B. Dan Wood
Political Science 606.600
Dynamic Analysis with Quantitative Methods
Fall Semester, 2007
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Location: 2064 |
Office: 2098 |
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Time: |
Office Hours: |
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Phone: 845-1610 |
Email: bdanwood@polisci.tamu.edu |
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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 more recently developed approaches such as Error
Correction models, multivariate time series (VAR), and pooled time series cross
sections. We will also look at time varying parameter models including ARCH,
regime switching, state space, and flexible least squares. 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. A primary statistical
package for the class will be RATS for Windows (WINRATS). We will also be
working in R. However, you need not be proficient in RATS or R before entering
the course since sample programs will be available.
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 assignments are due on
the date listed on the attached reading list. I will call on each of you at
some point to discuss aspects of the homework assignment. Homework is due the
next week after the assignment on the following course outline. The research
project is due on Tuesday, December 4th.
Late homework or research projects will not be accepted. The final
examination will be take home and due one week after the end of classes.
Enders, Walter. Applied
Econometric Time Series, Second Edition. Wiley.
Enders, Walter. RATS Handbook for Econometric Time Series. Wiley.
Shumway,
Robert H. and David S. Stoffer. Time Series and Its Applications: with R
Examples. Springer
Pfaff, Bernard. Analysis
of Integrated and Cointegrated Time Series with
R. Springer.
Doan, Thomas A. RATS User’s Manual, Version 6.
McCleary,
Richard and Richard Hay. 1980. Applied Time Series Analysis. Sage Publications. This is out of print. I will supply a
copy for Xeroxing.
Selected illustrative articles as recommended in the attached
course outline.
The RATS
Handbook contains a disk with some of the data you will need for completing
the exercises. Click here for the
data for other assignments.
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 the Office of Support
Services for Students with Disabilities in Room 126 of the
r=required reading
n=recommended, but not required
a=assignment
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 Users Manual, chapters 1 through 3.
r--. Read Shumway, chapter1.
r--. Read Greene, William H. 2003. Econometric Analysis: 5th
Edition. Chapter 12.
a--. Do Enders Handbook, chapter 1, using
WINRATS. Do R-code examples in Shumway, chapter 1. Be prepared to discuss the materials.
2. A survey of elementary regression time
series methods: autocorrelation. (The following reading assignments should be
completed over the next three weeks.)
r-- Read Granger, C.W. J. and P. Newbold.
1974. Spurious Regressions in Econometrics. Journal of
Econometrics. 2: 111-120.
r-- Read Enders, Chapters 1, 2, and 4. You can just skim the differential equation
stuff in chapter 1 if it seems too difficult.
r- Read Shumway, chapters 2
and 3.
r-- Read Pfaff, chapters 1, 2,4, and 5.
r—Look at McCleary and Hay,
Chapters 1&2, pp. 17-139 for a simplified treatment.
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-- The Ostrom
data (see link above) contains US and
a – Do Enders Handbook, chapter 2.
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--. Using either RATS or R, graph the German
Immigration, IBM B, and Mine Injuries Series. Use RATS or R to identify the
different ARIMA components of these series. Make sure that you do appropriate
transformations of these series to assure level and variance stationarity. In RATS use @DFUNIT, @PPUNIT, and @BAYESTST
to do formal hypothesis tests for the level stationarity
of these and the transformed series.
Also, visit
a--.Do R-code examples in Pfaff, chapters 2 and 4.
a--. D R-code examples in Shumway, chapter 3.
a -- Do R-code examples in Pfaff, chapter 1.
4. Building univariate
noise models: estimation, diagnosis, and metadiagnosis.
a--. Use RATS or R to identify and estimate a univariate ARIMA model for the Sutter County Workforce and
Boston Armed Robery time series. 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 or R to identify and estimate a univariate ARIMA model for the Swedish Harvest Index and
Hyde Park Purse Snatchings series. 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. 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 Enders, Chapter 5, pp. 240-247.
r--. Read Shumwaya, Chapter
5, pp. 295-301.
r--. Read Wood, B. Dan. 1988. Principals,
Bureaucrats, and Responsiveness. American Political
Science Review. 82: 213-234.
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 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. Offer a written critique of their results.
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.
an--. Use RATS to build an intervention model for the
Suicides series. Replicate McCleary and Hay's
analyses of this series (if possible) as reported on pp. 191-199.
7. Transfer functions and causality. (Read
the following over the next two weeks.)
r--. Read Enders, Chapter 5, pp. 247-264.
r--. Read McCleary
and Hay, Chapter 5.
r--. Reread Shumway, chapter
5, pp. 295-301
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 to build a transfer function
causal model for the IBM Paris-
8. Bridging the gap between ARIMA and
regression time series models.
r--. Read
r--. Read Beck, Nathaniel. 1991. Comparing Dynamic
Specifications: The Case of Presidential Approval. Political
Analysis.
r--. Lewis-Beck, Michael S. 1986. Interrupted
Time Series. New Tools For Social Scientists.
