B. Dan Wood
Political Science 607.600
Dynamic Analysis with Quantitative Methods
Spring Semester, 2016
Location: 2064 Allen Building
Office: 2098 Allen Building
Office Hours: 1:30-2:00 Wednesday
Purpose- This course considers statistical techniques to evaluate social processes occurring through time. The early focus is on econometric regression methods, followed by a Box-Jenkins perspective, with later attention given to approaches such as Error Correction models, multivariate time series (VAR), and State Space modeling. We will also look at time varying parameter models including ARCH and regime switching. The emphasis throughout the course will be on application, rather than on statistical theory. However, the focus of most lectures will be statistical theory.
It is assumed that you bring to the course a background in high school level algebra and statistics up to and including regression. You should also have some experience with microcomputers. Primary statistical packages for the class will be RATS (WINRATS; MACRATS), STATA, and R. However, you need not be proficient in these before entering the course since sample programs will be provided.
Grades- The course grade will be determined by your performance on the homework assignments and class presentations (33%), a research project in which you apply one or more of the tools presented in this course (33%), and a final examination (33%). Homework is due the next week after the assignment on the following course outline. I will call on each of you at some point to discuss aspects of the homework assignment. The research project is due via email on Wednesday, May 4. Late homework or research projects will not be accepted. The final examination will be take-home, sent out electronically on Wednesday May 4 and due back electronically on Wednesday May 11.
Janet, John Freeman, Matthew Hitt, and John Pevehouse. Time Series Analysis for the Social Sciences (TSASS). Cambridge University Press.
---Enders, Walter. Applied Econometric Time Series, Fourth Edition (AETS4). Wiley.
---Shumway, Robert H. and David S. Stoffer. Time Series and Its Applications: with R Examples, Third Edition. Springer (Available Free Online)
---Pfaff, Bernard. Analysis of Integrated and Cointegrated Time Series, with R. Second Edition. Springer. (Available Free Online)
---Enders, Walter and Thomas A. Doan. RATS Programming Manual (AETS4 PM). Wiley. (Available Free Online)
---Enders, Walter. Applied Econometric Time Series Student Manual (AETS4 SM). Wiley. (Available Free Online)
---Doan, Thomas A. RATS Introduction, Version 9.
---Doan, Thomas A. RATS User’s Guide, Version 9.
---Doan, Thomas A. RATS Reference Manual, Version 9. Evanston, IL: Estima.
Materials will also be drawn from McCleary, Richard and Richard Hay. 1980. Applied Time Series Analysis. Sage Publications. This is out of print. I will supply a pdf copy.
Selected illustrative articles as recommended in the attached course outline.
Course resources, data, and example programs
are located here.
The Americans with Disabilities Act (ADA) is a federal anti-discrimination statute that provides comprehensive civil rights protection for persons with disabilities. Among other things, this legislation requires that all students with disabilities be guaranteed a learning environment that provides for reasonable accommodation of their disabilities. If you believe you have a disability requiring an accommodation, please contact Disability Services, currently located in the Disability Services building at the Student Services at White Creek complex on west campus or call 979-845-1637. For additional information, visit http://disability.tamu.edu.
n=recommended, but not required
1. Introductory overview: administrative matters primarily. During this session we will be positioning Time Series Analysis within the larger framework of statistical analysis and research design. Additionally, we will be introducing the computer software for the course.
r--. Read RATS Introduction, chapter 1; RATS Users Guide,
chapters 1 through 3.
r--. Read TSASS chapter 1.
r--. Read AETS4, chapter 1
r--. Read Shumway, chapter 1.
r--. Read Greene, William H. 2012. Econometric Analysis:7th Edition. Chapter 20.
a--. Do RATS Programming Manual (PM), chapter 1, using RATS.
a--. Do AETS4, replication example from chapter 1 using RATS.
a--. Replicate the examples in TSASS, chapter 1, using STATA.
a--. Do R-code examples in Shumway, chapter 1.
Be prepared to discuss the materials.
2. A survey of elementary regression time series methods and autocorrelation. (Note: The following readings should be completed over the next three weeks.)
r-- Read AETS4, Chapters 2 and 4.
r--Read TSASS Chapter 2 and the Appendix
r--Read Shumway, chapters 2 and 3, chapter 5, pp. 277-279.
r-- Read Pfaff, chapters 1, 3, 5, and 6.
r—Look at McCleary and Hay, Chapters 1&2, pp. 17-139 for a simplified treatment.
r-- Read Granger, C.W. J. and P. Newbold. 1974. Spurious Regressions in Econometrics. Journal of Econometrics. 2: 111-120.
r--. Read Greene, William H. 2012. Econometric Analysis:7th Edition. Chapter 21.
n-- Chatfield, C. 1979. Inverse Autocorrelations. Journal of the Royal Statistical Society. 142: 363-377.
n-- Schwartz, G. 1978.
n-- Akaike, H. 1974. A New Look at Model Identification. IEEE Transaction On Automatic Control. AC-19: 716-723.
a –. Do RATS Programming Manual (PM), chapter 2, using RATS.
a –. Do replication examples in AETS4, chapter 2, using RATS.
a –. Do replication examples in TSASS, chapter 3 using STATA.
a --. Do R-code examples in Shumway, chapter 2.
