TY  - RPRT
        AU  - Bacarreza, Gustavo J. Canavire
AU  - Rios-Avila, Fernando
AU  - Sacco-Capurro, Flavia
        TI  - Recovering Income Distribution in the Presence of Interval-Censored Data
        PY  - 2023/Feb/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 15921
        UR  - https://www.iza.org/publications/dp15921
        AB  - This paper proposes a method to analyze interval-censored data, using multiple imputation based on a heteroskedastic interval regression approach. The proposed model aims to obtain a synthetic data set that can be used for standard analysis, including standard linear regression, quantile regression, or poverty and inequality estimation. The paper presents two applications to show the performance of the method. First, it runs a Monte Carlo simulation to show the method's performance under the assumption of multiplicative heteroskedasticity, with and without conditional normality. Second, it uses the proposed methodology to analyze labor income data in Grenada for 2013–20, where the salary data are interval-censored according to the salary intervals prespecified in the survey questionnaire. The results obtained are consistent across both exercises.
        KW  - heteroskedastic interval regression
KW  - Monte Carlo simulation
KW  - interval-censored data
KW  - wages
        ER  -