Estimating ordered categorical variables using panel data: a generalized ordered probit model with an autofit procedure Report as inadecuate




Estimating ordered categorical variables using panel data: a generalized ordered probit model with an autofit procedure - Download this document for free, or read online. Document in PDF available to download.

Abstract

Estimation procedures for ordered categories usually assume that the estimated coefficients of independent variables do not vary between the categories parallel-lines assumption. This view neglects possible heterogeneous effects of some explaining factors. This paper describes the use of an autofit option for identifying variables that meet the parallel-lines assumption when estimating a random effects generalized ordered probit model. We combine the test procedure developed by Richard Williams gologit2 with the random effects estimation command regoprob by Stefan Boes.



Item Type: MPRA Paper -

Original Title: Estimating ordered categorical variables using panel data: a generalized ordered probit model with an autofit procedure-

Language: English-

Keywords: generalized ordered probit; panel data; autofit, self-assessed health-

Subjects: C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C87 - Econometric SoftwareC - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C23 - Panel Data Models ; Spatio-temporal ModelsC - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C25 - Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions ; ProbabilitiesI - Health, Education, and Welfare > I1 - Health > I10 - General-





Author: Pfarr, Christian

Source: https://mpra.ub.uni-muenchen.de/24181/







Related documents