Prof. Dr. Martin Spindler
Mittwochs 13-14 Uhr und nach Vereinbarung.
|Fields of Interest||Econometrics and Statistics with Applications to Finance, Insurance and Health Economics
Nonparametric and High-dimensional Statistics / Econometrics, Machine Learning
|Current Position||Professor for Statistics, Department for Business Administration, University of Hamburg, since 6/2016|
|Professional Experience||Visiting Professor for Microeconometrics (substitution for Professor van den Berg), University Mannheim, Spring term 2016
Senior Researcher, Max Planck Society / Munich Center for the Economics of Aging, 5/2012-5/2016
Visiting Professor, Boston College, Boston, USA and Visiting Scholar, Massachusetts Institute of Technology, Cambridge, USA, 10/2015–12/2015
Research Fellow / Visiting Scholar, Massachusetts Institute of Technology, Cambridge, USA, 8/2013–7/2014
Research Stay, Singapore Management University, Singapore, 5/2012–6/2012
Visiting Scholar, Columbia University, 1/2011–4/2011
|Education||PhD in Economics, Munich Graduate School of Economics, University of Munich, 5/2012
Title: Essays in Econometrics
Master in Mathematics ("Diplom"), University of Munich, 2008.
B.A. Mathematics ("Vordiplom"), University of Regensburg, 2005.
Master in Economics ("Diplom"), University of Regensburg, 2003.
Abitur, Joseph-von-Fraunhofer Gymnasium, Cham, 1998.
Causal mediation analysis with double machine learning (with Helmut Farbmacher, Martin Huber, Lukas Laffers, Henrika Langen) (accepted at Econometrics Journal).
DoubleML – An Object-Oriented Implementation of Double Machine Learning in Python (with Philipp Bach, Victor Chernozhukov and Malte Kurz) (accepted at Journal of Machine Learning Research).
Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls (with Helmut Wasserbacher) (accepted at Digital Finance).
Transformation Models in High-Dimensions (with Sven Klaassen and Jannis Kück), 2021, Journal of Business and Economic Statistics, 1-11.
Heterogeneous Effects of Poverty on Cognition (with Helmut Farbmacher and Heinrich Kögel), 2021, Labour Economics Volume 71.
An Explainable Attention Network for Fraud Detection in Claims Management (with Helmut Farbmacher and Leander Löw), 2020, Journal of Econometrics.
Semiparametric count data modeling with an application to health service demand (with Philipp Bach and Helmut Farbmacher), Econometrics and Statistics (Special Issue on Nonparametric and Quantile Regression), 8, 125-140, 2018.
Raus aus der Black Box, Versicherungswirtschaft 73, 70-72, 2018.
L2Boosting for Economic Applications (with Ye Luo), American Economic Review, Papers and Proceedings, 107(5), 270–73, 2017.
hdm: High-Dimensional Metrics (with Victor Chernozhukov and Christian Hansen), R Journal 8(2), 185–199, 2016.
How do unisex rating regulations affect gender differences in insurance premiums? (with Vijay Aseervatham and Christoph Lex), The Geneva Papers on Risk and Insurance - Issues and Practice 41, 128-160, 2016.
Lasso for Instrumental Variable Selection: A Replication Study, Journal of Applied Econometrics, 31(2), 450–454, 2016.
Post-Selection and Post-Regularization Inference in Linear Models with many Controls and Instruments (with Victor Chernozhukov and Christian Hansen), American Economic Review, Papers and Proceedings, 105(5), 486–90, 2015.
Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach (with Victor Chernozhukov and Christian Hansen), Annual Review of Economics, Vol. 7: 649-688, 2015.
Stock Market Volatility: Identifying Major Drivers and the Nature of Their Impact (with Stefan Mittnik and Nikolay Robinzonov), Journal of Banking and Finance, 58, 1–14, 2015.
Asymmetric Information in (private) Accident Insurance, Economics Letters, 130, May 2015, 85–88, 2015.
Asymmetric Information in the Automobile Insurance: Evidence from Germany (with Steffen Hagmayer and Joachim Winter), Journal of Risk and Insurance 81 (4), 781–801, 2014.
