Simple closed-form estimation of a binary latent variable model

Yingyao Hu, Jingrong Li, Ji-Liang Shiu and Matthew Shum

The Econometrics Journal, 2025, vol. 28, issue 2, 198-218

Abstract: SummaryThis paper develops a closed-form non-parametric estimator of the conditional distribution function for a binary outcome variable given an unobserved latent variable. This type of function is commonly used in models that involve measurement error and dynamic models with agent-specific unobserved heterogeneity. This paper presents a consistent extremum sieve estimator with the following advantages: (i) it has a closed-form expression for all the sieve coefficients, (ii) it is computationally straightforward, equivalent to computing eigenvalues and eigenvectors of matrices without the use of iterative optimization algorithms. While as flexible as the sieve maximum likelihood estimator previously proposed for this model, our estimator proves computationally simpler. The finite sample properties of the estimator are investigated through a Monte Carlo study, and the developed estimator is applied to a probit model to assess the targeting performance of a social welfare program.

Keywords: Binary outcome variable; unobserved latent variable; closed-form expression (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1093/ectj/utae016 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:emjrnl:v:28:y:2025:i:2:p:198-218.

Access Statistics for this article

The Econometrics Journal is currently edited by Jaap Abbring

More articles in The Econometrics Journal from Royal Economic Society Contact information at EDIRC.
Bibliographic data for series maintained by Oxford University Press ().