arXiv 31 Oct 2023 · Econometrics
arXiv:2311.00013 · PDF · DOI · OpenAlex · Extracted main text
We propose two approaches to estimate semiparametric discrete choice models for bundles. Our first approach is a kernel-weighted rank estimator based on a matching-based identification strategy. We establish its complete asymptotic properties and prove the validity of the nonparametric bootstrap for inference. We then introduce a new multi-index least absolute deviations (LAD) estimator as an alternative, of which the main advantage is its capacity to estimate preference parameters on both alternative- and agent-specific regressors. Both methods can account for arbitrary correlation in disturbances across choices, with the former also allowing for interpersonal heteroskedasticity. We also demonstrate that the identification strategy underlying these procedures can be extended naturally to panel data settings, producing an analogous localized maximum score estimator and a LAD estimator for estimating bundle choice models with fixed effects. We derive the limiting distribution of the former and verify the validity of the numerical bootstrap as an inference tool. All our proposed methods can be applied to general multi-index models. Monte Carlo experiments show that they perform well in finite samples.
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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 | Seo, M. H. and T. Otsu (2018) Local M-estimation with discontinuous criterion for dependent and limited observations | 1.000 | 22 | 4 | 100% |
| 2 | Kim, J. and D. Pollard (1990) Cube root asymptotics | 1.000 | 13 | 5 | 100% |
| 3 | Sherman, R. P (1994) b): U-processes in the analysis of a generalized semiparametric regression estimator | 1.000 | 9 | 3 | 100% |
| 4 | Hong, H. and J. Li (2020) The numerical bootstrap | 1.000 | 6 | 3 | 100% |
| 5 | Sherman, R. P (1994) a): Maximal inequalities for degenerate U-processes with applications to optimization estimators | 1.000 | 5 | 3 | 100% |
| 6 | Sherman, R. P (1993) The limiting distribution of the maximum rank correlation estimator | 0.874 | 7 | 2 | 100% |
| 7 | Fox, J. T. and N. Lazzati (2017) A note on identification of discrete choice models for bundles and binary games | 0.874 | 6 | 2 | 100% |
| 8 | Han, A. K (1987) Nonparametric analysis of a generalized regression model; the maximum rank correlation estimator | 0.874 | 5 | 2 | 100% |
| 9 | Shi, X., M. Shum, and W. Song (2018) Estimating semiparametric panel multinomial choice models using cyclic monotonicity | 0.843 | 3 | 3 | 100% |
| 10 | Newey, W. K. and D. McFadden (1994) Large sample estimation and hypothesis testing | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 69 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Bundle Choice Model with Endogenous Regressors: An Application to Soda Tax | 0.405 | 1 | 1 |