clarke

Harold D. Clarke is Ashbel Smith Professor in the School of Economic, Political and Policy Sciences, University of Texas at Dallas, and adjunct Professor, Department of Government, University of Essex. His current research interests focus on the political economy of party support. He has published widely on this topic in journals such as the American Journal of Political Science, American Political Science Review, and British Journal of Political Science. He is chief editor of Electoral Studies. He has been a principal investigator for the 2001, 2005 and 2010 British Election Study (University of Essex and University of Texas at Dallas), the 2011 Political Support in Canada Study, and the 2012 Political Support in America Study. His most recent books are Brexit – Why Great Britain Voted to Leave the European Union (Cambridge University Press, 2017), Affluence, Austerity and Electoral Change in Britain (Cambridge University Press, 2013) and Austerity and Political Choice in Britain (Palgrave Macmillan, 2015).

Course Content
This applied course focuses on statistical methods for conducting dynamic analyses of economic, political and social data. Several important model classes are considered: ARIMA, ARFIMA, Fractional Error Correction, GARCH and Dynamic Conditional Correlations, Markov Switching, Dynamic Panels for Time Series Cross-Sectional (TSCS) Data, State Space Representations, VAR and Vector Error Correction. Both frequentist and Bayesian approaches to model specification, analysis, and interpretation are discussed. The course will provide working knowledge of major software packages such as Eviews, R, Stata and WinRats to analyze various dynamic models. Students are invited to bring their own data sets for analyses in daily lab sessions.

Course Objectives
The course will benefit anyone who is interested in conducting multivariate dynamic analyses of economic, political, and social processes. The aim is to teach course participants how to undertake and evaluate sophisticated dynamic analyses of economic, political, and social data. Methods considered will be helpful to graduate students and faculty in the social sciences as well as researchers working in the public and private sectors.

Course Prerequisites
Participants should be familiar with applied multiple regression analysis and the standard Windows operating environment. Basic knowledge of a major statistical software package such as Stata is helpful but not required.

Representative Reading:
Asteriou, Dimitrios and Stephen G. Hall. 2011. Applied Econometrics. 2nd ed. London: Palgrave.
Becketti, Sean. 2013. Introduction to Time Series Using Stata. College Station, TX: Stata Press.
Box-Steffensmeier, Janet et al. 2014. Time Series Analysis for the Social Sciences. New York: Cambridge University Press.
Commandeur, Jacques and Siem Jan Koopman. 2007. An Introduction to State Space Time Series Analysis. Oxford: Oxford University Press.
Pfaff, Bernhard. 2006. Analysis of Integrated and Cointegrated Time Series With R. New York Springer.

Recommended Texts
Commandeur, Jacques and Siem Jan Koopman. 2007. An Introduction to State Space Time Series Analysis. Oxford: Oxford University Press.
Box-Steffensmeier, Janet et al. 2014. Time Series Analysis for the Social Sciences. New York: Cambridge University Press.

Supplementary Texts
Asteriou, Dimitrios and Stephen G. Hall. 2011. Applied Econometrics: A Modern Approach Using EVIEWS and Microfit. 2nd Edition. London: Palgrave.
Enders, Walter. Applied Econometric Time Series. 3rd edition. New York: John Wiley & Sons, 2010.
Kleiber, Christian and Achim Zeileis. 2008. Applied Econometrics with R. New York: Springer.
Ntzoufras, Ioannis. 2009. Bayesian Modeling Using Winbugs. New York: Wiley.
Pfaff, Bernhard. 2006. Analysis of Integrated and Cointegrated Time Series With R. New York Springer.

TOPICS

1. Introduction: Analyzing Time Series Data
– Stationarity and nonstationary processes
– Using Stata, Eviews, RATS and R to analyze time series data

Reading:
Box-Steffensmeier et al., ch. 1
DeBoef and Keele “Taking Time Seriously: Dynamic Regression Models”
Enders, ch. 2
Kleiber and Zeileis, chs. 1, 2, 3

2. Univariate ARIMA Models
– Specification, estimation and diagnostics
– Forecasting with ARIMA models

Reading:
Box-Steffensmeier et al., ch. 2, Sections 2.1 – 2.6
Enders, ch. 2
Plaff, ch. 1

3. Intervention and Transfer Function Models for Policy Analysis & Political Economy
– Specification, estimation and diagnostics
– Exogeneity testing

Reading:
Box-Steffensmeier et al., ch. 2, Sections 2.7 – 2.9
Clarke, Mishler and Whiteley, “Recapturing the Falklands”
Enders, ch. 5, pp. 272-297

4. Unit Roots and Cointegration
– Non-stationary processes – implications and testing
-Dynamic equilibria and cointegrated processes

Reading:
Box-Steffensmeier et al., chs. 5, 6
Enders, Applied Econometric Time Series, chs. 4, 6
Harris and Sollis, ch. 5
Kleiber and Zeileis, ch. 6
Pfaff, chs. 2, 3, 4, 6

