A binary choice model with partial observability for panel data

Issue title: Special Issue In commemoration of Professor Sergey Aivazian and his remarkable contributions to statistics and econometrics: Multivariate Statistics in Applications to Econometrics

Guest editors: Henry Penikas

Article type: Research Article

Authors: Batalova, Ekaterina^a | Furmanov, Kirill^{a; *} | Shelkova, Ekaterina^b

Affiliations: [a] The National Research University Higher School of Economics, Moscow, Russia | [b] Deloitte, Risk Advisory Department Moscow, Russia

Correspondence: [*] Corresponding author: Kirill Furmanov, Russian Federation, Moscow, 11 Pokrovsky Bulvar, Pokrovka Complex, room S526, Russia. Tel.: +7 917 567 9001; E-mail: kfurmanov@hse.ru.

Abstract: We consider a panel model with a binary response variable that is a product of two unobservable factors, each determined by a separate binary choice equation. One of these factors is assumed to be time-invariant and may be interpreted as a latent class indicator. A simulation study shows that maximum likelihood estimates from even the shortest panel are much more reliable than those obtained from a cross-section. As an illustrative example, the model is applied to Russian Longitudinal Monitoring Survey data to estimate a proportion of the non-employed population who are participating in job search.

Keywords: Partial observability, split population, binary choice, latent classes

DOI: 10.3233/MAS-200503

Journal: Model Assisted Statistics and Applications, vol. 15, no. 4, pp. 315-322, 2020