Time Series Analysis with Two or More Time Series

Author(s):  
Youseop Shin

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.

2012 ◽  
Vol 8 (4) ◽  
pp. 377
Author(s):  
Carl B. McGowan, Jr. ◽  
Izani Ibrahim

In this paper, we demonstrate the use of time series analysis, including unit roots tests, Granger causality tests, cointergation tests and vector error correction models. We generate four time series using simulation such that the data has both a random component and a growth trend. The data are analyzed to demonstrate the use of time series analysis procedures.


2016 ◽  
Vol 57 ◽  
pp. 311-323 ◽  
Author(s):  
Sunčica Vujić ◽  
Jacques J.F. Commandeur ◽  
Siem Jan Koopman

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