We propose a framework for estimation of the conditional mean function in a parametric model with function space covariates. The approach employs a functional mean squared error objective criterion. Under regularity conditions, consistency and asymptotic normality are established. The analysis extends to situations where the asymptotic properties are influenced by estimation errors arising from the presence of nuisance parameters. Wald, Lagrange multiplier, and quasi-likelihood ratio statistics are studied. An empirical application conducts lifetime income path comparisons across different demographic groups according to years of work experience.
|Number of pages||66|
|Journal||International Economic Review|
|Publication status||Published - 2022 Feb|
Bibliographical noteFunding Information:
We gratefully acknowledge the acting Editor, Jesus Fernandez‐Villaverde, and three anonymous referees for providing very helpful comments on the original version of the article. We also acknowledge helpful discussions with Kees Jan van Garderen, Kevin Sheppard, Richard Smith, Liangjun Su, Ying Wang, and participants of ANZESG (Wellington, 2019) and SETA (Osaka, 2019). Cho acknowledges research support from an Isaac Manasseh Meyer Fellowship of the National University of Singapore and kind hospitality of the Department of Economics at the Chinese University of Hong Kong during his visit in 2020; Phillips acknowledges research support from a Kelly Fellowship at the University of Auckland and the NSF under Grant No. SES 18‐50860; and Seo acknowledges research support from AcRF Tier 1.
© (2021) by the Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association
All Science Journal Classification (ASJC) codes
- Economics and Econometrics