arXiv 8 Dec 2024 · Econometrics
arXiv:2412.05794 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a Bayesian factor-augmented bundle choice model to estimate joint consumption as well as the substitutability and complementarity of multiple goods in the presence of endogenous regressors. The model extends the two primary treatments of endogeneity in existing bundle choice models: (1) endogenous market-level prices and (2) time-invariant unobserved individual heterogeneity. A Bayesian sparse factor approach is employed to capture high-dimensional error correlations that induce taste correlation and endogeneity. Time-varying factor loadings allow for more general individual-level and time-varying heterogeneity and endogeneity, while the sparsity induced by the shrinkage prior on loadings balances flexibility with parsimony. Applied to a soda tax in the context of complementarities, the new approach captures broader effects of the tax that were previously overlooked. Results suggest that a soda tax could yield additional health benefits by marginally decreasing the consumption of salty snacks along with sugary drinks, extending the health benefits beyond the reduction in sugar consumption alone.
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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 | Jacobi, L., Wagner, H., and Frühwirth-Schnatter, S (2016) Bayesian treatment effects models with variable selection for panel outcomes with an application to earnings effects of maternit… | 0.928 | 5 | 4 | 80% |
| 2 | Gentzkow, M (2007) Valuing new goods in a model with complementarity: Online newspapers | 0.874 | 11 | 2 | 100% |
| 3 | Iaria, A. and Wang, A (2020) Identification and estimation of demand for bundles | 0.874 | 5 | 2 | 100% |
| 4 | Wagner, H., Frühwirth-Schnatter, S., and Jacobi, L (2023) Factor-augmented bayesian treatment effects models for panel outcomes | 0.843 | 4 | 4 | 75% |
| 5 | Jacobi, L., Sovinsky, M., and Sun, T (2024) Substance complementarities under access restrictions: analysis of (il)licit multi-substance use with endogeneous choice sets self | 0.811 | 4 | 2 | 100% |
| 6 | Rossi, P. E., Allenby, G. M., and McCulloch, R (2012) Bayesian Statistics and Marketing | 0.811 | 4 | 2 | 100% |
| 7 | Deza, M (2015) Is there a stepping stone effect in drug use? separating state dependence from unobserved heterogeneity within and between illic… | 0.737 | 3 | 2 | 100% |
| 8 | Loaiza-Maya, R. and Nibbering, D (2022) Scalable bayesian estimation in the multinomial probit model | 0.737 | 3 | 2 | 100% |
| 9 | Sovinsky, M., Jacobi, L., Allocca, A., and Sun, T (2024) More than joints: Multi-substance use, choice limitations, and policy implications self | 0.737 | 3 | 2 | 100% |
| 10 | Ershov, D., Orr, S., Laliberte, J.-W., and Marcoux, M (2024) Estimating complementarity with large choice sets: An application to mergers | 0.693 | 5 | 1 | 100% |
Showing the top 10 of 52 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Semiparametric Discrete Choice Models for Bundles | 0.644 | 2 | 2 |