Liebl, Dominik and Rameseder, Stefan and Rust, Christoph (2020) Improving Estimation in Functional Linear Regression With Points of Impact: Insights Into Google AdWords. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 29 (4). pp. 814-826. ISSN 1061-8600, 1537-2715
Full text not available from this repository. (Request a copy)Abstract
The functional linear regression model with points of impact (PoI) is a recent augmentation of the classical functional linear model with many practically important applications. In this article, however, we demonstrate that the existing data-driven procedure for estimating the parameters of this regression model can be very instable and inaccurate. The tendency to omit relevant PoI is a particularly problematic aspect resulting in omitted-variable biases. We explain the theoretical reason for this problem and propose a new sequential estimation algorithm that leads to significantly improved estimation results. Our estimation algorithm is compared with the existing estimation procedure using an in-depth simulation study. The applicability is demonstrated using data from Google AdWords, today's most important platform for online advertisements. The R-package FunRegPoI and additional R-codes are provided in the online .
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | SMOOTHING SPLINES ESTIMATORS; VARIABLE SELECTION; DYNAMICS; CLASSIFICATION; Functional data analysis; Functional linear regression; Online advertising; Points of impact |
| Subjects: | 300 Social sciences > 330 Economics |
| Divisions: | Business, Economics and Information Systems > Institut für Volkswirtschaftslehre und Ökonometrie Business, Economics and Information Systems > Institut für Volkswirtschaftslehre und Ökonometrie > Lehrstuhl für Ökonometrie (Prof. Dr. Rolf Tschernig) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 23 Mar 2021 12:07 |
| Last Modified: | 23 Mar 2021 12:07 |
| URI: | https://pred.uni-regensburg.de/id/eprint/44548 |
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