arXiv 21 Apr 2024 · Econometrics
arXiv:2404.13735 · PDF · DOI · OpenAlex · Extracted main text
In this paper, I study the nonparametric identification and estimation of the marginal effect of an endogenous variable $X$ on the outcome variable $Y$, given a potentially mismeasured instrument variable $W^*$, without assuming linearity or separability of the functions governing the relationship between observables and unobservables. To address the challenges arising from the co-existence of measurement error and nonseparability, I first employ the deconvolution technique from the measurement error literature to identify the joint distribution of $Y, X, W^*$ using two error-laden measurements of $W^*$. I then recover the structural derivative of the function of interest and the "Local Average Response" (LAR) from the joint distribution via the "unobserved instrument" approach in Matzkin (2016). I also propose nonparametric estimators for these parameters and derive their uniform rates of convergence. Monte Carlo exercises show evidence that the estimators I propose have good finite sample performance.
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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 | Schennach, White and Chalak (2012) Local indirect least squares and average marginal effects in nonseparable structural systems | 0.874 | 5 | 2 | 100% |
| 2 | Schennach (2004) Nonparametric regression in the presence of measurement error | 0.855 | 8 | 5 | 62% |
| 3 | Schennach (2004) Estimation of nonlinear models with measurement error | 0.843 | 4 | 4 | 75% |
| 4 | Matzkin (2016) On independence conditions in nonseparable models: Observable and unobservable instruments | 0.843 | 3 | 3 | 100% |
| 5 | Song, Schennach and White (2015) Estimating nonseparable models with mismeasured endogenous variables | 0.737 | 3 | 2 | 100% |
| 6 | Imbens and Newey (2009) Identification and estimation of triangular simultaneous equations models without additivity | 0.693 | 7 | 1 | 100% |
| 7 | Andrews (1995) Nonparametric kernel estimation for semiparametric models | 0.644 | 4 | 1 | 100% |
| 8 | Altonji and Matzkin (2005) Cross section and panel data estimators for nonseparable models with endogenous regressors | 0.644 | 2 | 2 | 100% |
| 9 | Chesher (2003) Identification in nonseparable models | 0.644 | 2 | 2 | 100% |
| 10 | Fan and Truong (1993) Nonparametric regression with errors in variables | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 21 scored citations.