Associate Professor Gregor Reich
- Gregor.Reich@nhh.no
- Telephone
- +47 55 95 92 08
- Department
- Strategy and Management
- Office
- E365
- Expertise
- Computational Economics Structural Econometrics Industrial Economics
bio
Gregor Reich is an Associate Professor at the Norwegian School of Economics (NHH).
He holds an MA in Economics, and a PhD in Business Administration, both from University of Zurich. After his PhD, he collected some industry experience, and then went for a PostDoc to the Hoover Institution (Stanford).
His research interests are the application of computational methods to estimation problems in business, finance, and economics. For a method to estimate models with missing data observations developed in his thesis, he won the 2015 Mercator Award for Junior Researchers.
Selected publications
| Author(s) | Title | Publisher |
|---|---|---|
| Reich, Gregor | Divide and Conquer: Recursive Likelihood Function Integration for Hidden Markov Models with Continuous Latent Variables | Operations Research Volume 66 (6); page 1457 - 1470; 2018 |
| Larsen, Bradley J; Oswald, Florian; Reich, Gregor; Wunderli, Dan | A Test of the Extreme Value Type I Assumption in the Bus Engine Replacement Model | Economics Letters Volume 116 (2); page 213 - 216; 2012 |
| Reich, Gregor | Market Self-organization under Limited Information | Lecture Notes in Computer Science (LNCS) Volume 7023; page 32 - 41; 2011 |
Working papers
- „Small Data“: Efficient Inference with Occasionally Observed States. (with Andreas Lanz, Philipp Müller; 2020; available on request)
- Efficient Likelihood Ratio Confidence Intervals using Constrained Optimization. (with Ken Judd; 2019; available at SSRN)
- Learning in Proof-of-Work Protocols—And Why It Does Not Matter (Yet). (with José Parra-Moyano and Karl Schmedders; 2020; updated version available on request)
- Structural Estimation Using Parametric Mathematical Programming with Equilibrium Constraints and Homotopy Path Continuation. (with Philipp Müller; 2019; available at SSRN)
- Adaptive Grids for the Estimation of Dynamic Models. (with Ole Wilms; 2017; updated version available on request)