Andrii Babii, Jean-Pierre Florens
arXiv 3 May 2017 · Mathematics — Statistics Theory
arXiv:1705.01654 · PDF · Extracted main text
It is common to assume in empirical research that observables and unobservables are additively separable, especially, when the former are endogenous. This is done because it is widely recognized that identification and estimation challenges arise when interactions between the two are allowed for. Starting from a nonseparable IV model, where the instrumental variable is independent of unobservables, we develop a novel nonparametric test of separability of unobservables. The large-sample distribution of the test statistics is nonstandard and relies on a novel Donsker-type central limit theorem for the empirical distribution of nonparametric IV residuals, which may be of independent interest. Using a dataset drawn from the 2015 US Consumer Expenditure Survey, we find that the test rejects the separability in Engel curves for most of the commodities.
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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 | van der Vaart and Wellner (1996) Weak convergence and empirical processes: with applications to statistics | 1.000 | 6 | 4 | 100% |
| 2 | Engl, Hanke, and Neubauer (2000) Regularization of inverse problems | 1.000 | 6 | 3 | 100% |
| 3 | Blundell, Chen, and Kristensen (2007) Semi-nonparametric IV estimation of shape-invariant Engel curves | 0.928 | 4 | 3 | 100% |
| 4 | Darolles, Fan, Florens, and Renault (2011) Nonparametric instrumental regression | 0.928 | 4 | 3 | 100% |
| 5 | Gagliardini and Scaillet (2012) Tikhonov regularization for nonparametric instrumental variable estimators | 0.928 | 4 | 3 | 100% |
| 6 | Carrasco, Florens, and Renault (2014) Asymptotic Normal Inference in Linear Inverse Problems | 0.843 | 3 | 3 | 100% |
| 7 | Nickl and Pötscher (2007) Bracketing metric entropy rates and empirical central limit theorems for function classes of Besov- and Sobolev-type | 0.843 | 3 | 3 | 100% |
| 8 | Horowitz and Lee (2007) Nonparametric instrumental variables estimation of a quantile regression model | 0.737 | 3 | 2 | 100% |
| 9 | Newey and Powell (2003) Instrumental variable estimation of nonparametric models | 0.737 | 3 | 2 | 100% |
| 10 | Babii and Florens (2020) Is completeness necessary? Estimation in nonidentified linear models | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 62 scored citations.