Time Series Analysis

Transcription

1 Time Series Analysis Professor Genie Baker University of Oregon Course description: This course introduces statistical methods appropriate when sample observations are not independent, but rather, are logically ordered. Coverage will begin with the traditional ARIMA (Box-Jenkins) approach to time series analysis, and proceed through dynamic modeling and regression approaches to recent developments such as cointegration analysis, error correction models, and vector autoregression. Heavy emphasis will be given to fundamental concepts and applied work. Prerequisites for the course include a solid understanding of the fundamentals of statistical inference, regression analysis, matrix algebra, and the general linear model. Course requirements and evaluation: There will be four homework assignments and an exam. Grades will be based on the top 3 homework scores and the exam score, with each receiving equal weight. Homework assignments will include statistical problems and computer assignments requiring the use of statistical software. SAS, Stata, and E-Views will be used for various applications to give students a sense of the comparative strengths and weakness of different packages. Required Texts: Patrick T. Brandt and John T. Williams (2006) Multiple Time Series Models. Sage. Walter Enders (2004) Applied Econometric Time Series, 2 nd Ed. NY: Wiley. Richard McCleary and Richard Hay (1980) Applied Time Series Analysis for the Social Sciences. Sage. Recommended texts: Jeff B. Cromwell, Walter C. Labys and Michel Terraza (1993) Univariate Tests for Time Series. Sage. Wojciech W. Charemza and Derek F. Deadman (1997) New Directions in Econometric Practice: General to Specific Modeling, Cointegration and Vector Autoregression. 2 nd Edition. Edward Elgar. **see end for other useful texts and articles

2 Course Sequence and (dates are approximate) Introduction (July 21) Why you re here Put another way, time series vs. cross-sectional data Review of generalized least squares Fixing error terms vs. fixing the specification Classical decomposition and smoothing. Reading Diebold, 1998 Cromwell et. al. (1993), ch. 1 Enders, pp. 1-6 Charemza and Deadman, ch. 1 Review of Generalized Least Squares in any good statistics text, if needed Downs and Rocke, 1979 PART I: The ARIMA (Box-Jenkins) Approach Univariate ARIMA models, impact assessment, and forecasting (July 22-28) Stationarity, trend and drift Autoregressive processes Moving average processes The autocorrelation and partial autocorrelation functions Identifying ARIMA models Estimating ARIMA models Forecasting Impact assessment Reading McCleary and Hay, ch. 1-4,6 Enders, ch. 4 to p. 225, ch. 2 (in that order) Cromwell et. al. ( 1993) Univariate Tests, pp Hamilton, pp (lag operators) Charemza, sections Transfer functions and intervention analysis (July 29, 30) The crosscorrelation function and identification Prewhitening Reading McCleary and Hay, ch. 5 Enders, ch. 5 through p. 261 Wood 1988

3 ARCH models (Jul. 31) Gujarati, pp Enders, Ch. 3 Engle 2004 PART II: Regression analysis of time series (August 1-7, exam Aug. 8) GLS, Comfac, and Autoregressive Distributed Lag models Review of GLS COMFAC The Koyck and Almon lag models Lagged Dependent Variables reexamined Newey-West Standard Errors Gujarati, Ch. 12, if needed Hibbs 1974 Hendry and Mizon 1978 Mizon 1995 Gujarati, Ch. 17 Rueda 2005 Beck 1985 Achen 2000 PART III: Recent Advances Time series dynamics Skepticism concerning traditional methodology Davidson, Hendry, Serba and Yeo as a turning point Testing for parameter constancy Charemza, Ch. 2-3, Section 4.1 Cointegration, Unit Roots and Error Correction Models (ECM) Cointegration Error Correction Models Murray 1994 Granger 2004

4 Charemza, sections Enders, ch. 6 through p. 366 Barabas 2004 EXAM APPROXIMATELY HERE Vector Autoregression (VAR) Review of Simultaneous Equations Modeling Principles of VAR VAR and causality, Granger causality VAR and cointegration Wonnacott and Wonnacott ch. 7-9, 17-20, if needed Brandt & Williams, pp. 1-32, (Intro to VAR, example) Brandt & Williams, pp (Criticisms of VAR) Brandt & Williams, pp , (Granger Causality) Brandt & Williams, pp , (Impulse Response Functions) Brandt & Williams, pp (Vector ECM) Enders, Ch. 5, pp. 264-end Charemza, Ch. 6 Sims 1980 Exogeneity, Encompassing and Model Selection Charemza, Ch. 7-8 Enders, pp Pagan 1987 Geweke 1984 Leamer 1983 PART III: Special Topics (if time permits) Pooled Cross-Section and Time-Series Models Stimson 1985 Beck and Katz 1995 Wawro 2002

