skip to content
Covid-19 virus
COVID-19 Resources

Reliable information about the coronavirus (COVID-19) is available from the World Health Organization (current situation, international travel). Numerous and frequently-updated resource results are available from this WorldCat.org search. OCLC’s WebJunction has pulled together information and resources to assist library staff as they consider how to handle coronavirus issues in their communities.

Image provided by: CDC/ Alissa Eckert, MS; Dan Higgins, MAM
Errors in the dependent variable of quantile regression models Preview this item
ClosePreview this item
Checking...

Errors in the dependent variable of quantile regression models

Author: Jerry A Hausman; Haoyang Liu; Ye Luo; Christopher J Palmer; National Bureau of Economic Research,
Publisher: Cambridge, Mass. : National Bureau of Economic Research, 2019.
Series: Working paper series (National Bureau of Economic Research), no. 25819.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
The popular quantile regression estimator of Koenker and Bassett (1978) is biased if there is an additive error term. Approaching this problem as an errors-in-variables problem where the dependent variable suffers from classical measurement error, we present a sieve maximum-likelihood approach that is robust to left-hand side measurement error. After providing sufficient conditions for identification, we demonstrate  Read more...
Rating:

(not yet rated) 0 with reviews - Be the first.

Subjects
More like this

Find a copy online

Links to this item

Find a copy in the library

&AllPage.SpinnerRetrieving; Finding libraries that hold this item...

Details

Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Jerry A Hausman; Haoyang Liu; Ye Luo; Christopher J Palmer; National Bureau of Economic Research,
OCLC Number: 1101195672
Notes: "May 2019"
Description: 1 online resource (67 pages) : illustrations.
Series Title: Working paper series (National Bureau of Economic Research), no. 25819.
Responsibility: Jerry A. Hausman, Haoyang Liu, Ye Luo, Christopher Palmer.

Abstract:

The popular quantile regression estimator of Koenker and Bassett (1978) is biased if there is an additive error term. Approaching this problem as an errors-in-variables problem where the dependent variable suffers from classical measurement error, we present a sieve maximum-likelihood approach that is robust to left-hand side measurement error. After providing sufficient conditions for identification, we demonstrate that when the number of knots in the quantile grid is chosen to grow at an adequate speed, the sieve maximum-likelihood estimator is consistent and asymptotically normal, permitting inference via bootstrapping. We verify our theoretical results with Monte Carlo simulations and illustrate our estimator with an application to the returns to education highlighting changes over time in the returns to education that have previously been masked by measurement-error bias.

Reviews

User-contributed reviews
Retrieving GoodReads reviews...
Retrieving DOGObooks reviews...

Tags

Be the first.
Confirm this request

You may have already requested this item. Please select Ok if you would like to proceed with this request anyway.

Linked Data


\n\n

Primary Entity<\/h3>\n
<http:\/\/www.worldcat.org\/oclc\/1101195672<\/a>> # Errors in the dependent variable of quantile regression models<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:CreativeWork<\/a>, schema:MediaObject<\/a>, schema:Book<\/a> ;\u00A0\u00A0\u00A0\nlibrary:oclcnum<\/a> \"1101195672<\/span>\" ;\u00A0\u00A0\u00A0\nlibrary:placeOfPublication<\/a> <http:\/\/id.loc.gov\/vocabulary\/countries\/mau<\/a>> ;\u00A0\u00A0\u00A0\nschema:about<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/9124506451#Topic\/economics_statistical_methods<\/a>> ; # Economics--Statistical methods<\/span>\n\u00A0\u00A0\u00A0\nschema:about<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/9124506451#Topic\/error_analysis_mathematics<\/a>> ; # Error analysis (Mathematics)<\/span>\n\u00A0\u00A0\u00A0\nschema:about<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/9124506451#Topic\/instrumental_variables_statistics<\/a>> ; # Instrumental variables (Statistics)<\/span>\n\u00A0\u00A0\u00A0\nschema:about<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/9124506451#Topic\/regression_analysis<\/a>> ; # Regression analysis<\/span>\n\u00A0\u00A0\u00A0\nschema:author<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/9124506451#Person\/luo_ye<\/a>> ; # Ye Luo<\/span>\n\u00A0\u00A0\u00A0\nschema:author<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/9124506451#Person\/palmer_christopher_j<\/a>> ; # Christopher J. Palmer<\/span>\n\u00A0\u00A0\u00A0\nschema:author<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/9124506451#Person\/liu_haoyang<\/a>> ; # Haoyang Liu<\/span>\n\u00A0\u00A0\u00A0\nschema:author<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/9124506451#Person\/hausman_jerry_a<\/a>> ; # Jerry A. Hausman<\/span>\n\u00A0\u00A0\u00A0\nschema:bookFormat<\/a> schema:EBook<\/a> ;\u00A0\u00A0\u00A0\nschema:datePublished<\/a> \"2019<\/span>\" ;\u00A0\u00A0\u00A0\nschema:description<\/a> \"The popular quantile regression estimator of Koenker and Bassett (1978) is biased if there is an additive error term. Approaching this problem as an errors-in-variables problem where the dependent variable suffers from classical measurement error, we present a sieve maximum-likelihood approach that is robust to left-hand side measurement error. After providing sufficient conditions for identification, we demonstrate that when the number of knots in the quantile grid is chosen to grow at an adequate speed, the sieve maximum-likelihood estimator is consistent and asymptotically normal, permitting inference via bootstrapping. We verify our theoretical results with Monte Carlo simulations and illustrate our estimator with an application to the returns to education highlighting changes over time in the returns to education that have previously been masked by measurement-error bias.<\/span>\"@en<\/a> ;\u00A0\u00A0\u00A0\nschema:exampleOfWork<\/a> <http:\/\/worldcat.org\/entity\/work\/id\/9124506451<\/a>> ;\u00A0\u00A0\u00A0\nschema:inLanguage<\/a> \"en<\/span>\" ;\u00A0\u00A0\u00A0\nschema:isPartOf<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/9124506451#Series\/working_paper_series_national_bureau_of_economic_research<\/a>> ; # Working paper series (National Bureau of Economic Research) ;<\/span>\n\u00A0\u00A0\u00A0\nschema:isPartOf<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/9124506451#Series\/nber_working_paper_series<\/a>> ; # NBER working paper series ;<\/span>\n\u00A0\u00A0\u00A0\nschema:name<\/a> \"Errors in the dependent variable of quantile regression models<\/span>\"@en<\/a> ;\u00A0\u00A0\u00A0\nschema:productID<\/a> \"1101195672<\/span>\" ;\u00A0\u00A0\u00A0\nschema:publisher<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/9124506451#Organization\/national_bureau_of_economic_research<\/a>> ; # National Bureau of Economic Research,<\/span>\n\u00A0\u00A0\u00A0\nschema:url<\/a> <http:\/\/www.nber.org\/papers\/w25819<\/a>> ;\u00A0\u00A0\u00A0\nschema:url<\/a> <http:\/\/dx.doi.org\/10.3386\/w25819<\/a>> ;\u00A0\u00A0\u00A0\nschema:url<\/a> <https:\/\/prod.nber.org\/papers\/w25819<\/a>> ;\u00A0\u00A0\u00A0\nwdrs:describedby<\/a> <http:\/\/www.worldcat.org\/title\/-\/oclc\/1101195672<\/a>> ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n\n