William D. Berry and
n--. Wonnacott, Ronald J. and Thomas H. Wonnacott.
1979. Econometrics. pp. 212-233.
n--. Caldiera, Gregory A.
1987. Public Opinion and the
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--. Replicate the analyses reported in Beck
(1991) Tables 1 through Table 4 (if possible). Discuss how all of these models
relate to one another. Which do you think is a best model?
9. Bridging the gap: Vector Autoregressive
and VARMA Models
r--. Read Enders, Chapter 5, pp. 264-311.
r--. Read Shumway, chapter
5, pp. 302-319.
r--. 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
r--. Read Maddala, G.S. 1992. Introduction
to Econometrics: Second Edition. Chapter 14.
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--. Williams, John T. 1990. The Political Manipulation of
Macroeconomic Policy. American Political Science
Review. 84: 767-96.
n--. Read Huang, Chi. 1989. Determining
the Lag Order of a Vector Autoregressive Process: Some Guidelines for Political
Studies.
a--. Do Enders Handbook, Chapter 5. Visit the
10. Bridging the gap: cointegration and error
correction models
r--. Enders, Chapter 6.
r--. Pfaff, Chapters 3, 6, and 7.
r--. Engle, R.F. and C.W.J. Granger.
1991. Introduction. Long Run Economic Relationships:
r--. Greene, William H. 2000. Econometric Analysis:
Fourth Edition. Section 18.4.
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 Enders Handbook, Chapter 6. Visit the Estima
website to obtain various canned procedures for exploring the presence of cointegration.
Do the R-code examples in Pfaff, chapters 3, 6, and 7.
11. Unit Roots, and
Integrated Data Revisited:
Fractional Integration, and Near Unit Roots
r--. Read Greene.
2003. Econometric Analysis: Fifth Edition. Section 20.3.5.
r--. Read Shumway, chapter
5, pp. 271-279.
r--. Read Pfaff, chapter 2, pp. 30-36.
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, 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.
r--. Read DeBoef, Suzanna
and James Granato, Overdifferencing:
Implications for the Time Series Study of Political Relationships. Working paper.
a--. Use either RATS or R to replicate (if
possible) the results in Table 5 of Box-Steffensmeier and Smith. Visit the Estima
website to obtain various canned procedures for dealing with Fractional Integration. Also, do
the R-code examples in Pfaff, chapter 2, pp. 30-36.
12. Time Varying Parameters: ARCH, Regime
Switching, and Other Time Varying Parameter Methods.
r--. Read Enders, Chapter 3.
r--. Read Shumway, chapter
5, pp280-295.
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.
n--. 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.
n--Read Wood.
2000. The Federal Balanced Budget Force: Modeling Variations from
1904-1996. Journal of Politics. 62:817-845.
n—Wood. 2002. The Time Varying Effect of
Public Approval on Presidential Success in Congress. Journal of Politics 2003, with Jon Bond and Richard
Fleischer.
a--. Do Enders Handbook, Chapter 3. Visit the Estima
web site to see extra procedures for estimating ARCH and Regime Switching models.
Do R-code examples in Shumway, sections 5.3 and 5.4.
13. Regression With
Pooled Data. (These readings and assignments cover the last two weeks.)
r--. Read Pindyck,
Robert S. and Daniel Rubinfeld, 1981. Pooling of Cross-Section and Time Series Data. Econometric Models and Economic Forecasts.
McGraw-Hill:
r--. Read Hsiao, Cheng. 1986. Analysis of Panel
Data, Chapters 1&2.
r--. Read Stimson, James A. 1985. Regression in Space
and Time: A Statistical Essay. American Journal of
Political Science. 29: 914-947.
r--. Read and study Judge, George G., W. E. Griffiths,
R. Carter Hill, Helmut Lutkepohl, and Tsoung-Chao Lee. The Theory and
Practice of Econometrics. Table 13.1
r--. Read Sayrs, Lois. Pooled
Time Series Analysis. Sage Publications:
r--. Read Greene, William H. 2003. Econometric
Analysis: Fifth
Edition. Chapter 13.
a--. Use the data in Greene, 2003, Appendix
Table F7.1 to replicate the applicable analyses in Greene, chapter 13. Enter the data in RATSDATA. Then use RATS's Panel procedure to replicate (if possible) the
results reported in Greene Examples 13.2 and 13.5.
14. Regression With
Pooled Data (cont.)
r--. Read Greene, William H. 2003. Econometric
Analysis: Fifth Edition. Chapter 13.
r--. Read Beck, Nathaniel and
Jonathan Katz. 1995. What to do (and Not to Do) with Time Series-Cross
Section Data in Comparative Politics. American Political
Science Review. 89: 634-47.
a--. Use Greene's data in Table F13.1 to explore
pooled time series cross section estimation. Enter the data. Then use RATS's Panel procedure to replicate (if possible) the
results reported in Greene Chapter 13.9.7.