Time Series Methods in Financial Econometrics Econ 509 B1 - Winter 2017

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1 Time Series Methods in Financial Econometrics Econ 509 B1 - Winter 2017 Instructor: Sebastian Fossati Office: Tory sfossati@ualberta.ca Website: Office Hours: TBD (see course website) Lecture Tuesday and Thursday 11:00-12:20 in Tory B-104. Course Objectives This course will cover topics in time series econometrics with focus on applications in macroeconomics, international finance, and finance. The topics we will cover include: univariate stationary time series models; time series forecasting; state-space models and the Kalman filter; unit-root theory, trend-stationarity, testing and applications; multivariate time series models; co-integration and error-correction models. Course Prerequisites I will assume everyone has a good understanding of basic mathematical statistics, linear algebra, linear algebra based econometrics, and maximum likelihood. Previous knowledge of time series econometrics is not assumed. The mathematical appendix in Hamilton gives a good summary of useful mathematical and statistical tools. Grading The final grade will be based on homework assignments (5%), a short term paper (25%), a midterm exam (30%), and a final exam (40%). Grades reflect judgments of student achievement made by instructors. These judgments are based on a combination of absolute achievement and relative performance in a class. Special notes: - Homework assignments will be a combination of computer problems using R and analytical problems. Everyone must turn in their own homework, but collaboration is permitted. No late homework will be accepted. Solutions will follow after the assignments are handed in. - The term paper will be a short length (under 20 pages) replication paper. Detailed instructions will be distributed later. Due date: Friday April 14 at 11:59 am. - Midterm Exam: Thursday February 16 at 11:00 am (in class). - Final Exam: Wednesday April 19 at 9:00 am (school schedule). 1

2 Textbook Enders (2015) will be our main reference. This book offers an accessible introduction to time series econometrics with numerous real-world examples. Hamilton (1994) offers a rigorous and comprehensive treatment of topics in time series econometrics. The notes by Cochrane provide a nice summary of time series models with applications in macroeconomics finance and is a good background source for those with little background in time series analysis. - Enders, W. (2015), Applied Econometric Time Series, 4th Edition. Wiley. - Hamilton, J.D. (1994), Time Series Analysis. Princeton University Press. - Cochrane, J. (2005), Time Series for Macroeconomics and Finance. Available from Cochrane s website ( Additional Textbooks You may also find the following textbooks useful. - Elliott, G. and Timmermann, A. (2016), Economic Forecasting. Princeton University Press. - Harvey, A.C. (1989), Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. - Kim, C.J. and Nelson, C.R. (1999), State-Space Models with Regime Switching. MIT Press. - Durbin, J. and Koopman, S.J. (2001), Time Series Analysis by State Space Models. Oxford University Press. - Tsay, R.S. (2010), Analysis of Financial Time Series, 3rd Edition. Wiley. - Koop, G. (2003), Bayesian Econometrics. Wiley. Computing R is used extensively in the course. R is a free software environment for statistical computing and graphics ( The course website has links to some R manuals for beginners and the Use R! series of books, all available for free (links provided on the course website). These books will help you implement the techniques covered in class. - Zuur, A.F., Ieno, E.N., and Meesters, E. (2009), A Beginner s Guide to R. Springer. - Cowpertwait, P. and Metcalfe, A. (2009), Introductory Time Series with R. Springer. - Pfaff, B. (2008), Analysis of Integrated and Cointegrated Time Series with R. Springer. 2

