An analysis of selected labor market outcomes of college dropouts in Germany: A machine learning estimation approach. Research report
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- Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018.
"Double/debiased machine learning for treatment and structural parameters,"
Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
- Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
- Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey & James Robins, 2017. "Double/debiased machine learning for treatment and structural parameters," CeMMAP working papers CWP28/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey & James Robins, 2017. "Double/debiased machine learning for treatment and structural parameters," CeMMAP working papers 28/17, Institute for Fiscal Studies.
- Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, vol. 66(2), pages 315-332, March.
- repec:iza:izawol:journl:y:2015:p:182 is not listed on IDEAS
- Card, David, 1999. "The causal effect of education on earnings," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 30, pages 1801-1863, Elsevier.
- Farrell, Max H., 2015.
"Robust inference on average treatment effects with possibly more covariates than observations,"
Journal of Econometrics, Elsevier, vol. 189(1), pages 1-23.
- Max H. Farrell, 2013. "Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations," Papers 1309.4686, arXiv.org, revised Feb 2018.
- Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey, 2017. "Double/Debiased/Neyman Machine Learning of Treatment Effects," American Economic Review, American Economic Association, vol. 107(5), pages 261-265, May.
- Gary S. Becker, 1962.
"Investment in Human Capital: A Theoretical Analysis,"
NBER Chapters, in: Investment in Human Beings, pages 9-49,
National Bureau of Economic Research, Inc.
- Gary S. Becker, 1962. "Investment in Human Capital: A Theoretical Analysis," Journal of Political Economy, University of Chicago Press, vol. 70(5), pages 1-9.
- Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
- Sylke V. Schnepf, 2015. "University dropouts and labor market success," IZA World of Labor, Institute of Labor Economics (IZA), pages 182-182, September.
- Guido W. Imbens, 2004.
"Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review,"
The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
- Guido W. Imbens, 2003. "Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Review," NBER Technical Working Papers 0294, National Bureau of Economic Research, Inc.
- Aina, Carmen & Baici, Eliana & Casalone, Giorgia & Pastore, Francesco, 2018.
"The economics of university dropouts and delayed graduation: a survey,"
GLO Discussion Paper Series
189, Global Labor Organization (GLO).
- Aina, Carmen & Baici, Eliana & Casalone, Giorgia & Pastore, Francesco, 2018. "The Economics of University Dropouts and Delayed Graduation: A Survey," IZA Discussion Papers 11421, Institute of Labor Economics (IZA).
- Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
- Glynn, Adam N. & Quinn, Kevin M., 2010. "An Introduction to the Augmented Inverse Propensity Weighted Estimator," Political Analysis, Cambridge University Press, vol. 18(1), pages 36-56, January.
- Arrow, Kenneth J., 1973. "Higher education as a filter," Journal of Public Economics, Elsevier, vol. 2(3), pages 193-216, July.
- Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, October.
- Grubb, W. Norton, 2002. "Learning and earning in the middle, part I: national studies of pre-baccalaureate education," Economics of Education Review, Elsevier, vol. 21(4), pages 299-321, August.
- Yona Rubinstein & James J. Heckman, 2001. "The Importance of Noncognitive Skills: Lessons from the GED Testing Program," American Economic Review, American Economic Association, vol. 91(2), pages 145-149, May.
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Cited by:
- Heigle, Julia & Pfeiffer, Friedhelm, 2020. "Langfristige Wirkungen eines nicht abgeschlossenen Studiums auf individuelle Arbeitsmarktergebnisse und die allgemeine Lebenszufriedenheit," ZEW Discussion Papers 20-004, ZEW - Leibniz Centre for European Economic Research.
- Neugebauer, Martin & Daniel, Annabell, 2021. "Higher Education Non-Completion, Employers, and Labor Market Integration: Experimental Evidence," SocArXiv evm74, Center for Open Science.
- McNamara, Sarah, 2020. "Returns to higher education and dropouts: A double machine learning approach," ZEW Discussion Papers 20-084, ZEW - Leibniz Centre for European Economic Research.
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