Welcome to my website! I am a Junior Professor (non-tenure) of Economics at Heidelberg University. I studied Mathematics with a major in computational statistics and obtained my PhD in Economics from the Graduate School for Economics, Finance and Management (GSEFM) at Goethe University Frankfurt. I completed my postdoctoral research at the Universities of Zurich and Basel.
I work on uncertainty quantification in forecasting, surveys, experiments, health, and economic modeling. My work lies at the crossroads between decision science, computational statistics, and economics.
Contact:
Email: patrick.schmidt@awi.uni-heidelberg.de
with Matthias Katzfuss (Texas A&M) and Tilmann Gneiting (HITS, KIT)
Discussion of "Elicitability and backtesting: Perspectives for banking regulation".
The Annals of Applied Statistics, 11(4):1883-1885, 2017.
Dynamic Measurement Invariance with Bayesian shrinkage
with Holger Brandt (University of Tübingen)Beyond simulations: Stratified randomization and regression adjustments for experimental data (draft available upon request)
with Simon Heß (University of Vienna)Optimal state-dependent forecasting for Value-at-Risk under the Basel Accord (slides available upon request)
PointFore (available on CRAN and Github):
Estimate specification models for the state-dependent level of an optimal quantile/expectile forecast. Wald Tests and the test of overidentifying restrictions are implemented. Plotting of the estimated specification model is possible. The package contains two data sets with forecasts and realizations: the daily accumulated precipitation at London, UK from the high-resolution model of the European Centre for Medium-Range Weather Forecasts (ECMWF) and GDP growth Greenbook data by the US Federal Reserve. See Schmidt, Katzfuss and Gneiting (2021) for more details on the identification and estimation of a directive behind a point forecast.
Further R-Packages are currently available upon request and will be uploaded when research papers are finished. Please get in contact.
Inference under Superspreading: Determinants of SARS-CoV-2 Transmission in Germany (2020). Github project: InfSup.
Testing rationality of mean, median and mode forecasts based on the paper "Testing forecast rationality for measures of central tendency".
Fitting probability distributions to quantiles and interval probabilities and extract functionals and scores from fitted distributions based on the paper "Belief elicitation with multiple point predictions".