3 Course Outline Note: E denotes Enders, H denotes Hamilton, C denotes Cochrane, CM denotes Cowpertwait and Metcalfe, and P denotes Pfaff. 1. Stationary Univariate Models, Estimation, and Model Selection - E, chapter 2 ( ); H, chapters 1-3; C, chapters 1-4, and 6; CM, chapters 1, 2, 4, and 6; P, chapter 1. - Ng, S., and Perron, P. (2005), A Note on the Selection of Time Series Models, Oxford Bulletin of Economics and Statistics, 67, State-Space Models, Forecasting, and Asymptotic Theory - E, chapter 2 (2.9, 2.10); H, chapters 4 (4.1, 4.2, 4.7, 4.8), 5 ( ), 7 (main result is Proposition 7.11), and 13 ( ); C, chapter 5; CM, chapter Diebold, F.X. (1998), The Past and Present of Macroeconomic Forecasting, Journal of Economic Perspectives, 12, Diebold, F.X., and Mariano, R.S. (1995), Comparing Predictive Accuracy, Journal of Business and Economic Statistics, 13, Elliott, G., and Timmermann, A. (2008), Economic Forecasting, Journal of Economic Perspectives, 46:1, Harvey, A. (2006), Forecasting with Unobserved Components Models, in G. Elliott, C. Granger and A. Timmermann (eds.) Handbook of Economic Forecasting. Amsterdam: North-Holland. - Koopman, S.J., and Ooms, M. (2010), Forecasting economic time series using unobserved components time series models, VU University Amsterdam, Department of Econometrics. - Diebold, F.X., Rudebusch, G.D., and Aruoba, S.B. (2006), The macroeconomy and the yield curve: a dynamic latent factor approach, Journal of Econometrics, 131(1-2), Aruoba, S.B., Diebold, F.X., and Scotti, C. (2009), Real-Time Measurement of Business Conditions, Journal of Business and Economic Statistics, 27(4), Aruoba, S.B., and Diebold, F.X. (2010), Real-Time Macroeconomic Monitoring: Real Activity, Inflation, and Interactions, American Economic Review, 100(2), Introduction to Univariate Nonstationary Time Series - E, chapter 4 (4.1, 4.2, 4.12); H, chapter 15; C, chapter 10; CM, chapter 7; P, chapter 3. - Beveridge, S., and Nelson, C.R. (1981), A New Approach to Decomposition of Economic Time Series into Permanent and Transitory Components with Particular Attention to Measurement of the Business Cycle, Journal of Monetary Economics, 7,

4 - 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, Stock, J.S., and Watson, M. (1988), Variable Trends in Economic Time Series, Journal of Economic Perspectives, 2, No. 3., Clark. P.K. (1987), The Cyclical Component of U.S. Economic Activity, Quarterly Journal of Economics, 102, Sinclair, T.M. (2009), The Relationships between Permanent and Transitory Movements in U.S. Output and the Unemployment Rate, Journal of Money, Credit and Banking, 41(2-3), Morley, J. (2002), A state-space approach to calculating the Beveridge-Nelson decomposition, Economics Letters, 75, Morley, J., Nelson, C.R., and Zivot, E. (2003), Why are Beveridge Nelson and Unobserved Components Decompositions of GDP so Different?, Review of Economics and Statistics, 85, No. 2, Asymptotics for Nonstationary Data and Unit Root Tests - E, chapter 4 ( ); H, chapter 17; P, chapter 5. - Campbell, J.Y., and Perron, P. (1991), Pitfalls and Opportunities: What Macroeconomists Should Know About Unit Roots, NBER Macroeconomics Annual, Cambridge, MA: MIT Press. - Phillips, P.C.B. (1987), Time Series Regression with a Unit Root, Econometrica, 55, Stock, J.H. (1991), Confidence Intervals for the Largest Autoregressive Root in U.S. Macroeconomic Time Series, Journal of Monetary Economics, 28, Stock, J.S. (1995), Unit Roots and Trend Breaks, in Handbook of Econometrics, Vol 4. - Ng, S., and Perron, P. (1995), Unit Root Tests in ARMA Models with Data- Dependent Methods for the Selection of the Truncation Lag, Journal of the American Statistical Association, 90, Elliot, G., Rothenberg, T.J., and Stock, J.H. (1996), Efficient Tests for an Autoregressive Unit Root, Econometrica, 64, Ng, S., and Perron, P. (2001), Lag Length Selection and the Construction of Unit Root Tests with Good Size and Power, Econometrica, 69, Perron, P. (1989), The Great Crash, the Oil Price Shock and the Unit Root Hypothesis, Econometrica, 57, Zivot, E., and Andrews, D.W.K. (1992), Further Evidence on the Great Crash, the Oil Price Shock and the Unit Root Hypothesis, Journal of Business and Economic Statistics 10,