Econometric Methods for Testing for Asymmetric Information – A Comparison of Parametric and Nonparametric Methods with an Application to Hospital Daily Benefits, The Geneva Risk and Insurance Review 39, 254–266, 2014.
Nonparametric Testing for Asymmetric Information (with Liangjun Su), Journal of Business and Economic Statistics 31 (2), 208–225, 2013.
Essays in Econometrics, Dissertation, University of Munich, 2012.
Asymmetric Information in Insurance Markets: Does it really exist? Insurance Economics 64, 6–8, July 2011.
L2Boosting for Estimation of Treatment Effects in a High-Dimensional Setting (with Jannis Kück and Ye Luo) (Revise & Resubmit at Journal of Econometrics).
Graphical Lasso Models in High-Dimensions (with Victor Chernozhukov, Sven Klaassen and Jannis Kück); presented at ICML 2018, CausalML Workshop (Revise & Resubmit at Biometrika).
Uniform Inference in High-Dimensional Additive Models (with Philipp Bach, Sven Klaassen, Jannis Kück) (Revise & Resubmit at Journal of Econometrics).
A Double Machine Learning Approach to Detecting Causal Effects in Panel Attrition (with Barbara Felderer) (Revise & Resubmit at Social Science Computer Review).
Sequence Embeddings help to identify fraudulent cases in healthcare insurance (with I. Fursov, A. Zaytsev, R. Khasyanov, E. Burnaev) (Reject & Resubmit at & IEEE Access).
L2Boosting in High-Dimensions: Rate of Convergence (with Ye Luo) (Reject & Resubmit at Journal of Machine Learning Reserach).
Valid Simultaneous Inference in High-Dimensional Settings (with Victor Chernozhukov and Philipp Bach) (Reject & Resubmit at Statistical Science).
DoubleML – An Object-Oriented Implementation of Double Machine Learning in R (with Philipp Bach, Victor Chernozhukov and Malte Kurz) (submitted).
Closing the U.S. Gender Wage Gap requires Understanding its Heterogeneity (with Philipp Bach and Victor Chernozhukov) (submitted).
Insights from Optimal Pandemic Shielding in a Multi-Group SEIR Framework (with Philipp Bach and Victor Chernozukov).
Self-Attention Network for Sequence Classification with Application to Claims Processing (with Leander Löw and Eike Brechmann).
Work in Progress
L1Boosting: Theoretical Results (with Sven Klassen and Ye Luo).
Adaptive Non-Parametric Smoothing of Discrete Variables (with Xi Chen, Victor Chernozhukov and Ye Luo).
Estimation of Nonlinear Panel Data Models with Machine Learning Methods (with Xi Chen, Victor Chernozhukov and Ye Luo).
High-Dimensional Varying Coefficient Models (with Zihao Yuan).
Knock-offs for Randomized Control Trials and Heterogenous Treatment Effects (with Sven Klaassen).
Demand Estimation: A comparison and Combination of Econometric and Machine Learning Methods (with Philipp Bach, Victor Chernozhukov, Sven Klaassen, Jannis Kück and Leander Löw).
Estimation of a High-dimensional Mincer Equation (with Philipp Bach, Victor Chernozhukov and Christian Hansen).
Estimation of Interest Rate Curves in a High-dimensional Setting (with Andreas Fuest).
Nonlinear and High-dimensional Modeling of VARs (with Stefan Mittnik and Nikolay Robinzonov).
Causal Machine Learning (with Victor Chernozhukov, Chris Hansen and Vasilis Syrgkanis; in preparation).
Moderne Verfahren der Angewandten Statistik (with Jan Gertheiss und Matthias Schmid; in preparation for Springer)
Chapters in Books
Long–term care insurance across Europe (with Tabea Bucher-Koenen and Johanna Schütz), Chapter 32, in: SHARE First Results Book 5, Börsch-Supan, A., T. Kneip, H. Litwin and G. Weber (Eds.).
Digital Finance – Die Zukunft der Finanzplanung in Unternehmen (with Heinrich Kögel and Helmut Wasserbacher), in: Arbeitswelt und KI 2030, Springer Gabler.