5 Other useful texts: A. Banerjee, J.J. Dolado, J.W. Galbraith and David F. Hendry (1993) Co-integration, Error-Correction and the Econometric Analysis of Non-Stationary Data. Oxford: Oxford University Press. G. Box and G. Jenkins (1984) Time Series Analysis: Forecasting and Control, 2 nd ed. San Francisco: Holden Day. C. Chatfield (1984) The Analysis of Time Series: An Introduction, 3 rd ed. London: Chapman and Hall. Jeff B. Cromwell, Michael J Hannan, Walter C. Labys and Michel Terraza (1994) Multivariate Tests for Time Series Models. Thousand Oaks, CA: Sage. K. Cuthbertson et. al. (1992) Applied Econometric Techniques. Ann Arbor: University of Michigan Press. R. Davidson and J. MacKinnon (1993) Estimation and Inference in Econometrics. New York: Oxford University Press. Robert F. Engle, ed. (1995) ARCH: Selected. Oxford: Oxford University Press. Robert F. Engle and C. W. J. Granger, eds. (1991) Long-Run Economic Relationships: in Cointegration. Cambridge: Cambridge University Press. W. A. Fuller (1977) Introduction to Statistical Time Series. NY: Wiley. L. G. Godfrey (1991) Misspecification Tests in Econometrics. Cambridge: Cambridge University Press. Samuel Goldberg (1958) Introduction to Difference Equations. NY: Dover Publications. Arthur Goldberger (1992) A Course in Econometrics. Cambridge, Mass.: Harvard University Press. John M.Gottman (1981) Time Series Analysis: A Comprehensive Introduction for Social Scientists. Cambridge: Cambridge University Press. C.W. Granger, ed. (1990) Modelling Economic Series: Advanced Texts in Econometrics. Oxford: Oxford University Press. C.W. Granger and Paul Newbold (1986) Forecasting Economic Time Series, 2 nd ed. San Diego: Academic Press.

6 William Greene (2002) Econometric Analysis, 5 th ed. Upper Saddle River, NJ: Prentice- Hall. Damodar N. Gujarati (2003) Basic Econometrics, 4 th ed. NY: McGraw-Hill. James D. Hamilton (1994) Time Series Analysis. Princeton: Princeton University Press. A. C. Harvey (1990) The Econometric Analysis of Time Series. Cambridge, Mass.: MIT Press. A. C. Harvey (1993) Time Series Models. Cambridge, Mass.: MIT Press. David F. Hendry (1993) Econometrics: Alchemy or Science? Oxford: Blackwell Publishers. Jack Johnston and John DiNardo (1997) Econometric Methods, 4 th ed. NY: McGraw- Hill. Peter Kennedy (1998) A Guide to Econometrics, 4 th ed. Cambridge, Mass.: MIT Press. Edward Leamer (1978) Specification Searches. NY: Wiley. G. S. Maddala (1992) Introduction to Econometrics. NY: Macmillan. Terence C. Mills (1990) Time Series Techniques for Economists. Cambridge University Press. Aris Spanos (1986) Statistical Foundations of Econometric Modelling. Cambridge: Cambridge University Press. Ronald J Wonnacott and Thomas H. Wonnacott (1979) Econometrics, 2 nd ed. NY: Wiley. Robert A. Yaffee and Monnie McGee (2000) An Introduction to Time Series Analysis and Forecasting: with Applications of SAS and SPSS. Academic Press. Articles Christopher H. Achen (2000) Why Lagged Dependent Variables Can Suppress the Explanatory Power of Other Independent Variables. Paper presented at the Annual Meeting of the Political Methodology Section of the American Political Science Association, UCLA, July Jason Barabas (2006) Rational Exuberance: The Stock Market and Public Support for Social Security Privatization. Journal of Politics.

7 Nathaniel Beck and Jonathan Katz (1995) What To Do (and Not to Do) with Timeseries Cross-Section Data in Comparative Politics. American Political Science Review 3, 89 (September): Nathaniel Beck (1991) Comparing Dynamic Specifications: The Case of Presidential Approval. Political Analysis 3: Nathaniel Beck (1985) Estimating Dynamic Models is Not Merely a Matter of Technique. Political Methodology 11: S. Beveridge and C. Nelson (1981) A New Approach to Decomposition of Economic Time Series in Permanent and Transitory Components with Particular Attention to the Measurement of the Business Cycle. Journal of Monetary Economics 7: G. Box and D. Cox (1964) An Analysis of Transformations. Journal of the Royal Statistical Society, Series B: Janet M. Box-Steffensmeier. and Tse-Min Lin (1995) A Dynamic Model of Campaign Spending in Congressional Campaigns. Political Analysis 6. Patrick T. Brandt and John R. Freeman (2006) Advances in Bayesian Time Series Modeling and the Study of Politics: Theory Testing, Forecasting and Policy Analysis. Policy Analysis 14(1): Patrick T. Brandt, John Williams, Benjamin Fordham, and Brian Pollins (2000) Dynamic Modeling for Persistent Event Count Time Series AJPS October: Patrick T. Brandt and John Williams (2001) A Linear Poisson Autoregressive Model: The Poisson AR(p) Model. Political Analysis 9(2): James E. H. Davidson, David F. Hendry, Frank Srba and Stephen Yeo (1978) Econometric Modelling of the Aggregate Time-Series Relationship Between Consumers Expenditure and Income in the United Kingdom. Economic Journal 88: Suzanna DeBoeuf and Luke J. Keele (2008) Taking Time Seriously. American Journal of Political Science 52(1): D. Dickey and W. Fuller (1979) Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association 74: F. Diebold (1998) The Past, Present and Future of Macroeconomic Forecasting. Journal of Economic Perspectives 12, 2:

