Dr. Lukas Hoesch
www.lukashoesch.com | l.hoesch@vu.nl
Department of Econometrics and Data Science, Vrije Universiteit Amsterdam
Office 11A-83, De Boelelaan 1105, 1081HV Amsterdam, The Netherlands

EMPLOYMENT2021 - present

Assistant Professor – Vrije Universiteit Amsterdam
Department of Econometrics and Data Science• Candidate Fellow at the Tinbergen Institute Amsterdam since November 2021.
• Academic Visitor at the Department of Economics at Oxford University in theacademic year 2022/23 and 2023/24.

EDUCATION
2017 - 2021

Ph.D. in Economics
Universitat Pompeu Fabra, Spain
Programme in Economics, Finance and Management
Advisor: Prof. Barbara Rossi
Grade: Excellent with Cum Laude2016 - 2017

Master of Research in Economics, Finance and Management
Universitat Pompeu Fabra, Spain2015 - 2016

Master of Science in Economics and Finance
Barcelona Graduate School of Economics, Spain2014 - 2015

Visiting Student, Bachelor of Science in Economics
Stockholm University, Sweden

2012 - 2015

Bachelor of Science in Economics
University of Mannheim, Germany

RESEARCH FIELDSTime Series Econometrics, Forecasting, Machine Learning, and Macroeconometrics

PUBLICATIONS
Robust Inference in Structural VAR Models identified by Non-Gaussianitywith Adam Lee and Geert Mesters
Forthcoming, Quantitative Economics
Abstract: All parameters in structural vector autoregressive (SVAR) models are locally identified when the structural
shocks are independent and follow non-Gaussian distributions. Unfortunately, standard inference methods that exploit
such features of the data for identification fail to yield correct coverage for structural functions of the model parameters1when deviations from Gaussianity are small. To this extent, we propose a robust semi-parametric approach to conduct
hypothesis tests and construct confidence sets for structural functions in SVAR models. The methodology fully exploits
non-Gaussianity when it is present, but yields correct size / coverage regardless of the distance to the Gaussian
distribution. Empirically we revisit two macroeconomic SVAR studies where we document mixed results. For theoil price model of Kilian and Murphy (2012) we find that non-Gaussianity can robustly identify reasonable confidence
sets, whereas for the labour supply-demand model of Baumeister and Hamilton (2015) this is not the case. Moreover,these exercises highlight the importance of using weak identification robust methods to assess estimation uncertainty
when using non-Gaussianity for identification.

Has the Information Channel of Monetary Policy Disappeared? Revisiting the EmpiricalEvidence, with Barbara Rossi and Tatevik Sekhposyan
American Economic Journal: Macroeconomics, Vol. 15, No. 3, July 2023 (pp. 355-87)
Abstract: Does the Federal Reserve have an “information advantage” in forecasting macroeconomic variables beyond
what is known to private sector forecasters? And are market participants reacting only to monetary policy shocks oralso to information on the future state of the economy that the Federal Reserve communicates in its announcements
via an “information channel”? This paper investigates the evolution of both the information advantage and information
channel over time. Although they appear to be important historically, we find substantially weaker empirical evidenceof their presence in recent years once instabilities are accounted for.

RESEARCH IN PROGRESS
Specification Tests Robust to Multiple Instabilities
Preparing for submission.
Abstract: I develop a hypothesis test for model evaluation which is robust to time-variation in parameters. The proposed
method can be applied in-sample and out-of-sample to any economic model based on moment conditions. In-sample,
the test selects between two nested model specifications in the presence of parameter instabilities. Out-of-sample, thetest can be used to evaluate the performance of model or judgmental forecasts robust to time-variation. The key featureof the proposed test is that it is particularly powerful in the presence of multiple shifts in parameters without imposing
a specific form of time-variation. Further, the test statistic provides narrative evidence on which parts of the sample
drive the rejection of the null hypothesis. Simulations show that the test is accurately sized in finite samples and ismore powerful than tests assuming constant coefficients or a single break if the data-generating process exhibits multiple
shifts in parameters. Using the proposed test, I document the presence of short-horizon predictability in the U.S. equity
premium during the postwar period. I find evidence of predictability for a large set of variables once time- variation istaken into account. The test further provides evidence of heterogeneity in the location of predictability episodes acrossvariables. The findings explain why traditional tests often fail to uncover predictability in the full sample and why studies
that split the sample at different dates often arrive at conflicting results regarding the predictive ability of a wide class ofvariables. The paper has been awarded the 7th UniCredit Foundation Economics Job Market Best Paper Award.

Multi-period Growth-at-Risk Forecasting with Sequence-to-Sequence Neural Networks
with Julia Schaumburg and Sicco Kooiker
Research project in progress.