3. Time Series methods: introduction to ARIMA models with a focus on identification and stationarity issues.
a –. Do AETS4 SM, chapters 2 and 4, sections pertaining to RATS.
a –. Do replication examples in AETS4, chapter 4 using RATS.
a –. Do replication examples in TSASS, chapter 2 using STATA.
a--. Do R-code examples in Shumway, chapter 3.
a--. Do R-code examples in Pfaff, chapters 1, 3, and 5.
a--. Now, work independently of the texts using RATS, STATA, and R to graph the Mine Injuries and IBM B time series from the McCleary and Hay book. Use RATS, STATA, and R to identify the different ARIMA components of these series using the autocorrelation function and its associated tools. Make sure that you do appropriate transformations of these series to assure level and variance stationarity. In RATS use @URADF, @PPUNIT, @BAYESTST, @KPSS, @ERSTEST, and @VRATIO to do formal hypothesis tests for the level stationarity of these and the transformed series. Also, visit
4. Building univariate noise models: estimation, diagnosis, and metadiagnosis.
A --. Use RATS, STATA, or R to identify and estimate a univariate ARIMA model for the Sutter County Workforce and Boston Armed Robbery time series from McCleary and Hay. Replicate (if possible) the results reported by McCleary and Hay for these two series on pp. 104-121. Offer a written critique of the models they report there.
5. Building univariate noise models: estimation, diagnosis, and metadiagnosis (continued).
A --. Use RATS, STATA, or R to identify and estimate a univariate ARIMA model for the Swedish Harvest Index and Hyde Park Purse Snatchings series from McCleary and Hay. Replicate (if possible) the results reported by McCleary and Hay for these two series on pp. 121-132. Offer a written critique of the models they report there.
6. Spectral Analysis of Time Series
r --. Read Shumway, Chapter 7.
r --. Read Beck, Nathaniel. 1991. The Illusion of Cycles in International Relations. International Studies Quarterly. 35: 455-76.
r --. Wood, B. Dan. 2012. Presidential Saber Rattling: Causes and Consequences. pp. 64-67.
a --. Use Rats, STATA, or R to find the cyclical components associated with presidential saber rattling time series reported in Wood 2012. The data are in the saberrattling.rat or saberrattling.dta file in your data download directory. I will provide the reading.
7. ARIMA impact assessment: transfer function approaches. (Read the following over the next two weeks.)
r --. Read McCleary and Hay,
Chapters 3 and 4, pp. 141-228.
r --. Read AETS4, Chapter 5, pp. 261-68.
r --. Read Box-Steffensmeier et al, pp. 58-65.
r --. Read Wood, B. Dan. 1988. Principals, Bureaucrats, and Responsiveness. American Political Science Review. 82: 213-234.
r --. Read Wood, B. Dan and Alesha Doan. 2003. The Politics of Problem Definition: Applying and Testing Threshold Models. American Journal of Political Science. 47: 640-653.
r --. Read Wood, B. Dan and Soren Jordan. 2016. Presidents and the Politics of Polarization. Paper delivered at the annual meeting of the Southern Political Science Association.
n --. Read Box, G.E.P. and G.C. Tiao. 1975. Intervention Analysis with Applications to Economic and Environmental Problems. Journal of the American Statistical Association. 70: pp. 70-79.
n --. Read Hibbs, Douglas A. Jr. 1977. Political Parties and Macroeconomic Policy. American Political Science Review. 71: 1467-1479.
n --. Read Moe, Terry M. 1982. Regulatory Performance and Presidential Administration. American Journal of Political Science. 26: 197-224.
n --. Read Rasler, Karen. 1986. War, Accommodation, and Violence in the
n --. Read Hibbs, Douglas A. Jr. 1977. On Analyzing the Effects of Policy Interventions: Box-Jenkins and Box-Tiao vs. Structural Equation Models. Sociological Methodology: 1977.
n --. Read Oppenheimer, Bruce I., James A. Stimson, and Richard W. Waterman. 1986. Interpreting
a --. Use RATS (not possible in STATA and not convenient in R) to build intervention models for the Directory Assistance and Schizophrenic Perceptual Speed series. Replicate McCleary and Hay's analyses of these series (if possible) as reported on pp. 145-164. Additionally, use RATS to build intervention models for the Sutter County Workforce, Minneapolis Public Drunkenness, and Hyde Park Purse Snatchings series. Replicate McCleary and Hay's analyses of these series (if possible) as reported on pp. 164-191, 199-201. Offer a written critique of their results.
a --. Replicate the analysis in TSASS Table 2.6 in STATA. Repeat their analyses using the approach delineated during this week using RATS. Note especially the results for the gradual intervention. Critique and discuss the approach taken in TSASS in light of the results of the RATS replication.
