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Time Series Analysis in the Social SciencesThe Fundamentals$
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Youseop Shin

Print publication date: 2017

Print ISBN-13: 9780520293168

Published to California Scholarship Online: September 2017

DOI: 10.1525/california/9780520293168.001.0001

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Time Series Analysis with Two or More Time Series

Time Series Analysis with Two or More Time Series

Chapter:
(p.115) Six Time Series Analysis with Two or More Time Series
Source:
Time Series Analysis in the Social Sciences
Author(s):

Youseop Shin

Publisher:
University of California Press
DOI:10.1525/california/9780520293168.003.0006

Chapter Six explains time series analysis with one or more independent variables. The dependent variable is the monthly violent crime rates and the independent variables are unemployment rates and inflation. This chapter discusses several topics related to the robustness of estimated models, such as how to prewhiten a time series, how to deal with trends and seasonal components, how to deal with autoregressive residuals, and how to discern changes of the dependent variable caused by independent variables from its simple continuity. This chapter also discusses the concepts of co-integration and long-memory effect and related topics such as error correction models and autoregressive distributive lags models.

Keywords:   bivariate time series analysis, multiple time series analysis, prewhitening, Granger causality, co-integration, long memory effect, error correction model, autoregressive distributed lags model

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