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Technische Universität München
Technische Universität München

Research agenda

A vital aspect of the digital transformation is the exploration and analysis of big data, which are often not amenable to standard econometric models. In a series of publications, we propose machine learning techniques that can be used for these purposes. The findings of these projects have theoretical implications for research on the use of machine learning in econometrics, but also practical applications for large companies and (smaller) family-owned firms. 

To foster international collaboration between econometricians, we co-organize the Munich Econometrics Seminar, where the latest advances in the field are presented and discussed:
Munich Econometrics Seminar

Current research projects

Learning from high-dimensional, heterogeneous data: Machine learning methods in econometrics, supported by the German Research Foundation (DFG), 2021-2024.

In this project we work with methods from Machine Learning and Artificial Intelligence in microeconomic applications to estimate heterogeneous causal effects and to predict individual and firm behaviour.

All publications

2022

  • Bach, Philipp; Chernozhukov, Victor; Kurz, Malte S. Kurz; Spindler, Martin: DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python. Journal of Machine Learning Research 23 (53), 2022, 1-6 mehr…
  • Farbmacher, Helmut; Guber, Raphael; Klaassen, Sven: Instrument Validity Tests With Causal Forests. Journal of Business & Economic Statistics 40 (2), 2022, 605-614 mehr…
  • Farbmacher, Helmut; Hartmann, Maximilian; Kögel, Heinrich: Economic Hardship, Sleep and Self-Rated Health: Evidence from the Supplemental Nutrition Assistance Program (SNAP). American Journal of Health Economics 8 (2), 2022, 216-251 mehr…
  • Farbmacher, Helmut; Huber, Martin; Lafférs, Lukáš; Langen, Henrika; Spindler, Martin: Causal Mediation Analysis with Double Machine Learning. Econometrics Journal 25 (2), 2022, 277-300 mehr…
  • Farbmacher, Helmut; Löw, Leander; Spindler, Martin: An Explainable Attention Network for Fraud Detection in Claims Management. Journal of Econometrics 228 (2), 2022, 244-258 mehr…
  • Kurz, Malte S.; Mittnik, Stefan: Risk Assessment and Spurious Seasonality. Econometrics and Statistics, 2022 mehr…
  • Kurz, Malte S.; Spanhel, Fabian: Testing the simplifying assumption in high-dimensional vine copulas. Electronic Journal of Statistics 16 (2), 2022 mehr…

2021

  • Farbmacher, Helmut; Kögel, Heinrich; Spindler, Martin: Heterogeneous Effects of Poverty on Attention. Labour Economics 71, 2021, 102028 mehr…
  • Kurz, Malte S.: Distributed Double Machine Learning with a Serverless Architecture. Companion of the ACM/SPEC International Conference on Performance Engineering, ACM, 2021Virtual Event, France mehr…

2020

  • Bucher-Koenen, Tabea; Farbmacher, Helmut; Guber, Raphael; Vikström, Johan: Double Trouble: The Burden of Child-rearing and Working on Maternal Mortality. Demography 57 (2), 2020, 559-576 mehr…
  • Bun, Maurice J. G.; Farbmacher, Helmut; Poldermans, Rutger W.: Finite Sample Properties of the GMM Anderson–Rubin Test. Econometric Reviews 39 (10), 2020, 1042-1056 mehr…

2019

  • Farbmacher, Helmut; Kann, Alexander: On the Effect of Imputation on the 2SLS Variance. On the Effect of Imputation on the 2SLS Variance, 2019 mehr…
  • Spanhel, Fabian; Kurz, Malte S.: Simplified vine copula models: Approximations based on the simplifying assumption. Electronic Journal of Statistics 13 (1), 2019 mehr…
  • Windmeijer, Frank; Farbmacher, Helmut; Davies, Neil; Davey Smith, George: On the Use of the Lasso for Instrumental Variables Estimation with Some Invalid Instruments. Journal of the American Statistical Association 114 (527), 2019, 1339-1350 mehr…

2018

  • Bach, Philipp; Farbmacher, Helmut; Spindler, Martin: Semiparametric Count Data Modeling with an Application to Health Service Demand. Econometrics and Statistics 8, 2018, 125-140 mehr…
  • Farbmacher, Helmut; Guber, Raphael; Vikström, Johan: Increasing the Credibility of the Twin Birth Instrument. Journal of Applied Econometrics 33 (3), 2018, 457-472 mehr…
  • Helmut Farbmacher: SIVREG: Stata module to perform adaptive Lasso with some invalid instruments. SIVREG: Stata module to perform adaptive Lasso with some invalid instruments, 2018 mehr…
  • Kurz, Malte S.: A note on low-dimensional Kalman smoothers for systems with lagged states in the measurement equation. Economics Letters 168, 2018, 42-45 mehr…

2017

  • Farbmacher, Helmut; Ihle, Peter; Schubert, Ingrid; Winter, Joachim; Wuppermann, Amelie: Heterogeneous Effects of a Nonlinear Price Schedule for Outpatient Care. Health Economics 26 (10), 2017, 1234-1248 mehr…
  • Farbmacher, Helmut; Kögel, Heinrich: Testing under a Special Form of Heteroscedasticity. Applied Economics Letters 24 (4), 2017, 264-268 mehr…

2016

  • Spanhel, Fabian; Kurz, Malte S.: The partial copula: Properties and associated dependence measures. Statistics & Probability Letters 119, 2016, 76-83 mehr…

2015

  • Davies, Neil M.; von Hinke Kessler Scholder, Stephanie; Farbmacher, Helmut; Burgess, Stephen; Windmeijer, Frank; Smith, George Davey: The Many Weak Instruments Problem and Mendelian Randomization. Statistics in Medicine 34 (3), 2015, 454-468 mehr…

2013

  • Farbmacher, Helmut: Extensions of Hurdle Models for Overdispersed Count Data. Health Economics 22 (11), 2013, 1398-1404 mehr…
  • Farbmacher, Helmut; Winter, Joachim: Per-Period Co-Payments and the Demand for Health Care: Evidence from Survey and Claims Data. Health Economics 22 (9), 2013, 1111-1123 mehr…

2012

  • Farbmacher, Helmut: GMM with Many Weak Moment Conditions: Replication and Application of Newey and Windmeijer (2009). Journal of Applied Econometrics 27 (2), 2012, 343-346 mehr…

2011

  • Farbmacher, Helmut: Estimation of Hurdle Models for Overdispersed Count Data. The Stata Journal 11 (1), 2011, 82-94 mehr…
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