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Instructors: Prof. Dr. Bernd Lucke
Event type:
Interactive class
Displayed in timetable as:
Hours per week:
3
Credits:
6,0
Language of instruction:
English
Min. | Max. participants:
- | 45
Comments/contents:
This course provides an introduction to empirical business cycle analysis using descriptive and structural models from time series econometrics. No prior knowledge of time series econometrics is required, but students should be familiar with basic econometrics. The course surveys the development of business cycles theory in recent decades and shows how theories can be tested empirically using modern time series methods. The lecture focuses on the application of methods, it does not cover the underlying estimation theory. In class, we will replicate the results of severeal seminal papers. In the interactive part of the lecture, students are asked to double-check previous results in the literature in the light of newly available data.
Learning objectives:
In the field of macroeconomic theory, you will learn the main differences between various business cycle theories from a practical point of view. In the field of economics, you will understand fundamental principles and techniques in time series analysis and learn to independently apply them in computational analysis.
Didactic concept:
In scientific business cycle analysis, theoretical concepts are commonly directly tested with empirical data. We will discuss some of the most important publications of the last decades and reproduce their findings using computer-supported analysis. As part of the exercises, you will go beyond reproduction and test old theories using new data sets. In the lecture, based on the selected literature, we will discuss how new perceptions on business cycles made new methods in time series analysis necessary and how, vice versa, innovations in the analysis of time series led to new findings that stimulated business cycle theory.
Literature:
Beaudry, P., and Lucke, B., (2009): Letting Different Views about Business Cycles Compete, NBER Macroeconomics Annual 2009, Volume 24, pages 413-455, National Bureau of Economic Research.
Beaudry, P., and Portier, F., (2004): An Exploration into Pigou’s Theory of Cycles, Journal of Monetary Economics 51, pp. 1183-1216.
Beaudry, P., and Portier, F., (2006): Stock Prices, News and Economic Fluctuations, American Economic Review, Vol. 96, pp. 1293-1307
Blanchard, O., and Quah, D., (1989): The Dynamic Effects of Aggregate Demand and Aggregate Supply Disturbances, American Economic Review 79, pp. 655-673.
Campbell, J. Y., and Mankiw, N. G., (1990): Permanent Income, Current Income, and Consumption, Journal of Business and Economic Statistics 8, pp. 265-279.
Christiano, L. J., Eichenbaum, M., and Vigfusson, R., (2003): What Happens after a Technology Shock?, NBER Working Paper 9819.
Fisher, J. D. M., (2003): The Technology Shocks Matter, Federal Reserve Bank of Chicago WP 2002-14, December 2003.
Fisher, J. D. M., (2006): The Dynamic Effects of Neutral and Investment-Specific Technology Shocks, Journal of Political Economy 114, pp. 413-451.
Friedman, Milton, (1957): A Theory of the Consumption Function, Princeton University Press, 1957.
Galí, J., (1999): Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?, American Economic Review 89, pp. 249-271.
Greenwood, J., Hercovitz, Z., Krusell, P. (1997): Long-Run Implications of Investment-Specific Technological Change, American Economic Review, Vol. 87, No. 3, pp. 342-362.
Hall, R. E., (1978): Stochastic Implications of the Life Cycle-Permanent Income Hypothesis: Theory and Evidence, Journal of Political Economy 86, pp.971-987.
King, R., Plosser, C., Rebelo, S. (1988): Production, Growth, and Business Cycles, Journal of Monetary Economics 21,
I. The Basic Neoclassical Model, pp. 195-232,
II. New Directions, pp. 309-341.
King, R., Plosser, C. I., Stock, J., Watson, M. (1991): Stochastic Trends and Economic Fluctuations, American Economic Review 81, pp. 819-840.
Kirchgässner, G., Wolters, J., (2005): Einführung in die moderne Zeitreihenanalyse, Verlag Vahlen, München.
Lütkepohl, Helmut (2006): New Introduction to Multiple Time Series Analysis, Springer Verlag, Berlin
Nelson, C. R., and Plosser, C. I., (1982): Trends and Random Walks in Macroeconomic Time Series: Some Evidence and Implications, Journal of Monetary Economics 10, pp. 139-162.
Additional examination information:
Students will have to submit student contributions. Timely submission of student contributions is a necessary condition for being admitted to the final exam.
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