Towards a reliable categorical regression analysis for non-randomly coarsened observations: An analysis with German labour market data

Plass, Julia; Cattaneo, Marco E. G. V.; Augustin, Thomas; Schollmeyer, Georg and Heumann, Christian (28. November 2017): Towards a reliable categorical regression analysis for non-randomly coarsened observations: An analysis with German labour market data. Department of Statistics: Technical Reports, No.206

Abstract

In most surveys, one is confronted with missing or, more generally, coarse data. Many methods dealing with these data make strong, untestable assumptions, e.g. coarsening at random. But due to the potentially resulting severe bias, interest increases in approaches that only include tenable knowledge about the coarsening process, leading to imprecise, but credible results. We elaborate such cautious methods for regression analysis with a coarse categorical dependent variable and precisely observed categorical covariates. Our cautious results from the German panel study "Labour market and social security'' illustrate that traditional methods may even pretend specific signs of the regression estimates.

• Item Type: Paper
• Keywords: coarse data, (cumulative) logit model, missing data, partial identification, PASS data, (profile) likelihood
• Faculties: Mathematics, Computer Science and Statistics > Statistics > Technical Reports
• Subjects: 500 Science > 510 Mathematics
• JEL Classification: C83
• Language: German