas (CIF)
publishDate 2018
url http://repositorio.utdt.edu/handle/utdt/10758
work_keys_str_mv AT galianisebastian inferenceandestimationinsmallsampledynamicpaneldatamodels
AT gonzalezrozadamartin inferenceandestimationinsmallsampledynamicpaneldatamodels
bdutipo_str Repositorios
_version_ 1764820543040651264

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