Prof. Dr. Daniel Streitz

Examination via a written at-home product (”term paper”). The term paper comprises two parts: i) a conceptual part in which the students explain concepts discussed in the lectures, and ii) an empirical exercise. In the empirical exercise the students perform their own data analyses in R or STATA, explain their findings, and relate them to concepts discussed in the lecture. Both parts are handed in together with the code as one written product (the term paper) that forms the basis for the final grade. The examination is offered once a year at the end of the summer term.

English

684 M.Sc. Economics: Wahlpflichtmodul

Course (2h per week), Exercises (2h per week)

Topics include, but are not limited to:

• theoretical and empirical evidence on the effects of financial intermediation on the real economy
• the role of financial intermediation in the ”finance-growth nexus”
• effects of credit supply on firm investment and employment decisions (theory and evidence)
• effects of credit constraints on households
• effects of financial innovation (e.g., securitization) on bank business models and the economy
• (mis-) allocation of credit
• conducting empirical analyses on real effects of financial intermediation and interpretation of results

Students

• know basic facts about the functioning of banks’ business models and the changes the banking sector has undergone in the last decades (towards an “originate to distribute model”).
• understand the basic mechanisms how shocks to the banking sector can transmit to firms and households.
• can critically reflect on and contrast and compare different empirical studies on the effects of the banking sector on the economy.
• understand and can explain the challenges associated with empirically examining the effects of financial intermediation on the real economy (e.g., understand the difference between correlation and causation and different tools used in the empirical banking literature to estimate causal effects).
• can replicate (simple) empirical studies in R and are able to interpret regression results.