Eric Zivot
Winter 1997
Department of Economics, Office: 310F Savory
University of Washington
Phone: 543-6715
Office Hours: MW 4-5
email: ezivot@u.washington.edu
This is survey course in time series econometrics with focus on applications in macroeconomics, international finance and finance. We will cover univariate and multivariate models of stationary and nonstationary time series in the time domain. The goals of the course are threefold: (1) develop a comprehensive set of tools and techniques for analyzing various forms of univariate and multivariate time series and for understanding the current literature in applied time series econometrics; (2) survey the current research topics in time series econometrics; (3) show how to use EVIEWS (MicroTSP for Windows) and GAUSS (matrix programming language) for empirical analyses.
The field of time series econometrics has exploded in the last decade and there is not enough time in a quarter course to comprehensively cover all of the important contributions. Consequently, we will often discuss and present results without formal proofs. All of the gory details, however, are supplied in the textbook by Hamilton and in the references on the reading list.
The topics we will cover include:
A good grasp of basic mathematical statistics and linear algebra is necessary for surviving the course. Some familiarity with real analysis and stochastic processes would make life easier for understanding the technical details but is not required. The mathematical appendix in Hamilton gives a very good summary of useful mathematical and statistical tools. For those with a strong interest in time series, I recommend studying graduate level probability (measure theoretic), statistics and stochastic processes in the statistics department.
A previous course in time series is not required or assumed. However, a basic knowledge of ARMA models and Box-Jenkins' methods will be helpful.
Credit for this course is obtained by
(1) completing weekly homework assignments (50%)
(2) completing a short empirical paper (50%) or completing a econometric programming project (50%) - to be explained in class.
For those interested in theory, I highly recommend that you do the homework problems at the end of each chapter in Hamilton (the answers are provided in the text). These problems will give you practice using the tools and techniques of time series econometrics.
I will distribute weekly homework assignments, which will be a combination of computer labs using EVIEWS and/or GAUSS and analytical problems. I will provide detailed instructions for using EVIEWS and GAUSS. EVIEWS is available on the CSSCR network and in the economics computer lab on the third floor of Savery Hall. GAUSS is also available on four PCs in the CSSCR lab,on the PCs in economics computer room and on the Mead UNIX cluster (limited to two simultaneous users) . Manuals for these programs are in CSSCR and the economics computer room (see also the GAUSS resources on my web page). We will go over the homework in the Friday computer labs.
The required textbooks are:
1. Time Series Analysis, by James D. Hamilton, Princeton University Press, 1994.
2. The Econometric Modelling of Financial Time Series, by Terence C. Mills, Cambridge University Press, 1993
3. Class Reader, available at the AVE copy center (next to the Big Time Brewery on University). All of the material on the reading list (below) is included in the class reader.
In addition, I will often hand out class notes and post lecture material on the class web page.
The book by Hamilton will be our main reference source. It is a rigorous, comprehensive yet very readable treatment of the most recent topics in time series econometrics. I will often refer to Hamilton for the technical details left out of the lecture material. The book by Mills is a nice survey text in time series econometrics with applications in finance and is a good background source for those with little background in time series analysis.
The class web page is located at
http://weber.u.washington.edu/~ezivot/econ584/econ584.htm
There will be a lot of material placed on the class web page including but not limited to: class announcements, lecture material, homework assignments, data, example EVIEWS and GAUSS programs, GAUSS resources (including the entire GAUSS help files), time series econometrics bibliography, working paper bibliography and links to time series econometrics topics elsewhere on the web. I am in process of creating an online applied econometrics textbook modeled around the computer labs in the course. Comments and suggestions are encouraged!
Note: H denotes "Hamilton".
M denotes "Mills".
* denotes "required reading".
Readings: *H, chapters 1 - 5.
*M, chapters 1 and 2.
Reading: *H, chapters 13, 15-17 and 22.
*M, chapters 3 and 4.
*Nelson, C.R. and C.I. Plosser (1982), "Trends and Random Walks in Macroeconomic Time Series: Some Evidence and Implications," Journal of Monetary Economics, 10, 139-162.
*Campbell, J.Y. and P. Perron (1991), "Pitfalls and Opportunities: What Macroeconomists Should Know About Unit Roots and Cointegration," NBER Macroeconomics Annual, Cambridge, MA: MIT Press.
Campbell, J. and G. Mankiw (1987), "Are Output Fluctuations Transitory," Quarterly Journal of Economics.
_____ (1987), "Permanent and Transitory Components in Macroeconomic Fluctuations," American Economic Review.
Clark. P.K. (1987), "The Cyclical Component of U.S. Economic Activity," Quarterly Journal of Economics.
Cochrane, J. (1988), "How Big is the Random Walk Component in GNP," Journal of Political Economy, No. 5.
Hamilton, J. (1994), "State Space Models," in Handbook of Econometrics, Vol 4, chapter 50.
