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A bivariate ordered probit estimator with mixed effects

Authors: Franz Buscha; Anna Conte;

A bivariate ordered probit estimator with mixed effects

Abstract

In this paper, we discuss the derivation and application of a bivariate ordered probit model with mixed effects. Our approach allows one to estimate the distribution of the effect (gamma) of an endogenous ordered variable on an ordered explanatory variable. By allowing gamma to vary over the population, our estimator offers a more flexible parametric setting to recover the causal effect of an endogenous variable in an ordered choice setting. We use Monte Carlo simulations to examine the performance of the maximum likelihood estimator of our system and apply this to a relevant example from the UK education literature.

Keywords

ddc:330, Großbritannien, bivariate ordered probit, maximum likelihood, mixed effects, truancy, Maximum-Likelihood-Methode, C51, Probit-Modell, bivariate ordered probit, Bildungsökonomik, mixed effects, C35, I20, truancy, maximum likelihood, Theorie, Schätzung, jel: jel:C51, jel: jel:C35, jel: jel:I20

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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