arXiv 24 Apr 2020 · Econometrics · 37 citations (OpenAlex)
arXiv:2004.11751 · PDF · DOI · OpenAlex · Extracted main text
This chapter reviews the microeconometrics literature on partial identification, focusing on the developments of the last thirty years. The topics presented illustrate that the available data combined with credible maintained assumptions may yield much information about a parameter of interest, even if they do not reveal it exactly. Special attention is devoted to discussing the challenges associated with, and some of the solutions put forward to, (1) obtain a tractable characterization of the values for the parameters of interest which are observationally equivalent, given the available data and maintained assumptions; (2) estimate this set of values; (3) conduct test of hypotheses and make confidence statements. The chapter reviews advances in partial identification analysis both as applied to learning (functionals of) probability distributions that are well-defined in the absence of models, as well as to learning parameters that are well-defined only in the context of particular models. A simple organizing principle is highlighted: the source of the identification problem can often be traced to a collection of random variables that are consistent with the available data and maintained assumptions. This collection may be part of the observed data or be a model implication. In either case, it can be formalized as a random set. Random set theory is then used as a mathematical framework to unify a number of special results and produce a general methodology to carry out partial identification analysis.
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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 | Manski and Tamer (2002) Inference on Regressions with Interval Data on a Regressor or Outcome | 1.000 | 34 | 3 | 100% |
| 2 | Beresteanu and Molinari (2008) Asymptotic Properties for a Class of Partially Identified Models | 1.000 | 23 | 6 | 100% |
| 3 | Beresteanu, Molchanov, and Molinari (2011) Sharp identification regions in models with convex moment predictions self | 1.000 | 21 | 5 | 100% |
| 4 | Ciliberto and Tamer (2009) Market Structure and Multiple Equilibria in Airline Markets | 1.000 | 21 | 3 | 100% |
| 5 | Chernozhukov, Hong, and Tamer (2007) Estimation and Confidence Regions for Parameter Sets in Econometric Models | 1.000 | 14 | 3 | 100% |
| 6 | Kaido, Molinari, and Stoye (2019) Confidence Intervals for Projections of Partially Identified Parameters | 1.000 | 10 | 3 | 100% |
| 7 | Manski (2003) Partial Identification of Probability Distributions | 1.000 | 10 | 3 | 100% |
| 8 | Manski (1989) Anatomy of the Selection Problem | 1.000 | 8 | 4 | 100% |
| 9 | Andrews and Soares (2010) Inference for Parameters Defined by Moment Inequalities Using Generalized Moment Selection | 1.000 | 7 | 4 | 100% |
| 10 | Chesher and Rosen (2019) Generalized instrumental variable models, methods, and applications | 1.000 | 7 | 3 | 100% |
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