Bayesian nonparametric instrumental variable regression based on penalized splines and Dirichlet process mixtures
We propose a Bayesian nonparametric instrumental variable approach that allows us to correct for endogeneity bias in regression models where the covariate effects enter with unknown functional form. Bias correction relies on a simultaneous equations specification with flexible modeling of the join...
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| Formato: | Working Paper |
| Lenguaje: | Inglés |
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Georg-August-Universität Göttingen. Courant Research Centre. Poverty, Equity and Growth
2025
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| Acceso en línea: | https://www.econstor.eu/bitstream/10419/90568/1/CRC-PEG_DP_127.pdf http://repositorio.unne.edu.ar/handle/123456789/57160 |
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I48-R184-123456789-571602025-10-20T12:18:46Z Bayesian nonparametric instrumental variable regression based on penalized splines and Dirichlet process mixtures Wiesenfarth, Manuel Hisgen, Carlos Matías Kneib, Thomas Cadarso Suárez, Carmen Endogeneity Markov Chain Monte Carlo methods Simultaneous credible bands We propose a Bayesian nonparametric instrumental variable approach that allows us to correct for endogeneity bias in regression models where the covariate effects enter with unknown functional form. Bias correction relies on a simultaneous equations specification with flexible modeling of the joint error distribution implemented via a Dirichlet process mixture prior. Both the structural and instrumental variable equation are specified in terms of additive predictors comprising penalized splines for nonlinear effects of continuous covariates. Inference is fully Bayesian, employing efficient Markov Chain Monte Carlo simulation techniques. The resulting posterior samples do not only provide us with point estimates, but allow us to construct simultaneous credible bands for the nonparametric effects, including data-driven smoothing parameter selection. In addition, improved robustness properties are achieved due to the flexible error distribution specification. Both these features are extremely challenging in the classical framework, making the Bayesian one advantageous. In simulations, we investigate small sample properties and an investigation of the effect of class size on student performance in Israel provides an illustration of the proposed approach which is implemented in an R package bayesIV. 2025-08-05T14:09:22Z 2025-08-05T14:09:22Z 2012 Working Paper Wiesenfarth, Manuel, et. al., 2012. Bayesian nonparametric instrumental variable regression based on penalized splines and Dirichlet process mixtures. Germany: Georg-August-Universität Göttingen. Courant Research Centre. Poverty, Equity and Growth https://www.econstor.eu/bitstream/10419/90568/1/CRC-PEG_DP_127.pdf http://repositorio.unne.edu.ar/handle/123456789/57160 eng Discussion papers;127 application/pdf Georg-August-Universität Göttingen. Courant Research Centre. Poverty, Equity and Growth |
| institution |
Universidad Nacional del Nordeste |
| institution_str |
I-48 |
| repository_str |
R-184 |
| collection |
RIUNNE - Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE) |
| language |
Inglés |
| topic |
Endogeneity Markov Chain Monte Carlo methods Simultaneous credible bands |
| spellingShingle |
Endogeneity Markov Chain Monte Carlo methods Simultaneous credible bands Wiesenfarth, Manuel Hisgen, Carlos Matías Kneib, Thomas Cadarso Suárez, Carmen Bayesian nonparametric instrumental variable regression based on penalized splines and Dirichlet process mixtures |
| topic_facet |
Endogeneity Markov Chain Monte Carlo methods Simultaneous credible bands |
| description |
We propose a Bayesian nonparametric instrumental variable approach that allows
us to correct for endogeneity bias in regression models where the covariate effects
enter with unknown functional form. Bias correction relies on a simultaneous equations specification with flexible modeling of the joint error distribution implemented
via a Dirichlet process mixture prior. Both the structural and instrumental variable
equation are specified in terms of additive predictors comprising penalized splines
for nonlinear effects of continuous covariates. Inference is fully Bayesian, employing
efficient Markov Chain Monte Carlo simulation techniques. The resulting posterior
samples do not only provide us with point estimates, but allow us to construct
simultaneous credible bands for the nonparametric effects, including data-driven
smoothing parameter selection. In addition, improved robustness properties are
achieved due to the flexible error distribution specification. Both these features are
extremely challenging in the classical framework, making the Bayesian one advantageous. In simulations, we investigate small sample properties and an investigation
of the effect of class size on student performance in Israel provides an illustration
of the proposed approach which is implemented in an R package bayesIV. |
| format |
Working Paper |
| author |
Wiesenfarth, Manuel Hisgen, Carlos Matías Kneib, Thomas Cadarso Suárez, Carmen |
| author_facet |
Wiesenfarth, Manuel Hisgen, Carlos Matías Kneib, Thomas Cadarso Suárez, Carmen |
| author_sort |
Wiesenfarth, Manuel |
| title |
Bayesian nonparametric instrumental variable
regression based on penalized splines and Dirichlet
process mixtures |
| title_short |
Bayesian nonparametric instrumental variable
regression based on penalized splines and Dirichlet
process mixtures |
| title_full |
Bayesian nonparametric instrumental variable
regression based on penalized splines and Dirichlet
process mixtures |
| title_fullStr |
Bayesian nonparametric instrumental variable
regression based on penalized splines and Dirichlet
process mixtures |
| title_full_unstemmed |
Bayesian nonparametric instrumental variable
regression based on penalized splines and Dirichlet
process mixtures |
| title_sort |
bayesian nonparametric instrumental variable
regression based on penalized splines and dirichlet
process mixtures |
| publisher |
Georg-August-Universität Göttingen. Courant Research Centre. Poverty, Equity and Growth |
| publishDate |
2025 |
| url |
https://www.econstor.eu/bitstream/10419/90568/1/CRC-PEG_DP_127.pdf http://repositorio.unne.edu.ar/handle/123456789/57160 |
| work_keys_str_mv |
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1849012467648167936 |