Econometrics#1: Regression Modeling, Statistics with EViews

Please note that, We have divided the "Econometrics" course in to TWO parts as follows:

1. Econometrics#1:  Regression Modeling, Statistics with EViews

2. Econometrics#2: Econometrics Modeling and Analysis in EViews

This is the first part and will cover mostly basics such as descriptive statistics, correlation techniques and regression analysis.

The course aims to provide quantitative/econometrics modeling skills through Descriptive Statistics, Correlation Techniques, Regression, Predictive and Econometrics Modeling skills. Quantitative methods and predictive modelling concepts could be extensively used in understanding the financial markets movements, huge datasets and statistics and studying tests and effects. The course picks theoretical and practical datasets for econometrics/quantitative/predictive analysis. Implementations are done using Eviews software. Observations, interpretations, predictions and conclusions are explained then and there on the examples as we proceed through the training. The course also emphasizes on the regression models.

The 2nd part of the course, that is Econometrics#2: Econometrics Modeling and Analysis in EViews, AIMS to also cover Auto-Correlation, Co-Integration and ARCH (Auto Regressive Conditional Heteroscedasticity) models

 Essential skillsets – Prior knowledge of Quantitative methods and MS Office, Paint

 Desired skillsets — Understanding of Data Analysis and VBA toolpack in MS Excel will be useful

The course works across multiple software packages such as Eviews, MS Office, PDF writers, and Paint. Furthermore, the course is distributed across 4 sections details of which are bulleted below, with brief description

Section 1: Eviews and Its Application to Econometrics Modelling: This course aims to provide basic to intermediate skills on implementing Econometrics/Predictive modelling concepts using Eviews software. Whilst its important to develop understanding of econometrics/quantitative modelling concepts, its equally important to be able to implement it using suitable software packages. This course fills the gap between understanding the concepts and implementing them practically

Section 2: Descriptive Statistics, Means, Standard Deviation and T-test – This course explains descriptive statistics concepts which will act as building blocks to subsequent courses

Section 3: Correlation Techniques – Correlation techniques explain relationships across variables and are important in explain the model fitment in regression courses

Section 4: Regression modelling — Regression modelling forms the core of Predictive modelling course. The core objective of this course is to provide skills in understand the regression model and interpreting it for predictions. The associated parameters of the regression model will be interpreted and tested for significance and test the goodness of fit of the given regression model