A Practical Guide to Time Series Analysis
Series: Methodological Tools in the Social Sciences;
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Product details:
- Publisher Cambridge University Press
- Date of Publication 31 March 2026
- ISBN 9781108418539
- Binding Hardback
- No. of pages536 pages
- Weight 500 g
- Language English 0
Categories
Short description:
We expand readers' knowledge of linear regression with detailed explanations and applications of key models used in time series analysis.
MoreLong description:
Understanding change over time is a critical component of social science. However, data measured over time - time series - requires their own set of statistical and inferential tools. In&&&160;this book,&&&160;Suzanna Linn, Matthew Lebo, and Clayton Webb explain the most commonly used time series models and demonstrate their applications using&&&160;examples. The&&&160;guide&&&160;outlines the steps taken to identify a series, make determinations about exogeneity/endogeneity, and make appropriate modelling decisions and inferences. Detailing challenges and explanations of key techniques not covered in most time series textbooks, the authors show how navigating between data and models, deliberately and transparently, allows researchers to clearly &&&160;explain their statistical analyses to a broad audience.
'They don't say so, but this impressive, comprehensive volume is the result of the authors' lifetimes of careful study of social science time series methods. Many of us have been privileged to hear presentations on their research leading to this milestone, and now everyone can benefit by reading this book and understanding the big picture they have been working towards.' Gary King, Albert J Weatherhead III University Professor, and Director of the Institute for Quantitative Social Science, Harvard University
Table of Contents:
1. Introduction; Part I. Preliminary Questions: 2. Univariate time series models; 3. Testing for unit roots and stationarity; 4. The exogeneity question; Part II. Single-Equation Regression Models: 5. Regression models for stationary time series; 6. Regression models for unit root time series; 7. Regression models for time series with uncertain properties; Part III. Multiple-Equation Regression Models: 8. Vector autoregression for stationary time series; 9. Vector autoregression for unit root time series; 10. Time series analysis with transparency.
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