5. Fractional Integration and Fractional Cointegration
-Non-stationary processes generalized
-Cointegration and error correction generalized

Reading:
Baillie and Bollerslev, “Co-integration, Fractional Co-integration, and Exchange Rate Dynamics”
Barkoulas, Baum and Oguz, “Fractional Dynamics in a System of Long-Term International Interest Rates”
Box-Steffensmeier et al., ch. 7
Clarke and Lebo, “Fractional (Co)Integration and Governing Party Support in Britain”

6. ARCH, GARCH and Dynamic Conditional Correlations
-Volatility and conditional heteroscedasticity in financial and public opinion data
-Multivariate generalizations

Reading:
Asteriou, ch. 14
Clarke et al., 2009: ch. 4 (ARCH Models of Public Opinion)
Enders, ch. 3.
Lebo and Box-Steffensmeier, “Dynamic Conditional Correlations”

7. State Space Time Series Models: Introduction
– State Space representations of time series processes
– Dynamic factor analysis with covariates

Reading:
Commandeur and Koopman, chs. 1-9

8. State Space Time Series Models: A Bayesian Approach Using Winbugs and R to Winbugs
– Bayesian Dynamic factor analysis

Reading:
Jackman, “Pooling the Polls”
Ntzoufras, chs. 1-4

9. Vector Autoregression and Vector Error Correction
– Structural and Reduced Forms
-General VAR, MAR and Forecasting
-Specifying and Estimating VEC models

Reading:
Box-Steffensmeier et al., ch. 4
Enders, ch. 5, pp. 297-355.
Pfaff, ch. 7
Sims, “Macroeconomics and Reality”

10. Dynamic Panel Models for Data in Time and Space
– Fast and Slowly Moving Covariates
– Simultaneity Biases
– SUR Representations

Reading:
Plümper and Troeger “Efficient Estimation of Time-Invariant and Rarely Changing Variables in Finite Sample Panel Analyses with Unit Fixed Effects”
Williams and Whitten “But Wait, There’s More! Maximizing Substantive Inferences from TSCS Models”
Wawro, “Estimating Dynamic Panel Models”

REFERENCES

Baillie, R. T. and T. Bollerslev. 1994. “Cointegation, Fractional Cointegration and Exchange Rate Dynamics.” The Journal of Finance 49: 737-45.

Barkoulas, J.T., C. F. Baum and G. S. Oguz. N.D. “Fractional Dynamics in a System of Long Term International Interest Rates.” Unpublished manuscript.

Box-Steffensmeier, Janet M. and Renée M. Smith. 1996. “The Dynamics of Aggregate Partisanship.” American Political Science Review 90: 567-80.

Charemza, W.W. and Derek F. Deadman. New Directions in Econometric Practice. 2nd edition. Aldershot: Edward Elgar, 1997.

Clarke, H. D. and M. Lebo. 2003. “Fractional (Co)Integration and Governing Party Support in Britain.” British Journal of Political Science 33: 283-301.

Clarke, H. D., W. Mishler and P. Whiteley. 1990. “Recapturing the Falklands: Models of Conservative Popularity, 1979-83.” British Journal of Political Science 20:63-82.

Clarke, H. D. et al. 2009. Performance Politics and the British Voter. Cambridge: Cambridge University Press, ch. 4.

Engle, R. F. and C. W. J. Granger. 1987. “Co-integration and Error Correction: Representation, Estimation, and Testing.” Econometrica 55:251-76.

Franses, Philip Hans. Time Series Models for Business and Economic Forecasting. Cambridge: Cambridge University Press, 1998.

Harris, Richard and Robert Solis. Apllied Time Series Modelling and Forecasting. London: Wiley, 2003.

Jackman, Simon. 2005. “Pooling the Polls Over an Election Campaign.” Australian Journal of Political Science 40: 499-517.

Keele, Luke and Suzanna DeBoef. 2008. “Taking Time Seriously: Dynamic Regression.” American Journal of Political Science 52: 184-200.

Kwiatkowski, D., P.C.B. Phillips, P. Schmidt and Y. Shin. 1992. “Testing the Null Hypothesis of Stationairty Against the Alternative of a Unit Root.” Journal of Econometrics 54: 159-78.

Lebo, M. and J. Box-Steffensmeier. 2008. “Dynamic Conditional Correlations in Political Science.” American Journal of Political Science 52: 688-704.

Lebo, M., R. W. Walker and H. D. Clarke. 1999. “You Must Remember This: Dealing with Long Memory in Political Analyses.” Electoral Studies 19:31-48.

Sims, C. 1980. “Macroeconomics and Reality.” Econometrica 48:1-48.

Wawro, Gregrory. 2002. “Estimating Dynamic Panel Models in Political Science.” Political Analysis 10: 25-48.

Williams, Laron and Guy Whitten “But Wait, There’s More! Maximizing Substantive Inferences from TSCS Models”