arXiv 24 Feb 2020 · Econometrics
arXiv:2002.10415 · PDF · DOI · OpenAlex · Extracted main text
This paper studies the identification, estimation, and hypothesis testing problem in complete and incomplete economic models with testable assumptions. Testable assumptions ($A$) give strong and interpretable empirical content to the models but they also carry the possibility that some distribution of observed outcomes may reject these assumptions. A natural way to avoid this is to find a set of relaxed assumptions ($\tilde{A}$) that cannot be rejected by any distribution of observed outcome and the identified set of the parameter of interest is not changed when the original assumption is not rejected. The main contribution of this paper is to characterize the properties of such a relaxed assumption $\tilde{A}$ using a generalized definition of refutability and confirmability. I also propose a general method to construct such $\tilde{A}$. A general estimation and inference procedure is proposed and can be applied to most incomplete economic models. I apply my methodology to the instrument monotonicity assumption in Local Average Treatment Effect (LATE) estimation and to the sector selection assumption in a binary outcome Roy model of employment sector choice. In the LATE application, I use my general method to construct a set of relaxed assumptions $\tilde{A}$ that can never be rejected, and the identified set of LATE is the same as imposing $A$ when $A$ is not rejected. LATE is point identified under my extension $\tilde{A}$ in the LATE application. In the binary outcome Roy model, I use my method of incomplete models to relax Roy's sector selection assumption and characterize the identified set of the binary potential outcome as a polyhedron.
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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 | Kitagawa, T (2015) A test for instrument validity | 0.874 | 6 | 2 | 100% |
| 2 | Mourifie, I., M. Henry, and R. Méango (2018) Sharp bounds and testability of a roy model of stem major choices | 0.874 | 5 | 2 | 100% |
| 3 | Imbens, G. and J. Angrist (1994) Identification and estimation of local average treatment effects | 0.737 | 3 | 2 | 100% |
| 4 | Breusch, T. S (1986) Hypothesis testing in unidentified models | 0.737 | 3 | 2 | 100% |
| 5 | Mourifié, I. and Y. Wan (2017) Testing local average treatment effect assumptions | 0.737 | 3 | 2 | 100% |
| 6 | Card, D (1993) Using geographic variation in college proximity to estimate the return to schooling | 0.644 | 2 | 2 | 100% |
| 7 | Koopmans, T. C. and O. Reiersol (1950) The identification of structural characteristics | 0.644 | 2 | 2 | 100% |
| 8 | Manski, C. F (2019) Econometrics for decision making: Building foundations sketched by haavelmo and wald | 0.585 | 3 | 1 | 100% |
| 9 | Tamer, E (2003) Incomplete simultaneous discrete response model with multiple equilibria | 0.585 | 3 | 1 | 100% |
| 10 | Chesher, A. and A. M. Rosen (2012) Simultaneous equations models for discrete outcomes: coherence, completeness, and identification | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 26 scored citations.