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<math display="block">Y(t) = \alpha_0(t) + \sum_{j=1}^p\alpha_j(t)X_j(t)+\varepsilon(t),\ \text{for}\ t\in\mathcal{T},</math>
where <math>X_1,\ldots,X_p</math> are multiple functional covariates with ___domain <math>\mathcal{T}</math> and <math>\alpha_0,\alpha_1,\ldots,\alpha_p</math> are the coefficient functions with the same ___domain.<ref name=wang:16/>
=== Functional linear IV regression ===
The functional regression model is especially suitable for handling the mixed-frequency economic and financial data.<ref name=":0">{{Cite journal |last=Babii |first=Andrii |date=2022-10-02 |title=High-Dimensional Mixed-Frequency IV Regression |url=https://www.tandfonline.com/doi/full/10.1080/07350015.2021.1933501 |journal=Journal of Business & Economic Statistics |language=en |volume=40 |issue=4 |pages=1470–1483 |doi=10.1080/07350015.2021.1933501 |issn=0735-0015}}</ref> Regression modelling in economics usually involves the endogeneity problem which is formally described as non-zero correlation between the regressor and the error term. The [[Instrumental variables estimation|instrumental variable estimation]] is one of the leading methods to solve this issue.<ref name=":0" /><ref>{{Cite journal |last=Florens |first=Jean-Pierre |last2=Van Bellegem |first2=Sébastien |date=2015-06-01 |title=Instrumental variable estimation in functional linear models |url=https://www.sciencedirect.com/science/article/pii/S0304407615000445 |journal=Journal of Econometrics |series=High Dimensional Problems in Econometrics |volume=186 |issue=2 |pages=465–476 |doi=10.1016/j.jeconom.2015.02.020 |issn=0304-4076}}</ref>
== Functional nonlinear models ==
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