Inference in differences-in-differences with few treated groups and heteroskedasticity

Detalhes bibliográficos
Autor(a) principal: Ferman, Bruno
Data de Publicação: 2016
Outros Autores: Pinto, Cristine Campos de Xavier
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional do FGV (FGV Repositório Digital)
Texto Completo: http://hdl.handle.net/10438/17582
Resumo: Seminário de Econometria da Boston University
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spelling Ferman, BrunoPinto, Cristine Campos de XavierDemais unidades::RPCA2016-12-15T18:42:42Z2016-12-15T18:42:42Z2016http://hdl.handle.net/10438/17582Seminário de Econometria da Boston UniversityWe show that the usual inference methods used in Di fferences-in-Di fferences (DID) might not perform well with few treated groups and heteroskedastic errors. One important example is when there is variation in the number of observations per group, as this generates heteroskedasticity in the aggregate DID model. In this case, methods designed to work with few treated groups tend to (under-) over-reject when the treated groups are (large) small relative to the control groups. We provide Monte Carlo simulations and placebo regressions with real datasets showing that this problem is relevant even in datasets with many observations per group. We then derive an alternative inference method that works when there are few treated groups (oreven just one) and many control groups in the presence of heteroskedasticity. Our method assumes that wecan model the heteroskedasticity of a linear combination of the errors. We show that this assumption can be satis ed without imposing strong restrictions in common DID applications. Importantly, we do not need to specify the structure of the serial correlation of the errors. Our inference method can also be combined with feasible generalized least squares (FGLS) estimation. This way, we attain an asymptotically uniformly most powerful (UMP) test if the FGLS t-test is asymptotically UMP, while still provide correct size if the serial correlation is misspeci ed. We also provide an alternative inference method that relaxes our main assumption when the number of pre-treatment periods is large and we extend our methods to linear factor models with few treated groups.engDifferences-in-differencesInferenceHeteroskedasticityClusteringFew clustersBootstrapLinear factor modelEconomiaModelos lineares (Estatística)Inferência (Lógica)Inference in differences-in-differences with few treated groups and heteroskedasticityinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectreponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas (FGV)instacron:FGVinfo:eu-repo/semantics/openAccessRede de Pesquisa e Conhecimento AplicadoTEXTInference_in_Differences_in_Differences_With_few_Treated_Groups_and_Heteroskedasticity.pdf.txtInference_in_Differences_in_Differences_With_few_Treated_Groups_and_Heteroskedasticity.pdf.txtExtracted 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dc.title.eng.fl_str_mv Inference in differences-in-differences with few treated groups and heteroskedasticity
title Inference in differences-in-differences with few treated groups and heteroskedasticity
spellingShingle Inference in differences-in-differences with few treated groups and heteroskedasticity
Ferman, Bruno
Differences-in-differences
Inference
Heteroskedasticity
Clustering
Few clusters
Bootstrap
Linear factor model
Economia
Modelos lineares (Estatística)
Inferência (Lógica)
title_short Inference in differences-in-differences with few treated groups and heteroskedasticity
title_full Inference in differences-in-differences with few treated groups and heteroskedasticity
title_fullStr Inference in differences-in-differences with few treated groups and heteroskedasticity
title_full_unstemmed Inference in differences-in-differences with few treated groups and heteroskedasticity
title_sort Inference in differences-in-differences with few treated groups and heteroskedasticity
author Ferman, Bruno
author_facet Ferman, Bruno
Pinto, Cristine Campos de Xavier
author_role author
author2 Pinto, Cristine Campos de Xavier
author2_role author
dc.contributor.unidadefgv.por.fl_str_mv Demais unidades::RPCA
dc.contributor.author.fl_str_mv Ferman, Bruno
Pinto, Cristine Campos de Xavier
dc.subject.eng.fl_str_mv Differences-in-differences
Inference
Heteroskedasticity
Clustering
Few clusters
Bootstrap
Linear factor model
topic Differences-in-differences
Inference
Heteroskedasticity
Clustering
Few clusters
Bootstrap
Linear factor model
Economia
Modelos lineares (Estatística)
Inferência (Lógica)
dc.subject.area.por.fl_str_mv Economia
dc.subject.bibliodata.por.fl_str_mv Modelos lineares (Estatística)
Inferência (Lógica)
description Seminário de Econometria da Boston University
publishDate 2016
dc.date.accessioned.fl_str_mv 2016-12-15T18:42:42Z
dc.date.available.fl_str_mv 2016-12-15T18:42:42Z
dc.date.issued.fl_str_mv 2016
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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language eng
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