Unit Root Processes
Unit root processes are a class of stochastic processes in time series analysis where the autoregressive polynomial has a root equal to one, indicating non-stationarity. This means the series has a stochastic trend, with shocks having permanent effects, and its variance grows over time. Key examples include random walks, which are fundamental in econometrics and finance for modeling variables like stock prices or GDP.
Developers should learn about unit root processes when working with time series data in fields like finance, economics, or data science, as they help identify non-stationary behavior that can invalidate standard statistical inferences. Understanding unit roots is crucial for applying techniques like differencing to achieve stationarity, testing for cointegration, and building accurate forecasting models in tools like Python or R.