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article · Journal of Data Science · 2021

Generalized Linear Distributed Lag Models

Hanh Nguyen, Qin Shao

Cited by 0Open access

Abstract

We propose distributed generalized linear models for the purpose of incorporating lagged effects. The model class provides a more accurate statistical measure of the relationship between the dependent variable and a series of covariates. The estimators from the proposed procedure are shown to be consistent. Simulation studies not only confirm the asymptotic properties of the estimators, but exhibit the adverse effects of model misspecification in terms of accuracy of model estimation and prediction. The application is illustrated by analyzing the presidential election data of 2016.

Cite this paper

APA 7

Nguyen, H., & Shao, Q. (2021). Generalized Linear Distributed Lag Models. Journal of Data Science, 17(4), 660–673. https://doi.org/10.6339/jds.201910_17(4).0002

MLA 9

Nguyen, Hanh, and Qin Shao. “Generalized Linear Distributed Lag Models.” Journal of Data Science, vol. 17, no. 4, 2021, pp. 660–73, https://doi.org/10.6339/jds.201910_17(4).0002.

Chicago

Nguyen, Hanh, and Qin Shao. 2021. “Generalized Linear Distributed Lag Models.” Journal of Data Science 17 (4): 660–73. https://doi.org/10.6339/jds.201910_17(4).0002.

Harvard

Nguyen, H. and Shao, Q. (2021) ‘Generalized Linear Distributed Lag Models’, Journal of Data Science, 17(4), pp. 660–673. Available at: https://doi.org/10.6339/jds.201910_17(4).0002.

BibTeX
@article{nguyen2021generalized,
  title = {{Generalized Linear Distributed Lag Models}},
  author = {Nguyen, Hanh and Shao, Qin},
  journal = {Journal of Data Science},
  volume = {17},
  number = {4},
  pages = {660--673},
  year = {2021},
  doi = {10.6339/jds.201910_17(4).0002}
}

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