Patrick W. Schmidt
Welcome to my website! I am a PhD Candidate at the Graduate School for Economics, Finance and Management (GSEFM) at Goethe University Frankfurt and a member of the Computational Statistics Group at the Heidelberg Institute for Theoretical Studies (HITS).
I work on uncertainty quantification in forecasting, surveys, laboratory experiments, and economic modelling. My work lies at the crossroad between decision science, computational statistics, and experimental economics. The applications of my work range from development and behavioral economics to financial risk measurement and macroeconomic forecasting.
- Email: Patrick.Schmidt@h-its.org
Belief elicitation with multiple point predictions (2018) (Job Market Paper 1)
with Markus Eyting (GSEFM Frankfurt)
Elicitation of ambiguous beliefs with mixing bets (2019) (Job Market Paper 2)
with Matthias Katzfuss (Texas A&M) and Tilmann Gneiting (HITS, KIT)
with Andrew J. Patton (Duke) and Timo Dimitriadis (HITS)
Work in Progress
An income expectation panel of Indian fishers: Uncertainty perception and consumptionwith Xavier Giné (World Bank)
Inference on information sets: Estimation and testing of information sources
Beyond simulations: Stratified randomization and regression adjustments for experimental data (draft available upon request)with Simon Heß (Goethe University)
Optimal state-dependent forecasting for Value-at-Risk under the Basel Accord (slides available upon request)
Software Package in R
PointFore (available on CRAN):
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 (2017) 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.
- 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".