8. Transfer functions and causality.
r --. Read McCleary and Hay,
r --. Read AETS4, Chapter 5, pp. 268-281.
r --. Read Shumway, chapter 5, pp. 296-301
r --. Read Norporth, Helmut. Transfer Function Analysis. New Tools for Social Scientists. W.D. Berry and
r --. Read Freeman, John R. 1983. Granger Causality and Time Series Analysis of Political Relationships. American Journal of Political Science. 27: 327-358.
n --. Sheehan, Richard G. and Robin Grieves. Sunspots and Cycles: A Test Of Causation. Southern Economic Journal. 1982: 775-77.
n --. Read Carmines, Edward G. and James A. Stimson. 1986. On the Structure and Sequence of Issue Evolution. American Political Science Review. 80: 901-920.
a --. Use RATS (not possible in STATA and not convenient in R) to build a transfer function causal model for the IBM Paris-
9. Bridging the gap between ARIMA and regression time series models.
r --. Read Beck, Nathaniel. 1991. Comparing Dynamic
Specifications: The Case of Presidential Approval. Political Analysis.
r --. DeBoef and Keele. Taking Time Seriously. American Journal of Political Science. 52: 184-200. Pay particular attention to the encompassing nature of the autoregressive distributive lag class of models in their discussion.
r --. Lewis-Beck, Michael S. 1986. Interrupted Time Series. New Tools For Social Scientists. William D. Berry and
r --. Read
n --. Caldiera, Gregory A. 1987. Public Opinion and the U.S. Supreme Court. American Political Science Review. 81: 1139- 1154.
n --. Wood, B. Dan. 1990. Does Politics Make a Difference at the EEOC? American Journal of Political Science. 34: 503-30.
n --. Read Beck, Nathaniel. 1985.
n --. Read Monroe, Kristen R. 1981. Presidential Popularity: An Almon Distributed Lag Model. Political Methodology. 7: 43-69.
a --. Using the data file prespop.rat, located in your data download directory, to replicate the analyses reported in Beck (1991) Tables 1 through Table 4 (if possible). The article is in your data download directory. Discuss how all of these models relate to one another. Which do you think is a best model?
10. Bridging the gap: Vector Autoregressive and VARMA Models
r --. Read AETS4, Chapter 5, pp. 285-342.
r --. Read TSASS, Chapter 4.
r --. Read Shumway, Chapter 5, pp. 301-315.
r --. Read Pfaff, Chapter 2
r --. Read Freeman, John R., John T. Williams, and Tse-min Lin. Vector Autoregression and the Study of Politics. American Journal of Political Science. 33: 842-877.
r --. Read Freeman, John R, Hauser, Kellstedt, and Williams. 1998. Long-Memoried Processes, Unit Roots, and Causal Inference in Political Science. American Journal of Political Science. 42:1289-1327.
n --. Read Judge, George G., W. E. Griffiths, R. Carter Hill, Helmut Lutkepohl, and Tsoung-Chao Lee. Introduction to the Theory and Practice of Econometrics. Chapter 18
n --. Read Maddala, G.S. 1992. Introduction to Econometrics: Second Edition. Chapter 14.
n --. Williams, John T. 1990. The Political Manipulation of Macroeconomic Policy. American Political Science Review. 84: 767-96.
a --. Do AETS4 SM, Chapter 5, pp. 42-45 using RATS. Also, replicate the examples in AETS4, Chapter 5, pp. 285-317. Visit the
a --. Replicate the analyses in TSASS, Chapter 4, pp. 106-124 using STATA.
na --. Note: You can also implement VAR in R using the contributed package dse1. Not as convenient though.