5 - Perron, P., and Rodríguez, G. (2003), GLS Detrending, Efficient Unit Root Tests and Structural Change, Journal of Econometrics, 115, Introduction to Stationary VAR Models - E, chapter 5 ( ); H, chapters 10 and 11 ( ); C, chapter 7; P, chapter 2. - Sims, C.A. (1980), Macroeconomics and Reality, Econometrica, 48, Sims, C.A. (1992), Interpreting the Macroeconomic Time Series Facts: The Effects of Monetary Policy, European Economic Review, 36 (5), Stock, J.S., and Watson, M. (2001), Vector Autoregressions, Journal of Economic Perspectives, 15(4). - Fernandez-Villaverde, J., Rubio, J.F., Sargent, T.J., and Watson, M.W. (2007), A, B, C, (and D)s for Understanding VARs, American Economic Review. - Diebold, F.X., and Li, C. (2006), Forecasting the term structure of government bond yields, Journal of Econometrics, 130(2), Structural VAR Models - E, chapter 5 ( ); H, chapter 11 ( ); P, chapter 2. - Blanchard, O.J., and Quah, D. (1989), The Dynamic Effects of Aggregate Demand and Supply Disturbances, American Economic Review, 79, King, R.J., and Watson, M.W. (1997), Testing Long-Run Neutrality, Federal Reserve Bank of Richmond Economic Quarterly, vol. 83, pp Sarte, P.D.G. (1997), On the Identication of Structural Vector Autoregressions, Federal Reserve Bank of Richmond, Economic Quarterly, 83(3), Spurious Regression and Cointegration - E, chapter 6; H, chapters 18 and 19; C, chapter 11; P, chapters 4, 7, and 8. - Granger, C.W.J., and Newbold, P. (1974), Spurious regressions in econometrics, Journal of Econometrics, 2, McCallum, B.T. (2010), Is the Spurious Regression Problem Spurious, NBER Working Paper 15690, - Engle, R.F., and Granger, C.W.J. (1987), Co-integration and error-correction: Representation, estimation and testing, Econometrica, 55, Johansen, S. (1988), Statistical analysis of cointegration vectors, Journal of Economic Dynamics and Control, 12(2-3), Stock, J.S., and Watson, M. (1988), Variable Trends in Economic Time Series, Journal of Economic Perspectives, 2, No. 3., Watson, M. (1995), VARs and Cointegration, chapter 47 in Handbook of Econometrics, Vol. 4. 5

6 8. Modeling Volatility - E, chapter 3; H, chapter Zivot, E. (2008), Practical Issues in the Analysis of Univariate GARCH Models, Handbook of Financial Time Series, Diebold, F.X., and Lopez, J. (1995), Modeling Volatility Dynamics, NBER Technical Working Paper No Engle, R.F. (2001), GARCH 101: The Use of ARCH/GARCH Models in Applied Economics, Journal of Economic Perspectives, 15, Hansen, P.R., and Lunde, A. (2005), A forecast comparison of volatility models: does anything beat a GARCH(1,1)?, Journal of Applied Econometrics, 20, Notes Policy about course outlines can be found in 23.4(2) of the University Calendar. The University of Alberta is committed to the highest standards of academic integrity and honesty. Students are expected to be familiar with these standards regarding academic honesty and to uphold the policies of the University in this respect. Students are particularly urged to familiarize themselves with the provisions of the Code of Student Behaviour (online at and avoid any behaviour which could potentially result in suspicions of cheating, plagiarism, misrepresentation of facts and/or participation in an offence. Academic dishonesty is a serious offence and can result in suspension or expulsion from the University. 6