arXiv 5 Dec 2022 · Econometrics
arXiv:2212.02585 · PDF · DOI · OpenAlex · Extracted main text
In empirical studies, the data usually don't include all the variables of interest in an economic model. This paper shows the identification of unobserved variables in observations at the population level. When the observables are distinct in each observation, there exists a function mapping from the observables to the unobservables. Such a function guarantees the uniqueness of the latent value in each observation. The key lies in the identification of the joint distribution of observables and unobservables from the distribution of observables. The joint distribution of observables and unobservables then reveal the latent value in each observation. Three examples of this result are discussed.
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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 | Hu (2008) Identification and Estimation of Nonlinear Models with Misclassification Error Using Instrumental Variables: A General Solution self | 0.511 | 2 | 1 | 100% |
| 2 | Hu, Liu and Yao (2022) Revealing Unobservables by Deep Learning: Generative Element Extraction Networks (GEEN) | 0.405 | 1 | 1 | 100% |
| 3 | Hu (2017) The econometrics of unobservables: Applications of measurement error models in empirical industrial organization and labor econo… self | 0.405 | 1 | 1 | 100% |
| 4 | Kotlarski (1966) On Some Characterizations of Probability Distributions in Hilbert Spaces | 0.405 | 1 | 1 | 100% |
| 5 | Rao (1992) | 0.405 | 1 | 1 | 100% |
Showing the top 5 of 5 scored citations.