Related Entities<\/h3>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/9124506451#Organization\/national_bureau_of_economic_research<\/a>> # National Bureau of Economic Research,<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Organization<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"National Bureau of Economic Research,<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/9124506451#Person\/hausman_jerry_a<\/a>> # Jerry A. Hausman<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Person<\/a> ;\u00A0\u00A0\u00A0\nschema:familyName<\/a> \"Hausman<\/span>\" ;\u00A0\u00A0\u00A0\nschema:givenName<\/a> \"Jerry A.<\/span>\" ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Jerry A. Hausman<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/9124506451#Person\/liu_haoyang<\/a>> # Haoyang Liu<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Person<\/a> ;\u00A0\u00A0\u00A0\nschema:familyName<\/a> \"Liu<\/span>\" ;\u00A0\u00A0\u00A0\nschema:givenName<\/a> \"Haoyang<\/span>\" ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Haoyang Liu<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/9124506451#Person\/luo_ye<\/a>> # Ye Luo<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Person<\/a> ;\u00A0\u00A0\u00A0\nschema:familyName<\/a> \"Luo<\/span>\" ;\u00A0\u00A0\u00A0\nschema:givenName<\/a> \"Ye<\/span>\" ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Ye Luo<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/9124506451#Person\/palmer_christopher_j<\/a>> # Christopher J. Palmer<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Person<\/a> ;\u00A0\u00A0\u00A0\nschema:familyName<\/a> \"Palmer<\/span>\" ;\u00A0\u00A0\u00A0\nschema:givenName<\/a> \"Christopher J.<\/span>\" ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Christopher J. Palmer<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/9124506451#Series\/nber_working_paper_series<\/a>> # NBER working paper series ;<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nbgn:PublicationSeries<\/a> ;\u00A0\u00A0\u00A0\nschema:hasPart<\/a> <http:\/\/www.worldcat.org\/oclc\/1101195672<\/a>> ; # Errors in the dependent variable of quantile regression models<\/span>\n\u00A0\u00A0\u00A0\nschema:name<\/a> \"NBER working paper series ;<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/9124506451#Series\/working_paper_series_national_bureau_of_economic_research<\/a>> # Working paper series (National Bureau of Economic Research) ;<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nbgn:PublicationSeries<\/a> ;\u00A0\u00A0\u00A0\nschema:hasPart<\/a> <http:\/\/www.worldcat.org\/oclc\/1101195672<\/a>> ; # Errors in the dependent variable of quantile regression models<\/span>\n\u00A0\u00A0\u00A0\nschema:name<\/a> \"Working paper series (National Bureau of Economic Research) ;<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/9124506451#Topic\/economics_statistical_methods<\/a>> # Economics--Statistical methods<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Intangible<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Economics--Statistical methods<\/span>\"@en<\/a> ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/9124506451#Topic\/error_analysis_mathematics<\/a>> # Error analysis (Mathematics)<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Intangible<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Error analysis (Mathematics)<\/span>\"@en<\/a> ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/9124506451#Topic\/instrumental_variables_statistics<\/a>> # Instrumental variables (Statistics)<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Intangible<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Instrumental variables (Statistics)<\/span>\"@en<\/a> ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/9124506451#Topic\/regression_analysis<\/a>> # Regression analysis<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Intangible<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Regression analysis<\/span>\"@en<\/a> ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/id.loc.gov\/vocabulary\/countries\/mau<\/a>>\u00A0\u00A0\u00A0\u00A0a \nschema:Place<\/a> ;\u00A0\u00A0\u00A0\ndcterms:identifier<\/a> \"mau<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n