Univariate Time Series Analytics & Modeling with EViews

Build practical skills in univariate time series analysis by learning how to apply and evaluate ARMA (AutoRegressive Moving Average) models using EViews. This course is designed for learners with foundational statistical knowledge who want to develop reliable time series models through hands-on analysis and model diagnostics. You will begin by exploring the fundamentals of univariate time series modeling, including the interpretation of correlograms, autocorrelation, and partial autocorrelation using real-world data in EViews. As you progress, you will learn how to estimate ARMA models, interpret estimation outputs, evaluate parameter significance, and assess model performance using residual analysis, correlograms, and the Ljung-Box Q test. Through practical demonstrations, exercises, and quizzes, you will strengthen your ability to identify suitable model structures, validate model adequacy, and refine models using statistical evidence. By the end of the course, you will be able to construct, interpret, and evaluate univariate ARMA models in EViews for forecasting and analytical applications, building a solid foundation in time series modeling.