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Introduction to Time Series Analysis
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Introduction to Time Series Analysis



October 2014 | 232 pages | SAGE Publications, Inc
Introducing time series methods and their application in social science research, this practical guide to time series models is the first in the field written for a non-econometrics audience. Giving readers the tools they need to apply models to their own research, this unique book demonstrates the use of—and the assumptions underlying—common models of time series data, including finite distributed lag; autoregressive distributed lag; moving average; differenced data; and GARCH, ARMA, ARIMA, and error correction models.

 
PREFACE
 
ACKNOWLEDGEMENTS
 
ABOUT THE AUTHOR
 
SERIES EDITOR'S INTRODUCTION
 
1. Thinking Time-serially
 
2. Fundamental Concepts in Time Series Analysis
 
3. Static Time Series Models and Ordinary Least Squares Estimation
 
4. Introducing Dynamic Time Series Models
 
5. Autoregressive Moving Average (ARMA) Models
 
6. Models for Integrated and Cointegrated Data
 
Conclusion

Supplements

Supplemental Materials
Data sets and STATA commands for examples in the book are available under the heading "Sample Materials & Chapters" above.

This volume does an excellent job of introducing modern time series analysis to social scientists who are already familiar with basic statistics and the general linear model.”

William G. Jacoby, Michigan State University
Key features

KEY FEATURES:

 

  • This is the first book to approach time series analysis from the perspective of a social scientist interested in hypothesis testing.
  • Hypothesis testing is emphasized using examples relevant to the fields of public policy, political science, and sociology.
  • Examples from real-world datasets illustrate the models presented.
  • Datasets are available on the companion website, along with the necessary commands to reproduce the results in the monograph.

Sample Materials & Chapters

Chapter 1

R Files

Data Files


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