Econometrics in Python

We are econometric

Econometric time series analysis involves the use of statistical methods to model and forecast time-dependent data. We’ll delve into the critical concept of stationarity, its significance, and how to handle non-stationary series using Python. We will also walk through building a Seasonal Autoregressive Integrated Moving Average (SARIMA) model, one of the most robust models for time series forecasting.

Understanding Stationarity

A time series is considered stationary if its statistical properties do not change over time. This means that its mean and variance are constant over time, and its covariance function depends only on the time difference between observations and not on the actual time at which the covariance is computed. Stationarity is crucial because many time series models, including ARIMA and SARIMA, assume that the underlying time series is stationary.

Here are some visual examples to illustrate non-stationary and stationary time series:

1. Non-Stationary Series (Increasing Mean): The mean increases over time.
2. Non-Stationary Series (Varying Variance): The variance changes over time.
3. Non-Stationary Series (Time-Varying Covariance): The covariance changes with time.

Detecting Non-Stationarity