8 George Downs and David Rocke (1979) Interpreting Heteroscedasticity. American Journal of Political Science 23,4: Robert Durr (1993) An Essay on Cointegration and Error Correction Models Political Analysis 4: Robert Engle (2004) Risk and Volatility: Econometric Models and Financial Practice. American Economic Review, 94, 3: Engle, R.F. and Clive W.J. Granger (1987) Cointegration and Error Correction: Representation, Estimation, and Testing. Econometrica 55: Robert Engle, David F. Hendry and Jean-Francois Richard (1983) Exogeneity. Econometrica 51: John Freeman (1983) Granger Causality and the Time Series Analysis of Political Relationships. American Journal of Political Science 27, 2: John Freeman, Tse-min Lin and John Williams (1989) Vector Autoregression and the Study of Politics. American Journal of Political Science 33: John Freeman, D. Houser, Paul Kellstedt and John Williams (1998) Long-Memoried Processes, Unit Roots and Causal Inference in Political Science. American Journal of Political Science 42: Milton Friedman and Anna Schwartz (1991) Alternative Approaches to Analyzing Economic Data. American Economic Review 81,1: J. Geweke (1984) Inference and Causality in Economic Time Series Models. In Zvi Griliches and Michael D. Intriligator (eds.), Handbook of Econometrics, Vol II, pp Amsterdam: North Holland. Joshua Goldstein and Jon Pevehouse (1997) Reciprocity, Bullying, and International Cooperation: Time-series Analysis of the Bosnian Conflict. American Political Science Review 91 (3): Jim Granato (1991) An Agenda for Econometric Model Building. Political Analysis 3: C.W.J. Granger (2004) Time Series Analysis, Cointegration, and Applications. American Economic Review, 94,3: C.W.J. Granger and P. Newbold (1974) Spurious Regressions in Econometrics. Journal of Econometrics 2: Donald Green, B. Palmquist, and E. Schickler (1988) Macropartisanship: A Replication and Critique. American Political Science Review 92(4):

9 David Hendry and G. Mizon (1978) Serial Correlation as a Convenient Simplification, Not a Nuisance: A Comment on a Study of the Demand for Money by the Bank of England. Economic Journal 88: Douglas Hibbs (1974) Problems of Statistical Estimation and Causal Inference in Time- Series Regression Models. Sociological Methodology, Douglas Hibbs (1977) Political Parties and Macroeconomic Policy. American Political Science Review 71: Luke J. Keele and Nathan J. Kelly (2006) Dynamic Models for Dynamic Theories: The Ins and Outs of LDVs. Political Analysis 14(2): Edward E. Leamer (1983) Let s Take the Con out of Econometrics. American Economic Review 73, 1: R. Li (1976) A Dynamic Comparative Analysis of Presidential and House Elections. American Journal of Political Science 20, 4: Michael McKuen, Robert Erickson and James Stimson (1989) Macropartisanship. American Political Science Review 83: Grayham Mizon (1995) A Simple Message for Autocorrelation Correctors: Don t. Journal of Econometrics 69, 1: Michael P. Murray (1994) A Drunk and her Dog: An Illustration of Cointegration and Error Correction. The American Statistician 48, 1 (Feb): T. Naylor, S. Seaks and D. Wichern (1972) Box-Jenkins Methods: An Alternative to Econometric Models. International Statistical Review 40,2: Charles Ostrom and Renee Smith (1992) Error Correction, Attitude Persistence and Executive Rewards and Punishments: A Behavioral Theory of Presidential Approval. Political Analysis 4: Adrian Pagan (1987) Three Econometric Methodologies: A Critical Appraisal. Journal of Economic Surveys 1: David Rueda (2005) Insider Outsider Politics in Industrialized Democracies: The Challenge to Social Democratic Parties. American Political Science Review 99,1: Christopher Sims (1980) Macroeconomics and Reality. Econometrica 48: 1-48.

10 Christopher A. Sims and T. Zha (1998) Bayesian Methods for Dynamic Multivariate Models International Economic Review Christopher A. Sims and T. Zha (1999) Error Bands for Impulse Responses Econometrica 67(5): James A. Stimson (1985) Regression in Space and Time: A Statistical Essay. American Journal of Political Science 29, 4: W. Thurman and M. Fisher (1988) Chickens, Eggs, and Causality, or Which Came First? American Journal of Agricultural Economics Gregory J. Wawro (2002) Estimating Dynamic Panel Models in Political Science. Political Analysis 10: John Williams (1992) What Goes Around Comes Around: Unit Root Tests and Cointegration. Political Analysis 4: B. Dan Wood (1988) Bureaucrats, Principals, and Responsiveness in Clean Air Enforcements. American Political Science Review 82: G. Udny Yule (1926) Why Do We Sometimes Get Nonsense Correlations between Time-Series? Journal of the Royal Statistical Society, Series A, 89: 1-69.