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FUNCTIONAL INSTRUMENTAL VARIABLE REGRESSION WITH AN APPLICATION TO ESTIMATING THE IMPACT OF IMMIGRATION ON NATIVE WAGES

Published online by Cambridge University Press:  07 November 2024

Dakyung Seong Open the ORCID record for Dakyung Seong [Opens in a new window]  and
Won-Ki Seo Open the ORCID record for Won-Ki Seo [Opens in a new window]
Dakyung Seong*
Affiliation:
University of Sydney
Won-Ki Seo
Affiliation:
University of Sydney
*
Address correspondence to Dakyung Seong, School of Economics, University of Sydney, Camperdown, NSW, Australia, e-mail: [email protected].
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Abstract

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Functional linear regression has gained popularity as a statistical tool for studying the relationship between function-valued variables. However, in practice, it is hard to expect that the explanatory variables of interest are strictly exogenous, due to, for example, the presence of omitted variables and measurement error. This issue of endogeneity remains insufficiently explored, in spite of its empirical importance. To fill this gap, this article proposes new consistent FPCA-based instrumental variable estimators and develops their asymptotic properties in detail. Simulation experiments under a wide range of settings show that the proposed estimators perform considerably well. We apply our methodology to estimate the impact of immigration on native labor market outcomes in the US.

Type
ARTICLES
Information
Econometric Theory , First View , pp. 1 - 36
Copyright
© The Author(s), 2024. Published by Cambridge University Press

Footnotes

The authors express deep appreciation to the editor, the co-editor, and the three anonymous referees for their invaluable and insightful suggestions. We are also thankful to Morten Ø. Nielsen and seminar participants at the University of Sydney, University of Queensland, and SETA 2022 for their helpful comments. Data and R code to replicate the empirical results in Table 3 are available on the authors’ websites.

References

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