Arco: an artificial counterfactual approach for high-dimensional panel time-series data
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional do FGV (FGV Repositório Digital) |
Texto Completo: | http://hdl.handle.net/10438/18334 |
Resumo: | We consider a new, flexible and easy-to-implement method to estimate causal effects of an intervention on a single treated unit and when a control group is not readily available. We propose a two-step methodology where in the first stage a counterfactual is estimated from a large-dimensional set of variables from a pool of untreated units using shrinkage methods, such as the Least Absolute Shrinkage Operator (LASSO). In the second stage, we estimate the average intervention effect on a vector of variables, which is consistent and asymptotically normal. Our results are valid uniformly over a wide class of probability laws. Furthermore, we show that these results still hold when the exact date of the intervention is unknown. Tests for multiple interventions and for contamination effects are also derived. By a simple transformation of the variables of interest, it is also possible to test for intervention effects on several moments (such as the mean or the variance) of the variables of interest. A Monte Carlo experiment evaluates the properties of the method in finite samples and compares it with other alternatives such as the differences-in-differences, factor and the synthetic control methods. In an application we evaluate the effects on inflation of an anti tax evasion program. |
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Carvalho, Carlos Viana deMasini, Ricardo PereiraMedeiros, Marcelo C.Escolas::EESP2017-06-13T18:38:09Z2017-06-13T18:38:09Z2017TD 454http://hdl.handle.net/10438/18334We consider a new, flexible and easy-to-implement method to estimate causal effects of an intervention on a single treated unit and when a control group is not readily available. We propose a two-step methodology where in the first stage a counterfactual is estimated from a large-dimensional set of variables from a pool of untreated units using shrinkage methods, such as the Least Absolute Shrinkage Operator (LASSO). In the second stage, we estimate the average intervention effect on a vector of variables, which is consistent and asymptotically normal. Our results are valid uniformly over a wide class of probability laws. Furthermore, we show that these results still hold when the exact date of the intervention is unknown. Tests for multiple interventions and for contamination effects are also derived. By a simple transformation of the variables of interest, it is also possible to test for intervention effects on several moments (such as the mean or the variance) of the variables of interest. A Monte Carlo experiment evaluates the properties of the method in finite samples and compares it with other alternatives such as the differences-in-differences, factor and the synthetic control methods. In an application we evaluate the effects on inflation of an anti tax evasion program.engEESP - Textos para Discussão;TD 454Counterfactual analysisComparative studiesTreatment effectsSynthetic controlPolicy evaluationLASSOStructural breakFactor modelsEconomiaEconomia - Modelos matemáticosModelos econométricosArco: an artificial counterfactual approach for high-dimensional panel time-series datainfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlereponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas (FGV)instacron:FGVinfo:eu-repo/semantics/openAccessTEXTTD 454_CEQEF 36.pdf.txtTD 454_CEQEF 36.pdf.txtExtracted texttext/plain103631https://repositorio.fgv.br/bitstreams/f969bef1-3191-4117-ab91-09aa110c6e10/download511ea7ccb6c10bdc6b1f2668429ef94bMD55ORIGINALTD 454_CEQEF 36.pdfTD 454_CEQEF 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dc.title.eng.fl_str_mv |
Arco: an artificial counterfactual approach for high-dimensional panel time-series data |
title |
Arco: an artificial counterfactual approach for high-dimensional panel time-series data |
spellingShingle |
Arco: an artificial counterfactual approach for high-dimensional panel time-series data Carvalho, Carlos Viana de Counterfactual analysis Comparative studies Treatment effects Synthetic control Policy evaluation LASSO Structural break Factor models Economia Economia - Modelos matemáticos Modelos econométricos |
title_short |
Arco: an artificial counterfactual approach for high-dimensional panel time-series data |
title_full |
Arco: an artificial counterfactual approach for high-dimensional panel time-series data |
title_fullStr |
Arco: an artificial counterfactual approach for high-dimensional panel time-series data |
title_full_unstemmed |
Arco: an artificial counterfactual approach for high-dimensional panel time-series data |
title_sort |
Arco: an artificial counterfactual approach for high-dimensional panel time-series data |
author |
Carvalho, Carlos Viana de |
author_facet |
Carvalho, Carlos Viana de Masini, Ricardo Pereira Medeiros, Marcelo C. |
author_role |
author |
author2 |
Masini, Ricardo Pereira Medeiros, Marcelo C. |
author2_role |
author author |
dc.contributor.unidadefgv.por.fl_str_mv |
Escolas::EESP |
dc.contributor.author.fl_str_mv |
Carvalho, Carlos Viana de Masini, Ricardo Pereira Medeiros, Marcelo C. |
dc.subject.eng.fl_str_mv |
Counterfactual analysis Comparative studies Treatment effects Synthetic control Policy evaluation LASSO Structural break Factor models |
topic |
Counterfactual analysis Comparative studies Treatment effects Synthetic control Policy evaluation LASSO Structural break Factor models Economia Economia - Modelos matemáticos Modelos econométricos |
dc.subject.area.por.fl_str_mv |
Economia |
dc.subject.bibliodata.por.fl_str_mv |
Economia - Modelos matemáticos Modelos econométricos |
description |
We consider a new, flexible and easy-to-implement method to estimate causal effects of an intervention on a single treated unit and when a control group is not readily available. We propose a two-step methodology where in the first stage a counterfactual is estimated from a large-dimensional set of variables from a pool of untreated units using shrinkage methods, such as the Least Absolute Shrinkage Operator (LASSO). In the second stage, we estimate the average intervention effect on a vector of variables, which is consistent and asymptotically normal. Our results are valid uniformly over a wide class of probability laws. Furthermore, we show that these results still hold when the exact date of the intervention is unknown. Tests for multiple interventions and for contamination effects are also derived. By a simple transformation of the variables of interest, it is also possible to test for intervention effects on several moments (such as the mean or the variance) of the variables of interest. A Monte Carlo experiment evaluates the properties of the method in finite samples and compares it with other alternatives such as the differences-in-differences, factor and the synthetic control methods. In an application we evaluate the effects on inflation of an anti tax evasion program. |
publishDate |
2017 |
dc.date.accessioned.fl_str_mv |
2017-06-13T18:38:09Z |
dc.date.available.fl_str_mv |
2017-06-13T18:38:09Z |
dc.date.issued.fl_str_mv |
2017 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
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publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10438/18334 |
dc.identifier.sici.none.fl_str_mv |
TD 454 |
identifier_str_mv |
TD 454 |
url |
http://hdl.handle.net/10438/18334 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartofseries.por.fl_str_mv |
EESP - Textos para Discussão;TD 454 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional do FGV (FGV Repositório Digital) instname:Fundação Getulio Vargas (FGV) instacron:FGV |
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