2TEACHING EXPERIENCE
2021 - presentCourse Coordinator & Lecturer, Vrije Universiteit Amsterdam• Data Structures and Algorithms, Course Coordinator & Lecturer
B.Sc. Econometrics and Operations Research• Numerical Methods, Course Coordinator & Lecturer
B.Sc. Econometrics and Operations Research• Data Science Project, Course Coordinator & Lecturer
B.Sc. Econometrics and Operations Research• Data Science, Lecturer
MBA in International Business, Executive Education• Data Science for Executives, Lecturer
Executive MBA: Leading with Purpose, Executive Eucation

2022 - 2023

Course Coordinator & Lecturer, Tinbergen Institute Amsterdam• Foundations of Machine Learning with Applications in Python
Summer School, Business Data Science Graduate Program

2016 - 2021

Teaching Assistant, Universitat Pompeu Fabra• Advanced Techniques in Macroeconomics II (Ph.D.-level course)• Advanced Macroeconomics• Econometrics I (taught in Spanish)• Econometrics II• Data Analysis (taught in Spanish)• Introduction to Game Theory• Competition Policy

2017 - 2020

Teaching Assistant, Barcelona Graduate School of Economics• Introduction to MATLAB (M.Sc. Econ, M.Sc. ITFD)• Brush-Up in Probability & Statistics (M.Sc. Econ)

PROFESSIONAL ACTIVITIESRefereeing

Journal of Applied Econometrics, Journal of Business & Economic Statistics,
International Journal of Forecasting, Econometrics and Statistics, Studies in
Nonlinear Dynamics & Econometrics, Macroeconomic Dynamics

Organization

Organizer of the Departmental Brownbag Seminar at Vrije Universiteit Amsterdam

3Presentations

University of Oxford (2023), 3rd Catalan Economic Society Conference (2023),
Nuffield College Oxford (2023), University of Cologne (2023), Tilburg University(2022), 42nd International Symposium on Forecasting (2022), 15th NESG Meeting(2022), 27th International Conference on Computing in Economics and Finance(2021), Vrije Universiteit Amsterdam (2021), CEMFI (2021), Queen Mary
University of London (2021), University of Mannheim (2021), University of Vienna(2021), Norges Bank (2021), Nova School of Business and Economics (2021),
European Winter Meetings of the Econometric Society (2020), Spanish Economic
Association SAEe Meeting (2020), 40th International Symposium on Forecasting(2020), Barcelona GSE PhD Jamboree (2020), SIdE Workshop for students inEconometrics and Empirical Economics (2020), 6th Barcelona GSE Summer Forum(2018)

IT SKILLS
Certifications• AWS Certified Solutions Architect - Associate (SAA-C03, February 2023)

CloudArchitecture• Experience developing (hybrid) cloud architectures for scientific applications.
• Experience with cloud application security (AWS Identity & Access Management,
Application Load Balancer, Organizations & Shield) and DevSecOps solutions(SonarQube, AWS CloudWatch & Secrets Manager).
• Experience with CI/CD pipelines (AWS Code Pipeline, Github Actions &Jenkins) and Infrastructure-as-code (AWS CloudFormation & Ansible).
• Experience with Content-Delivery-Networks (AWS CloudFront), serverlesscomputing (AWS Lambda) and edge computing (Lambda@Edge).
• Experience developing IoT solutions using AWS IoT including development of IoT
sensors (ESP32 with Embedded C & MicroPython).

SoftwareEngineering• 10+ years of software engineering experience using C#, C++, Golang, Python,Visual Basic, ASP.NET, PHP, HTML and JavaScript.
• Experience with SQL & NoSQL on-premise and cloud database solutions (MySQL,MS SQL, PostgreSQL, MongoDB, Prometheus, AWS RDS & DynamoDB).
• Experience with Virtual Machines (VMware Workstation, VirtualBox andParallels) and containerization (Docker, Kubernetes and AWS ECS/EKS).
• Experience with Agile software development (Scrum), DevOps and DevSecOps.

Data Analytics• Extensive experience developing and implementing algorithms for data analytics
using R, C++, Python, Julia, MATLAB and STATA.
• Experience developing KPI dashboards and interactive data visualizations(Grafana, Shiny & React).
• Experience developing and training machine-learning models (TensorFlow &Keras, PyTorch, Scikit-Learn, Jupyter Notebooks, AWS SageMaker).

Support Tools• Extensive experience with Office 365, including programming of VBA macros.
• Experience with version control (Git & Subversion), development environments(MS Visual Studio, VS Code) and database management software (MS Access).

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AWARDS & SCHOLARSHIPS
2020

7th Economics Job Market Best Paper Award by UniCredit Foundation and theEuropean Economic Association.

2020“Best Student Presentation Award in recognition of an inspiring, effective andprofessional Student Speaker at the 40th International Symposium on Forecasting”,
awarded by the International Institute of Forecasters and sponsored by Amazon.

2013 - 2017

Scholar of the German National Academic Foundation (Studienstiftung desdeutschen Volkes), Germany’s largest, and most prestigious scholarship foundation.

OTHER RESEARCH EXPERIENCE
2017 - 2021

Research Assistant, Universitat Pompeu Fabra, Spain2014 - 2015

Research Assistant, Mannheim University, Germany

2013 - 2014

Research Assistant, ZEW Mannheim, Germany

PERSONAL INFORMATION
Citizenship

GermanLanguages

German (native), English (fluent), Spanish (fluent),Dutch (intermediate), French (basic)

REFERENCES
Available upon request.

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