Target audience and registration

The course is only available for five students each semester of the Masters program in Economics. 6 Credits (PO 2012 and PO 2015) can be obtained. Assignments are based on the "first-come, first-serve" principle. The registration at the examination office has to be done before the early-exams-deadline.

Course

Each student has to conduct an empirical study and write a paper of appr. 20 pages.

Topic

The chair  focuses on topics concerning financial markets, commodities and monetary economics. The chosen topic should cover one of these areas. Personal preferences and ideas are always welcomed and considered. Basic knowledge in econometrics and empirical research is mandatory. Knowledge in Excel and econometric software is beneficial.

Master-thesis

Once the project course is successfully completed, the empirical results can serve as the basis for the Master-thesis.

Contact person

Please contact directly the tutor which offers topics concerning your interest.
 

Topic-suggestions
 

Tutor: Prof. Dr. Martin T. Bohl

1. Monetary Policy: Quantifying Monetary Shocks

• Ramey, V.A. (2016), „Macroeconomic Shocks and Their Propagation”, in: Handbook of  Macroeconomics, Vol. 2A, pp. 71-162.
• Nakamura, E. & Steinsson, J. (2018), “High-Frequency Identification of Monetary Non-Neutrality: The Information Effect”, in: Quarterly Journal of Economics, Vol. 133 (3), pp. 1283-1330.
• Altavilla, C., Brugnolini, L. ,Gürkaynak, R.S. , Matto, R. & Ragusa, G. (2019), “Measuring euro area monetary policy”, in: Journal of Monetary Economics, Vol. 108, pp. 162-179.

2. Unstructured Data in Monetary Policy and Financial Analysis

• Loughran, T. & McDonald, B. (2011), “When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks”, in: The Journal of Finance, Vol. 66 (1), pp. 35-65.
• Shapiro, A.H. & Wilson, D.J. (2022), “Taking the Fed at its Word: A New Approach to Estimating Central Bank Objectives using Text Analysis”, in: Review of Economic Studies, Vol. 89 (5), pp. 1-38.
• Apel, M., Blix Grimaldi, M., & Hull, I. (2022): “How Much Information Do Monetary Policy Committees Disclose? Evidence from the FOMC’s Minutes and Transcripts” in: Journal of Money, Credit, and Banking, Vol. 54 (5), pp. 1460-1490.

3. Economics of Financial Crises and Credit Cycles

• Schularick, M. & Taylor, A.M. (2012), “Credit Booms Gone Bust: Monetary Policy, Leverage Cycles, and Financial Crises, 1870-2008”, in: American Economic Review, Vol. 102 (2), pp. 1029-1061.
• Baron, M., E. Verner & Xiong, W. (2021), “Banking Crises Without Panics", in: Quarterly Journal of Economics, Vol. 136 (1), pp. 51-113.
• Sufi, A. & Taylor, A.M. (2022), “Financial Crises: A Survey”, in: Handbook of International Economics: International Macroeconomics, Vol. 6, pp. 291-340.

Tutor: Niklas Humann

4. Macroeconomic Forecasting using Machine Learning

• Petropoulos, F. et al. (2022), „Forecasting: Theory and Practice”, in: International Journal of Forecasting, Vol. 38, pp. 705-871.
• Coulombe, P.G., Leroux, M., Stevanovic, D. & Surprenant, S. (2022), “How is Machine Learning Useful for Macroeconomic Forecasting?”, in: Journal of Applied Econometrics, Vol. 37(5), pp. 920-964.
• McCracken, M. & Ng, S. (2016), „FRED-MD: A Monthly Database for Macroeconomic Research”, in: Journal of Business & Economic Statistics, Vol. 34 (4), pp. 574-589.
• Elliott, G. & Timmermann, A. (2016), „Economic Forecasting”, Princeton University Press.

5. Systemic Risk and the Connectedness of Financial Markets

• Benoit, S., Colliard, J.E., Hurlin, C., Pérignon, C. (2017), „Where the Risks Lie: A Survey on Systemic Risk”, in: Review of Finance, pp. 109-152.
• Baruník, J. & Krehlík, T. (2018), „Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk”, in: Journal of Financial Econometrics, Vol. 16 (2), pp. 271-296.
• Adekoya, O.B. & Oliyide, J.A. (2021), „How Covid-19 Drives Connectedness Among Commodity and Financial Markets: Evidence from TVP-VAR and Causality-in-Quantiles Techniques”, in: Resources Policy, Vol. 70, Article 101898.

6. Monetary Policy and Commodity Markets

• Bernanke, B. & Blanchard, O. (forthcoming), „What Caused the U.S. Pandemic-Era Inflation?”, in: American Economic Journal: Macroeconomics.
• Drechsel, T. & Tenreyro, S. (2018), „Commodity booms and busts in emerging economies“, in: Journal of International Economics, Vol. 112 (C), pp. 200-218.
• Juvenal, L. & Petrella, I. (2024), „Unveiling the dance of commodity prices and the global financial cycle”, in: Journal of International Economics, Vol. 150, Article 103913.
• Miranda-Pinto, J., Pescatori, A., Prifti, E. & Verduzco-Bustos, G. (2023), „Monetary Policy Transmission through Commodity Prices”, IMF Working Paper No. WP/23/215.

Tutor: Lars Kranzmann

7.  Understanding Central Banks through Text Analysis

• Gentzkow, M., Kelly, B. & Taddy, M. (2019), “Text as Data.”, in: Journal of Economic Literature, Vol. 66 (3), pp. 535-574.
• Ferrara, F.M. & Angino, S. (2022), “Does clarity make central banks more engaging? Lessons from ECB communications”, in: European journal of Political Economy, Vol. 74, Article 102146.
• Apel, M., Blix Grimaldi, M. & Hull, I. (2022), “How Much Information Do Monetary Policy Committees Disclose? Evidence from the FOMC’s Minutes and Transcripts” in: Journal of Money, Credit, and Banking, Vol. 54 (5), pp. 1460-1490.

8. Construction of a Financial Stress Index

• Cardarelli, R., Elekdag, S. & Lall, S. (2011), “Financial stress and economic contractions”, in: Journal of Financial Stability, Vol. 7, pp. 78-97.
• Hakkio, C.S. & Keeton, W.R. (2009), “Financial Stress: What Is It, How Can It Be Measured, and Why Does It Matter?” in: Economic Review, Federal Reserve Bank of Kansas City (Second Quarter), pp. 5-50.
• Vermeulen, R., Hoeberichts, M., Vašíček, B., Zigraiova, D, Smidkova, K. & de Haan, J. (2015), “Financial Stress Indices and Financial Crises”, in: Open Economic Review, Vol. 26, pp. 383-406.