Univariate Time Series Analytics & Modeling with EViews

Overview

This course provides a comprehensive and hands-on introduction to univariate time series modeling with a strong focus on ARMA (AutoRegressive Moving Average) techniques using EViews software. Designed for learners with foundational statistical knowledge, the course enables participants to apply, analyze, and evaluate key components of time series analysis, from identifying autocorrelation patterns to building and diagnosing ARMA models. In Module 1, learners are guided through the conceptual foundation of univariate time series, including the construction and interpretation of correlograms. Using real-world data, students identify time-dependent components and analyze autocorrelation structures to determine appropriate model forms. In Module 2, the focus shifts to ARMA estimation, output interpretation, and model diagnostics. Learners interpret EViews estimation results, evaluate parameter significance, and assess residual patterns using correlograms and statistical tests such as the Ljung-Box Q test. Throughout the course, practical exercises and quizzes reinforce understanding, enabling learners to develop models that are both theoretically sound and empirically valid. By course completion, participants will be able to confidently construct and validate univariate ARMA models for real-world forecasting and analytical tasks.

Syllabus

Foundations of Univariate Time Series Modeling

This module introduces learners to the fundamental concepts of univariate time series analysis using EViews. It begins with an overview of the principles and motivations behind modeling a single time-dependent variable and continues with hands-on demonstrations using examples and real data. Emphasis is placed on understanding and constructing correlograms, interpreting autocorrelation and partial autocorrelation plots, and diagnosing model suitability through estimation outputs. By the end of this module, learners will be equipped to apply core techniques in univariate time series modeling and interpret diagnostic results to guide model refinement.

ARMA Modeling and Diagnostic Techniques

This module builds upon foundational time series concepts to guide learners through the estimation, interpretation, and validation of ARMA (AutoRegressive Moving Average) models using EViews. It emphasizes the significance of model coefficients, goodness-of-fit statistics, and diagnostic checks including correlograms and residual analysis. Through real-time demonstrations and estimation outputs, learners gain practical skills in refining time series models and ensuring their statistical adequacy for forecasting applications.