Time Series Econometrics With Python

In this section, we will provide a detailed overview of the time series models that were covered in the course, and explain how to implement them in Python. Among other things, we will demonstrate how to calibrate ARIMA-type models, how to prepare time series data so that they can be modeled using an ARIMA process, and how to handle seasonality in time series data.

We will cover a range of topics related to time series modeling, including the basics of time series analysis, techniques for visualizing and exploring time series data, how to test for stationarity, and methods for fitting different types of time series models. We will also discuss strategies for selecting appropriate values for model parameters, such as the order of differencing, the order of autoregression, and the order of moving average terms.

Throughout this section, we will provide examples of how to use Python libraries like pandas, statsmodels, and matplotlib to implement time series models and visualize time series data. By the end of this section, you should have a solid understanding of the different types of time series models available, as well as the tools and techniques needed to build and evaluate these models using Python.