Chapter 13 Readings and Bibliographical References

13.1 Mandatory

Shin, Y. (2017). Time series analysis in the social sciences: the fundamentals. Univ of California Press.

13.2 Other readings

Wells, C., Shah, D. V., Pevehouse, J. C., Foley, J., Lukito, J., Pelled, A., & Yang, J. (2019). The Temporal Turn in Communication Research: Time Series Analyses Using Computational Approaches. International Journal of Communication (19328036), 13.

Brodersen KH, Gallusser F, Koehler J, Remy N, Scott SL. Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 2015, Vol. 9, No. 1, 247-274.

Gaubatz, K. T. (2014). A Survivor’s Guide to R: An Introduction for the Uninitiated and the Unnerved. SAGE Publications.

Liboschik, T., Fokianos, K., & Fried, R. (2017). tscount: An R package for analysis of count time series following generalized linear models. Journal of Statistical Software, 82(1), 1-51.

Schaffer, A. L., Dobbins, T. A., & Pearson, S. A. (2021). Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions. BMC medical research methodology, 21(1), 1-12.

Zivot E., Wang J. (2003) Unit Root Tests. In: Modeling Financial Time Series with S-Plus®. Springer, New York, NY.

13.3 Useful resources

Cross Validated for statistics-related questions

Stackoverflow for coding-related questions