Artikel
Quantile regression with generated regressors
This paper studies estimation and inference for linear quantile regression models with generated regressors. We suggest a practical two-step estimation procedure, where the generated regressors are computed in the first step. The asymptotic properties of the two-step estimator, namely, consistency and asymptotic normality are established. We show that the asymptotic variance-covariance matrix needs to be adjusted to account for the first-step estimation error. We propose a general estimator for the asymptotic variance-covariance, establish its consistency, and develop testing procedures for linear hypotheses in these models. Monte Carlo simulations to evaluate the finite-sample performance of the estimation and inference procedures are provided. Finally, we apply the proposed methods to study Engel curves for various commodities using data from the UK Family Expenditure Survey. We document strong heterogeneity in the estimated Engel curves along the conditional distribution of the budget share of each commodity. The empirical application also emphasizes that correctly estimating confidence intervals for the estimated Engel curves by the proposed estimator is of importance for inference.
- Sprache
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Englisch
- Erschienen in
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Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 9 ; Year: 2021 ; Issue: 2 ; Pages: 1-35 ; Basel: MDPI
- Klassifikation
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Wirtschaft
Hypothesis Testing: General
Estimation: General
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
- Thema
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engel curves
generated regressor
heterogeneity
quantile regression
- Ereignis
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Geistige Schöpfung
- (wer)
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Chen, Liqiong
Galvão Júnior, Antônio Fialho
Song, Suyong
- Ereignis
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Veröffentlichung
- (wer)
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MDPI
- (wo)
-
Basel
- (wann)
-
2021
- DOI
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doi:10.3390/econometrics9020016
- Handle
- Letzte Aktualisierung
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10.03.2025, 11:42 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Chen, Liqiong
- Galvão Júnior, Antônio Fialho
- Song, Suyong
- MDPI
Entstanden
- 2021
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