Arbeitspapier

Simultaneous Inference of the Partially Linear Model with a Multivariate Unknown Function

In this paper, we conduct simultaneous inference of the non-parametric part of a partially linear model when the non-parametric component is a multivariate unknown function. Based on semi-parametric estimates of the model, we construct a simultaneous confidence region of the multivariate function for simultaneous inference. The developed methodology is applied to perform simultaneous inference for the U.S. gasoline demand where the income and price variables are contaminated by Berkson errors. The empirical results strongly suggest that the linearity of the U.S. gasoline demand is rejected. The results are also used to propose an alternative form for the demand.

Sprache
Englisch

Erschienen in
Series: IRTG 1792 Discussion Paper ; No. 2020-008

Klassifikation
Wirtschaft
Hypothesis Testing: General
Estimation: General
Semiparametric and Nonparametric Methods: General
Thema
Simultaneous inference
Multivariate function
Simultaneous confidence region
Berkson error
Regression calibration

Ereignis
Geistige Schöpfung
(wer)
Kim, Kun Ho
Chao, Shih-Kang
Härdle, Wolfgang Karl
Ereignis
Veröffentlichung
(wer)
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
(wo)
Berlin
(wann)
2020

Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Kim, Kun Ho
  • Chao, Shih-Kang
  • Härdle, Wolfgang Karl
  • Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

Entstanden

  • 2020

Ähnliche Objekte (12)