Multiple Time Series Models, Issue 148

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SAGE, 2007 - Mathematics - 99 pages
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Many analyses of time series data involve multiple, related variables. Multiple Time Series Models presents many specification choices and special challenges. This book reviews the main competing approaches to modeling multiple time series: simultaneous equations, ARIMA, error correction models, and vector autoregression. The text focuses on vector autoregression (VAR) models as a generalization of the other approaches mentioned. Specification, estimation, and inference using these models is discussed. The authors also review arguments for and against using multi-equation time series models. Two complete, worked examples show how VAR models can be employed. An appendix discusses software that can be used for multiple time series models and software code for replicating the examples is available.Key FeaturesOffers a detailed comparison of different time series methods and approaches. Includes a self-contained introduction to vector autoregression modeling. Situates multiple time series modeling as a natural extension of commonly taught statistical models.
 

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This is very much a terse text on multiple time series. The one by Lutkepohl is comprehensive and detailed, but this one gives you a quick reference on the go. It's also a tribute to the co-author J.T. Williams.

Contents

Introduction to Multiple Time Series Models
1
11 Simultaneous Equation Approach
4
12 ARIMA Approach
6
13 Error Correction or LSE Approach
7
14 Vector Autoregression Approach
9
15 Comparison and Summary
12
Basic Vector Autoregression Models
14
21 Dynamic Structural Equation Models
15
273 VAR Versus VECM ECM
54
28 Criticisms of VAR
56
Examples of VAR Analyses
59
311 Testing for Unit Roots
61
312 Specifying the Lag Length
62
313 Estimation of the VAR
63
314 Granger Causality Testing
65
315 Decomposition of the Forecast Error Variance
66

22 Reduced Form Vector Autoregressions
18
23 Relationship of a Dynamic Structural Equation Model to a Vector Autoregression Model
20
24 Working With This Model
22
25 Specification and Analysis of VAR Models
23
251 Estimation of VAR
24
253 Testing Serial Correlation in the Residuals
28
254 Granger Causality
32
255 Interpreting Granger Causality
34
256 Testing Other Restrictions in a VAR Model
36
258 Error Bands for Impulse Responses
41
259 Innovation Accounting or Decomposition of Forecast Error Variance
45
26 Other Specification Issues
48
261 Should Differencing Be Used for Trending Data?
49
27 Unit Roots and Error Correction in VARs
50
272 Error Correction as a VAR Model
52
316 Impulse Response Analysis
68
32 Effective Corporate Tax Rates
71
321 Data
72
322 Testing for Unit Roots
73
324 Granger Causality Testing
74
325 Impulse Response Analysis
77
326 Decomposition of the Forecast Error Variance
79
327 A Further Robustness Check
81
33 Conclusion
82
Software for Multiple Time Series Models
85
Notes
89
References
92
Index
96
About the Authors
99
Copyright

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About the author (2007)

Patrick T. Brandt is an Assistant Professor of Political Science in the School of Social Science at the University of Texas at Dallas. He has published in the American Journal of Political Science and Political Analysis. He teaches courses in social science research methods and social science statistics. His current research focuses on the development and application of time series models to the study of political institutions, political economy, and international relations. He received an A.B. (1990) in Government from the College of William and Mary, an M.S. (1997) in Mathematical Methods in the Social Sciences from Northwestern University, and a Ph.D. (2001) in Political Science from Indiana University. Before joining the faculty at the University of Texas at Dallas, he held positions at the University of North Texas, Indiana University, and as a fellow at the Harvard-MIT Data Center.

John T. Williams was Professor and Chair of the Department of Political Science at University of California, Riverside. He taught time series analysis at the Inter-university Consortium for Political and Social Research Summer Training Program for over ten years. His work uses statistical methods in the study of political economy and public policy. He co-authored two books: Compound Dilemmas: Democracy, Collective Action, and Superpower Rivalry (University of Michigan Press, 2001) and Public Policy Analysis: A Political Economy Approach (Houghton Mifflin, 2000). He published over twenty journal articles and book chapters on a wide range of topics, ranging from macroeconomic policy to defense spending to forest resource management. He was a leader in the application of new methods of statistical analysis to political science, especially the use of vector autoregression (VAR), Bayesian, and event count time series models. He received a B.A. (1979), an M.A. (1981) from North Texas State University, and a Ph.D. (1987) from the University of Minnesota. Before moving to Riverside in 2001, he held academic positions at the University of Illinois Chicago (1985-1990) and at Indiana University, Bloomington (1990-2001).