Flexible instrumental variable distributional regression
| dc.contributor.author | BriseƱo Sanchez, Guillermo | |
| dc.contributor.author | Hohberg, Maike | |
| dc.contributor.author | Groll, Andreas | |
| dc.contributor.author | Kneib, Thomas | |
| dc.date.accessioned | 2021-08-02T11:03:09Z | |
| dc.date.available | 2021-08-02T11:03:09Z | |
| dc.date.issued | 2020-08-16 | |
| dc.description.abstract | We tackle two limitations of standard instrumental variable regression in experimen- tal and observational studies: restricted estimation to the conditional mean of the outcome and the assumption of a linear relationship between regressors and outcome. More flexible regres- sion approaches that solve these limitations have already been developed but have not yet been adopted in causality analysis. The paper develops an instrumental variable estimation proce- dure building on the framework of generalized additive models for location, scale and shape. This enables modelling all distributional parameters of potentially complex response distribu- tions and non-linear relationships between the explanatory variables, instrument and outcome. The approach shows good performance in simulations and is applied to a study that estimates the effect of rural electrification on the employment of females and males in the South African province of KwaZulu-Natal. We find positive marginal effects for the mean for employment of females rates, negative effects for employment of males and a reduced conditional standard deviation for both, indicating homogenization in employment rates due to the electrification pro- gramme. Although none of the effects are statistically significant, the application demonstrates the potentials of using generalized additive models for location, scale and shape in instrumental variable regression for both to account for endogeneity and to estimate treatment effects beyond the mean. | en |
| dc.identifier.uri | http://hdl.handle.net/2003/40358 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-22233 | |
| dc.language.iso | en | de |
| dc.relation.ispartofseries | Journal of the Royal Statistical Society: Series A;Vol. 183. 2020, Issue 4, pp 1553-1574 | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Causality | en |
| dc.subject | Distributional regression | en |
| dc.subject | Generalized additive models for location | en |
| dc.subject | Scale and shape | en |
| dc.subject | Instrumental variable | en |
| dc.subject | Treatment effects | en |
| dc.subject.ddc | 310 | |
| dc.title | Flexible instrumental variable distributional regression | en |
| dc.type | Text | de |
| dc.type.publicationtype | article | de |
| dcterms.accessRights | open access | |
| eldorado.secondarypublication | true | de |
| eldorado.secondarypublication.primarycitation | Journal of the Royal Statistical Society: Series A. Vol. 183. 2020, issue 4, pp 1553-1574. Special Issue: Causal inference from nonāexperimental studies: challenges, developments and applications | en |
| eldorado.secondarypublication.primaryidentifier | https://doi.org/10.1111/rssa.12598 | de |