{"title":"Partly linear instrumental variables regressions without smoothing on the instruments","authors":"Jean-Pierre Florens, Elia Lapenta","doi":"10.1007/s11749-024-00931-z","DOIUrl":null,"url":null,"abstract":"<p>We consider a semiparametric partly linear model identified by instrumental variables. We propose an estimation method that does not smooth on the instruments and we extend the Landweber–Fridman regularization scheme to the estimation of this semiparametric model. We then show the asymptotic normality of the parametric estimator and obtain the convergence rate for the nonparametric estimator. Our estimator that does not smooth on the instruments coincides with a typical estimator that does smooth on the instruments but keeps the respective bandwidth fixed as the sample size increases. We propose a data driven method for the selection of the regularization parameter, and in a simulation study we show the attractive performance of our estimators.</p>","PeriodicalId":51189,"journal":{"name":"Test","volume":"64 1","pages":""},"PeriodicalIF":1.2000,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Test","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s11749-024-00931-z","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0
Abstract
We consider a semiparametric partly linear model identified by instrumental variables. We propose an estimation method that does not smooth on the instruments and we extend the Landweber–Fridman regularization scheme to the estimation of this semiparametric model. We then show the asymptotic normality of the parametric estimator and obtain the convergence rate for the nonparametric estimator. Our estimator that does not smooth on the instruments coincides with a typical estimator that does smooth on the instruments but keeps the respective bandwidth fixed as the sample size increases. We propose a data driven method for the selection of the regularization parameter, and in a simulation study we show the attractive performance of our estimators.
期刊介绍:
TEST is an international journal of Statistics and Probability, sponsored by the Spanish Society of Statistics and Operations Research. English is the official language of the journal.
The emphasis of TEST is placed on papers containing original theoretical contributions of direct or potential value in applications. In this respect, the methodological contents are considered to be crucial for the papers published in TEST, but the practical implications of the methodological aspects are also relevant. Original sound manuscripts on either well-established or emerging areas in the scope of the journal are welcome.
One volume is published annually in four issues. In addition to the regular contributions, each issue of TEST contains an invited paper from a world-wide recognized outstanding statistician on an up-to-date challenging topic, including discussions.