arXiv 17 Apr 2024 · Econometrics · 1 citations (OpenAlex)
arXiv:2404.11324 · PDF · DOI · OpenAlex · Extracted main text
Model averaging methods have become an increasingly popular tool for improving predictions and dealing with model uncertainty, especially in Bayesian settings. Recently, frequentist model averaging methods such as information theoretic and least squares model averaging have emerged. This work focuses on the issue of covariate uncertainty where managing the computational resources is key: The model space grows exponentially with the number of covariates such that averaged models must often be approximated. Weighted-average least squares (WALS), first introduced for (generalized) linear models in the econometric literature, combines Bayesian and frequentist aspects and additionally employs a semiorthogonal transformation of the regressors to reduce the computational burden. This paper extends WALS for generalized linear models to the negative binomial (NB) regression model for overdispersed count data. A simulation experiment and an empirical application using data on doctor visits were conducted to compare the predictive power of WALS for NB regression to traditional estimators. The results show that WALS for NB improves on the maximum likelihood estimator in sparse situations and is competitive with lasso while being computationally more efficient.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Magnus, J.R., De Luca, G (2016) Weighted-average least squares (WALS): A survey | 0.950 | 7 | 4 | 86% |
| 2 | De Luca, G., Magnus, J.R., Peracchi, F (2018) Weighted-average least squares estimation of generalized linear models | 0.922 | 23 | 7 | 78% |
| 3 | Magnus, J.R., Powell, O., Prüfer, P (2010) A comparison of two model averaging techniques with an application to growth empirics | 0.843 | 3 | 3 | 100% |
| 4 | Wang, Z., Ma, S., Zappitelli, M., Parikh, C., Wang, C.Y., Devarajan, P (2016) Penalized count data regression with application to hospital stay after pediatric cardiac surgery | 0.737 | 4 | 3 | 50% |
| 5 | Cameron, A.C., Trivedi, P.K (1986) Econometric models based on count data. Comparisons and applications of some estimators and tests | 0.737 | 3 | 2 | 100% |
| 6 | De Luca, G., Magnus, J.R., Peracchi, F (2023) Weighted-average least squares (WALS): Confidence and prediction intervals | 0.737 | 3 | 2 | 100% |
| 7 | Gneiting, T., Raftery, A.E (2007) Strictly proper scoring rules, prediction, and estimation | 0.737 | 3 | 2 | 100% |
| 8 | Czado, C., Gneiting, T., Held, L (2009) Predictive model assessment for count data | 0.693 | 6 | 1 | 100% |
| 9 | De Luca, G., Magnus, J.R., Peracchi, F (2022) Sampling properties of the Bayesian posterior mean with an application to WALS estimation | 0.644 | 2 | 2 | 100% |
| 10 | Hothorn, T., Leisch, F., Zeileis, A., Hornik, K (2005) The design and analysis of benchmark experiments | 0.644 | 2 | 2 | 100% |
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