11. Bridging the gap: cointegration and error correction models
r --. AETS4, Chapter 6.
r --. Pfaff, Chapters 4, 7, and 8.
r --. Engle, R.F. and C.W.J. Granger. 1991. Introduction. Long Run Economic Relationships:
r --. Greene, 2012. Section 21.3.
r --. Reread DeBoef and Keele. Taking Time Seriously. American Journal of Political Science. 52: 184-200. This time attend carefully to the cointegration and error correction materials.
n --. Ostrom, Charles W., Jr. and Renee M. Smith. 1994. Cointegration and Error Correction in Multiple Time Series Analysis: Presidential Approval and the Quality of Life Equilibrium Hypothesis. Political Analysis. Volume 4: 127-184.
n --. Durr, Robert. 1994. Political Analysis. Volume 4: 185-228.
n --. Williams, John, 1994. Political Analysis. Volume 4: 229-236.
n --. Beck, Nathaniel. 1994. Political Analysis. Volume 4: 237-248.
n --.De Boef, Suzanna and Jim Granato. 2000. Testing for Cointegrating Relationships with Near Integrated Data. Political Analysis. 8: 99-117.
n --. Engle, R. F. and C.W.J. Granger. 1987. Cointegration and Error Correction: Representation,
n --. Engle, R. F. and B. Sam Yoo. 1987. Cointegrated Economic Time Series: An Overview with New Results. Paper presented at the European Meeting of the Econometric Society in
n --. MacKinnon, James G. 1991. Critical Values for Cointegration Tests. Long Run Economic Relationships:
a --. Do AETS4 SM, Chapter 6 in RATS. Visit the Estima website to obtain various canned procedures for exploring the presence of cointegration and estimating error correcting relations.
a --. Do AETS4 chapter 6 replication examples using RATS.
a--. Replicate the analyses in TSASS, chapter 6.
a--. Do the R-code examples in Pfaff, chapters 4, 7, and 8.
12. Unit Roots, and Integrated Data Revisited: Fractional Integration, and Near Unit Roots
r --. Read Shumway, chapter 5, pp. 267-276.
r --. Read Pfaff, chapter 3, pp. 62-70.
r --. Read Box-Steffensmeir, Janet and Andrew Tomlinson. Fractional Integration Methods in Political Science. Electoral Studies. 2000. 19: 63-76.
r --. Read Box-Steffensmeier, Janet and Renee Smith. 1998. Investigating Political Dynamics Using Fractional Integration Methods. American Journal of Political Science. 42: 661-689.
r --. Read Box-Steffensmeier, De Boef, and Lin. 2004. The Dynamics of the Partisan Gender Gap. American Political Science Review. 98:
r --. Read Box-Steffensmeier, Janet, Kathleen Knight, and Lee Sigelman. 1998. The Interplay of Macropartisanship and Macroideology: A Time Series Analysis. Journal of Politics. 60: 1031-1049.
r --. Read DeBoef, Suzanna and James Granato. 1997. Near Integration and the Analysis of Political Relationships. American Journal of Political Science. 41: 619-640.
a --. Use either RATS or R to replicate (if possible) the results in Table 5 of Box-Steffensmeier and Smith. The data are in your data download directory. Visit the Estima website to obtain various canned procedures for dealing with Fractional Integration.
a --. Using the same data, play with the tools for exploring issues of fractional integration in STATA (gphudak, modlpr, roblpr, ARFIMA, fracdiff).
a --. Also, do the R-code examples in Pfaff, chapter 2, pp. 30-36.
13. Time Varying Parameters: ARCH, Regime
Switching, and Other Time Varying Parameter Methods.
r --. Read AETS4, Chapters 3 and 7.
r --. Read Shumway, chapter 5, pp. 280-292.
r --. Read Wood. 2000. Weak Theories and Parameter Instability: Using Flexible Least Squares to Take Time-Varying Relationships Seriously. American Journal of Political Science. 44: 603-618.
r --. Read Wood. 2000. The Federal Balanced Budget Force: Modeling Variations from 1904-1996. Journal of Politics. 62:817-845.
r --. Wood. 2002. The Time Varying Effect of Public Approval on Presidential Success in Congress. Journal of Politics 2003, with Jon Bond and Richard Fleischer.
n --. Read Sayrs. 1993. The Long Cycle in International Relations: A Markov Specification International Studies Quarterly, Vol. 37, No. 2. 215-237.
n --. Read Freeman and Houser. 1998. A Computable Equilibrium Model for the Study of Political Economy American Journal of Political Science, Vol. 42, No. 2. 628-660.
a --.Do AETS4 SM, Chapter 3. Visit the Estima web site to see extra procedures for estimating ARCH and Regime Switching models.
a --. Read through and replicate the program examples in the RATS Users Guide, chapter 11.
a --. Do replication examples in AETS4, chapters 3 and 7.
a --. Do R-code examples in Shumway, sections 5.3 and 5.4.
14. State Space Modeling and the Kalman Filter.
r--. Read Shumway, Chapter 6.
r--. Read RATS User’s Guide, Chapter 10.
n--.Read “State Space Time Series Analysis,” Commandeur and Koopman
n--. Read “State Space Models with Regime Switching,” Kim and Nelson
a--. Do and try to understand the examples in RATS Users Guide, chapter 10 (especially, HPFILTER.rpg, SV.rpg, and Hamilton.rpg).
a--. Also, do and try to understand the R exercises in Shumway, chapter 6.