Having learned the simple random walk model, Taila wonders what are the advanced issues with nonstationary models associated with time-series data. Prof. Metric says that her curiosity is great because we will discuss these problems in this chapter, and once we finish with it, we will be able to:
1. Analyze cases when nonstationary models can be made stationary.
2. Explain the cointegration concepts and the theoretical foundations of the related tests.
3. Discuss other time-series models.
4. Apply Excel into performing the necessary tests and estimating the models.
Prof. Metric says that we will learn how to detect a nonstationary model and how to handle a nonstationary model in the next section.