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.
表中的内容
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
关于作者
Mark Pickup is an associate professor in the Department of Political Science at Simon Fraser University. Mark is a specialist in political behavior and political methodology. Substantively, his research primarily falls into four areas: political identities and vote choice; the economy and democratic accountability; conditions of democratic responsiveness; and polls and electoral outcomes. His research focuses on political information, public opinion, political identities, and election campaigns within North American and European countries. His methodological interests concern the analysis of longitudinal data (time series, panel, network, etc.), with secondary interests in Bayesian analysis and survey/lab experiment design.
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