Dr. Markus Fritsch
+49 (0)851 509-2565
+49 (0)851 509-2562
https://github.com/markusfritsch
WIWI 312
Innstraße 27
94032 Passau
Student consultation hours: Tue 09:00-10:00 am (please send an email notification in advance) or by individual appointment
Curriculum vitae
09/2019 - present: Assistant Professor Chair of Statistics U Passau
10/2014 - 09/2019: Ph.D. in Economics U Passau
12/2012 - 07/2014: Student Assistant Chair of Statistics U Passau
10/2012 - 08/2014: M.A. International Economics & Business U Passau
04/2011 - 08/2014: M.Sc. Business Administration U Passau
10/2007 - 03/2011: B.Sc. Business Administration & Economics U Passau and San Diego State University (CA, USA)
Publications
Fritsch M., Pua A. A. Y. and J. Schnurbus [2024],Teaching Advanced Topics in Econometrics using Introductory Textbooks: The Case of Dynamic Panel Data Methods. International Review of Economics Education, 47, 100297
Fritsch M., Haupt H., and J. Schnurbus [2024+], Efficiency of poll-based multi-period forecasting systems for German state elections. International Journal of Forecasting, forthcoming
Ranpal S., von Bargen S., Gilles S., Luschkova D., Landgraf M., Bogawski P., Traidl-Hoffmann C., Büttner C., Damialis A., Fritsch M., and S. Jochner-Oette [2024], Continental-scale Evaluation of Downy Birch Pollen Production: Estimating the Impacts of Global Change.Environmental Research, 252, 119114
Kleinke K., Fritsch M., Stemmler M., and F. Lösel [2024+], Multiple imputation of longitudinal data – A comparison of robust imputation methods regarding sample size requirements, with an application to corporal punishment data. In: Dependent Data in Social Sciences Research, 2nd edition, forthcoming
Jetschni J., Fritsch M., and S. Jochner-Oette [2023], How does pollen production of allergenic species differ between urban and rural environments? International Journal of Biometeorology, 67, 1839-1852
Haupt H. and M. Fritsch [2022], Quantile Trend Regression and Its Application to Central England Temperature; Mathematics, 10 (3), 413
Fritsch M. and S. Behm [2021], Data for modeling nitrogen dioxide concentration levels across Germany; Data in Brief, 38, 107324
Kleinke K., Fritsch M., Stemmler M., Reinecke J., and F. Lösel [2021], Quantile Regression-Based Multiple Imputation of Missing Values - An Evaluation and Application to Corporal Punishment Data; Methodology, 17 (3), 205-230
Fritsch M. and S. Behm [2021], Agglomeration and infrastructure effects in land use regression models for air pollution - Specification, estimation, and interpretations; Atmospheric Environment, 253, 118337
Fritsch M., Pua A. A. Y. and J. Schnurbus [2021], pdynmc: A Package for Estimating Linear Dynamic Panel Data Models Based on Nonlinear Moment Conditions; The R Journal, 13 (1), 218-231
Fritsch M., Haupt H., Lösel F. and M. Stemmler [2019], Regression trees and random forests as alternatives to classical regression modeling: Investigating the risk factors for corporal punishment. Psychological Test and Assessment Modeling, 61 (4), 389-417
Fritsch, M. [2019], Five essays on linear dynamic panel data models, Dissertation
Fritsch M., Haupt H. and P.T. Ng [2016], Urban house price surfaces near a World Heritage Site: modeling conditional price and spatial heterogeneity. Regional Science and Urban Economics, 60, 260-275
Software
pdynmc: Dynamic linear panel estimation based on linear and nonlinear moment conditions. Available from: https://cran.r-project.org/web/packages/pdynmc/; for the latest version, see: https://github.com/markusfritsch/pdynmc
fixedEventFC: Functions and data for investigating the efficiency of poll-based multi-period forecasting systems for German state elections. Available from: https://github.com/markusfritsch/fixedEventFC
quantWarming: Data and functions for trend analysis of temperature time series. Available from: https://github.com/markusfritsch/quantWarming
smoothLUR: Functions and data for smooth land use regression modeling. Available from: https://github.com/markusfritsch/smoothLUR
Research focuses
- Semi- and nonparametric estimation
- Quantile regression
- Panel data models
- Applied statistics and econometrics
- Data Science and statistical learning