Requirements

• Prior knowledge of Quantitative methods

Description

Welcome to the course on Univariate Time Series Modeling! In this comprehensive program, you'll delve into the fascinating world of time series analysis, a crucial domain for understanding and forecasting time-dependent data patterns. Throughout this course, you'll gain valuable insights into various techniques and methodologies used in modeling and interpreting time series data.

We'll start by laying the groundwork with an introduction to Univariate Time Series Modeling in Lecture 1. You'll understand the importance of time series analysis and its applications across different fields. Lecture 2 will provide a hands-on example to illustrate how Univariate Time Series Modeling is applied in real-world scenarios.

Section 1: Introduction

In this section, students will be introduced to the fundamentals of Univariate Time Series Modeling. Lecture 1 provides an overview of the concept, highlighting its significance in analyzing time-dependent data. Lecture 2 offers a practical example to illustrate the application of Univariate Time Series Modeling in real-world scenarios. Lecture 3 delves deeper into the analysis by exploring Correlogram, a key tool for understanding the autocorrelation structure of time series data.

Section 2: Correlogram Analysis

This section focuses on Correlogram Analysis, a crucial technique for examining the autocorrelation function of time series data. Lectures 4 and 5 provide comprehensive insights into Correlogram Analysis, covering its implementation and interpretation. Lecture 6 further expands on the topic by analyzing the estimation output and interpreting the results obtained from Correlogram Analysis.

Section 3: Interpretation of the ARMA Model

In Section 3, students will delve into the interpretation of the Autoregressive Moving Average (ARMA) model. Lecture 7 introduces the ARMA model and its components, while Lecture 8 continues the discussion by providing in-depth insights into interpreting the ARMA model parameters and outcomes.

Section 4: Correlogram, Estimation of Output, and ARMA Model

This section combines various concepts covered in the previous sections to provide a holistic understanding of time series modeling. Lecture 9 focuses on Correlogram and Estimation of Output Model, demonstrating how these techniques are applied in practice. Lecture 10 delves into the estimation of the ARMA model, discussing its implementation and interpretation. Lecture 11 further explores the intricacies of the ARMA model, while Lecture 12 integrates Correlogram and Estimation Output for the ARMA model, offering practical examples and insights into their combined application.

By the end of this course, you'll have a solid foundation in Univariate Time Series Modeling and the skills to analyze, interpret, and model time series data effectively. Get ready to embark on an exciting journey into the realm of time series analysis!

Who this course is for:

• "The course is designed for individuals interested in mastering time series analysis techniques using EViews software. It caters to students
• researchers
• analysts
• and professionals in fields such as economics
• finance
• business
• environmental science
• and data analytics who seek to enhance their skills in time series modeling and forecasting. Whether youre a beginner looking to understand the basics or an experienced practitioner aiming to refine your knowledge and application of time series methods
• this course offers valuable insights and practical tools to meet your learning objectives."