arXiv 9 Apr 2018 · Econometrics · publishedJournal of Econometrics (2020)
arXiv:1804.03110 · PDF · DOI · OpenAlex · Extracted main text
This paper provides a new methodology to analyze unobserved heterogeneity when observed characteristics are modeled nonlinearly. The proposed model builds on varying random coefficients (VRC) that are determined by nonlinear functions of observed regressors and additively separable unobservables. This paper proposes a novel estimator of the VRC density based on weighted sieve minimum distance. The main example of sieve bases are Hermite functions which yield a numerically stable estimation procedure. This paper shows inference results that go beyond what has been shown in ordinary RC models. We provide in each case rates of convergence and also establish pointwise limit theory of linear functionals, where a prominent example is the density of potential outcomes. In addition, a multiplier bootstrap procedure is proposed to construct uniform confidence bands. A Monte Carlo study examines finite sample properties of the estimator and shows that it performs well even when the regressors associated to RC are far from being heavy tailed. Finally, the methodology is applied to analyze heterogeneity in income elasticity of demand for housing.
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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 | S. Hoderlein, J. Klemelä, and E. Mammen (2010) Analyzing the random coefficient model nonparametrically | 0.928 | 4 | 3 | 100% |
| 2 | A. Lewbel and K. Pendakur (2017) Unobserved preference heterogeneity in demand using generalized random coefficients | 0.928 | 4 | 3 | 100% |
| 3 | J. Fan, Q. Yao, and Z. Cai (2003) Adaptive varying-coefficient linear models | 0.843 | 3 | 3 | 100% |
| 4 | A. Belloni, V. Chernozhukov, D. Chetverikov, and K. Kato (2015) Some new asymptotic theory for least squares series: Pointwise and uniform results | 0.737 | 4 | 2 | 75% |
| 5 | M. A. Masten (2018) Random coefficients on endogenous variables in simultaneous equations models | 0.737 | 3 | 2 | 100% |
| 6 | X. Chen and T. M. Christensen (2018) Optimal sup-norm rates and uniform inference on nonlinear functionals of nonparametric iv regression | 0.721 | 8 | 3 | 38% |
| 7 | X. Chen (2007) Large sample sieve estimation of semi-nonparametric models | 0.644 | 3 | 2 | 67% |
| 8 | R. Beran, A. Feuerverger, and P. Hall (1996) On nonparametric estimation of intercept and slope distributions in random coefficient regression | 0.644 | 2 | 2 | 100% |
| 9 | C. Breunig and S. Hoderlein (2018) Specification testing in random coefficient models | 0.644 | 2 | 2 | 100% |
| 10 | F. Dunker, K. Eckle, K. Proksch, and J. Schmidt-Hieber (2019) Tests for qualitative features in the random coefficients model | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 40 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | =0pt =0pt plus .5=0pt plus .5=.3Identification and Semiparametric Estimation of Conditional Means from Aggregate Data | 0.405 | 1 | 1 |
| 2 | Correlated Random Coefficient Distributions in Linear Panel Models | 0.405 | 1 | 1 |