B. Dan Wood
Political Science 607.600
Dynamic Analysis with Quantitative Methods
Spring Semester, 2016
Location: 2064 Allen Building 
Office: 2098 Allen Building 
Time: 
Office Hours: 1:302:00 
Phone: 8451610 
Email: bdanwood@polisci.tamu.edu 

Purpose This course
considers statistical techniques to evaluate social processes occurring through
time. The early focus is on econometric regression methods, followed by a
BoxJenkins 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 takehome, sent out electronically
on Wednesday May 4 and due back electronically on Wednesday May 11.
BoxSteffensmeier,
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 antidiscrimination
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 9798451637. For additional
information, visit http://disability.tamu.edu.
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 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:7^{th} 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 Rcode 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.
rRead TSASS Chapter 2 and the Appendix
rRead Shumway, chapters 2 and 3, chapter 5, pp. 277279.
r Read Pfaff, chapters 1, 3, 5, and 6.
r—Look at McCleary and
Hay, Chapters 1&2, pp. 17139 for a simplified treatment.
r Read Granger, C.W. J. and P. Newbold.
1974. Spurious Regressions in Econometrics. Journal of
Econometrics. 2: 111120.
r. Read Greene, William H. 2012. Econometric Analysis:7^{th} Edition. Chapter 21.
n Chatfield, C. 1979. Inverse Autocorrelations. Journal of the Royal Statistical Society. 142: 363377.
n Schwartz, G. 1978.
n Akaike, H. 1974. A New Look
at Model Identification. IEEE Transaction On Automatic
Control. AC19: 716723.
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 Rcode 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 Rcode examples in Shumway, chapter 3.
a. Do Rcode 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. 104121. 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. 121132. 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:
45576.
r . Wood, B. Dan. 2012. Presidential Saber Rattling: Causes and
Consequences. pp. 6467.
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. 141228.
r . Read AETS4, Chapter 5, pp. 26168.
r . Read BoxSteffensmeier
et al, pp. 5865.
r . Read Wood, B. Dan. 1988. Principals,
Bureaucrats, and Responsiveness. American Political
Science Review. 82: 213234.
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: 640653.
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. 7079.
n . Read Hibbs, Douglas A. Jr. 1977. Political Parties and
Macroeconomic Policy. American Political Science Review.
71: 14671479.
n . Read Moe, Terry M. 1982. Regulatory Performance
and Presidential Administration. American Journal of
Political Science. 26: 197224.
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: BoxJenkins and
BoxTiao 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. 145164.
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. 164191, 199201. 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,
Chapter 5.
r . Read AETS4, Chapter 5, pp. 268281.
r . Read Shumway, chapter 5, pp. 296301
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: 327358.
n . Sheehan, Richard G. and Robin Grieves. Sunspots
and Cycles: A Test Of Causation. Southern Economic Journal.
1982: 77577.
n . Read Carmines, Edward G. and James A. Stimson.
1986. On the Structure and Sequence of Issue Evolution. American
Political Science Review. 80: 901920.
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: 184200. Pay particular attention to the
encompassing nature of the autoregressive distributive lag class of models in
their discussion.
r . LewisBeck, 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: 50330.
n . Read Beck, Nathaniel. 1985.
n . Read Monroe, Kristen R. 1981. Presidential
Popularity: An Almon Distributed Lag Model. Political
Methodology. 7: 4369.
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. 285342.
r . Read TSASS, Chapter 4.
r . Read Shumway, Chapter 5, pp. 301315.
r . Read Pfaff, Chapter 2
r . Read Freeman, John R., John T. Williams, and Tsemin
Lin. Vector Autoregression and the Study of Politics.
American Journal of Political Science. 33: 842877.
r . Read Freeman, John R, Hauser, Kellstedt, and
Williams. 1998. LongMemoried
Processes, Unit Roots, and Causal Inference in Political Science. American Journal of Political Science. 42:12891327.
n . Read Judge, George G., W. E.
Griffiths, R. Carter Hill, Helmut Lutkepohl, and TsoungChao 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:
76796.
a . Do AETS4 SM, Chapter 5, pp. 4245 using
RATS. Also, replicate the examples in AETS4, Chapter 5, pp.
285317. Visit the
a . Replicate the analyses in TSASS, Chapter 4, pp. 106124 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: 184200. 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:
127184.
n . Durr, Robert. 1994. Political
Analysis. Volume 4: 185228.
n . Williams, John, 1994. Political Analysis. Volume 4: 229236.
n . Beck, Nathaniel. 1994. Political
Analysis. Volume 4: 237248.
n .De Boef, Suzanna and Jim Granato. 2000.
Testing for Cointegrating Relationships with
Near Integrated Data. Political
Analysis. 8: 99117.
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 Rcode 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. 267276.
r . Read Pfaff, chapter 3, pp. 6270.
r . Read BoxSteffensmeir,
Janet and Andrew Tomlinson. Fractional Integration Methods in
Political Science. Electoral
Studies. 2000. 19: 6376.
r . Read BoxSteffensmeier, Janet and Renee
Smith. 1998. Investigating Political Dynamics Using
Fractional Integration Methods. American Journal of
Political Science. 42: 661689.
r . Read BoxSteffensmeier, De Boef, and Lin. 2004. The Dynamics of the Partisan Gender Gap. American
Political Science Review. 98:
r . Read BoxSteffensmeier, Janet, Kathleen Knight, and Lee Sigelman. 1998. The Interplay of Macropartisanship
and Macroideology: A Time Series Analysis. Journal of Politics. 60: 10311049.
r . Read DeBoef, Suzanna
and James Granato.
1997. Near Integration and the Analysis of Political Relationships. American Journal of Political Science. 41: 619640.
a . Use either RATS or R to replicate (if
possible) the results in Table 5 of BoxSteffensmeier 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 Rcode examples in Pfaff, chapter
2, pp. 3036.
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. 280292.
r . Read Wood.
2000. Weak Theories and Parameter
Instability: Using Flexible Least Squares to Take TimeVarying Relationships
Seriously. American Journal of Political Science. 44: 603618.
r . Read Wood.
2000. The Federal Balanced Budget Force: Modeling Variations from
19041996. Journal of Politics. 62:817845.
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. 215237.
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. 628660.
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 Rcode 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.