Harvey, A.F. (1987), "Applications of the Kalman Filter in Econometrics," in T.F. Bewely ed. Advances in Econometrics, Fifth World Congress, Vol 1, Cambrigde University Press.
Kwiatkowski, D., P.C.B. Phillips, P. Schmidt and Y. Shin (1992), "Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root: How Sure are We that Economic Time Series Have a Unit Root," Journal of Econometrics 54, 159-78.
Lam, P. (1990), "The Hamilton Model with a General Autoregressive Component: Estimation and Comparison with Other Models of Economic Time Series," Journal of Monetary Economics 26, 409-32.
Perron, P. (1989), "The Great Crash, the Oil Price Shock and the Unit Root Hypothesis," Econometrica 57, 1361-401.
Stock, J.S. (1995), "Unit Roots and Trend Breaks", in Handbook of Econometrics, Vol 4.
Stock, J.S. and M.Watson (1988), "Variable Trends in Economic Time Series," Journal of Economic Perspectives, Vol 2, No. 3.
Zivot, E. and D.W.K. Andrews (1992), "Further Evidence on
the Great Crash, the Oil Price Shock and the Unit Root
Hypothesis," Journal of Business and Economic Statistics
10, 251-70.
Reading: *H, chapter 21.
*M, chapters 3 and 5 (section 2 only).
*Bera, A. and M.L. Higgins (1995), "On ARCH Models: Properties, Estimation and Testing," chapter 8 in Surveys in Econometrics, Basil Blackwell.
Bollerslev et. al. (1994) "ARCH Models," chapter 49 in the Handbook of Econometrics, Vol. 4.
Diebold, F.X. and J.A. Lopez (1995), "Modeling Volatility Dynamics," NBER Technical Working Paper No. 173.
Shephard, N. (1996), "Statistical Aspects of ARCH and Stochastic Volatility," in Times Series Models in Econometrics, Finance and Other Fields, D.R. Cox, D.V. Hinkley and O.E. Barndorff-Nielsen eds., Chapman & Hall.
Reading: *H, chapters 7 and 8.
*M, chapter 5 (sections 1-4 only).
*Den Haan, W.J. and A. Levin (1996), "A Practitioner's Guide to Robust Covariance Matrix Estimation," manuscript, UCSD and the Federal Reserve Board.
Reading: *H, chapter 10 and chapter 11, section 1.
*E, chapter 5, sections 4 - 9.
*Sims, C.A. (1980), "Macroeconomics and Reality," Econometrica, 48, 1-48.
*Darnell, A.C. and J.L. Evans (1990), The Limits of Econometrics, chapter 7 (Sims and Vector Autoregressions), Edward Elgar.
Lutkepohl, H. (1993), Introduction to Multiple Time Series Analysis, Second Edition, chapter 2.
Reading: *H, chapter 11, sections 2-7.
*Blanchard, O.J. and D. Quah (1989), "The Dynamic Effects of Aggregate Demand and Supply Disturbances," American Economic Review, 79, 655-673.
Bernanke, B. (1986), "Alternative Explanations of the Money-Income Correlation," Carnegie Rochester Conference Series on Public Policy, 25, 49-99.
Watson, M. (1995), "VARs and Cointegration" chapter 47 (section 4) in Handbook of Econometrics, Vol 4.
Readings: *H, chapters 18; chapter 19, section 1.
*M, chapter 6, sections 1-2.
Campbell, J.Y. and P. Perron (1991), "Pitfalls and Opportunities: What Macroeconomists Should Know About Unit Roots," NBER Macroeconomics Annual, Cambridge, MA: MIT Press.
Banerjee et al, Cointegration, Error Correction and the Econometric Analysis of Nonstationary Data, chapters 5, 7 and 8.
Watson, M. (1995), "VARs and Cointegration" chapter 47 (sections 1-3) in Handbook of Econometrics, Vol 4.
Readings: *H, chapter 19, sections 2-3; chapter 20.
*M, chapter 6, sections 3-6.
*Johansen, S. (1996), "Likelihood-based Inference for Cointegration of Some Nonstationary Time Series," chapter 2 in Times Series Models in Econometrics, Finance and Other Fields, D.R. Cox, D.V. Hinkley and O.E. Barndorff-Nielsen eds., Chapman & Hall.
_____ and S. K. Juselius (1992), "Testing Structural Hypotheses in a multivariate cointegration analysis of the PPP and the UIP for UK," Journal of Econometrics, 53, 211-244.
Juselius, K. and C.P. Hargreaves (1992), "Long-Run Relations in Australian Monetary Data," in C.P. Hargreaves (eds.), Economic Analysis of the Long-Run, Edward Elgar.
Watson, M. (1995), "VARs and Cointegration" chapter 47 (section 3) in Handbook of Econometrics, Vol 4.
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