Econometrics Course

This is an introductory College level econometrics course. Ideal for students who want to learn in a more intuitive way.

Last updated 2022-01-10 | 4.4

- In this course we'll help you understand the key Econometric theories and in particular give you an intuitive framework to build on. Econometrics can often feel overwhelmingly complicated. This course will give you a solid foundation to prepare for your specific University or College's Econometrics exam.

What you'll learn

In this course we'll help you understand the key Econometric theories and in particular give you an intuitive framework to build on. Econometrics can often feel overwhelmingly complicated. This course will give you a solid foundation to prepare for your specific University or College's Econometrics exam.

* Requirements

* It would be ideal although not absolutely necessary if you already have an idea of basic university statistics and linear algebra.

Description

"Much clearer than my Uni's lectures!" - Unsuya Karsan

"It was really useful, very well explained and interesting. I recommend it" - Marius Meza

With rates for Econometrics tutoring starting out at about $50+ per hour, our price of $74 for over 4 hours of content offers additional value by giving you unlimited access to the material and allowing you pause, rewind, fast forward and generally review the content to increase retention.

"Excellent explanation! I'm taking an "Introduction to Econometrics" course as an undergraduate and most of the time the instructor is long on mathematics and short on intuition. I needed this video to help me grasp why estimators are biased, and you succeeded in doing just that. Job well done!" - seanch84

Our aim is to help you fully understand the key Econometrics theories so once signed up, please do not hesitate to reach out to us if you feel there are any topics that you would like more clarity on.

COURSE TOPICS COVERED

*Learn Simple and Multiple Linear Regression.

*Acquire knowledge of Gauss Markov assumptions and theory.

*Master Finite Sample Properties of Ordinary Least Squares (OLS) Method (including proof of unbiasedness).

*Become competent in Hypothesis Testing (including Normal, t, F and Chi-squared tests).

*Grasp Variable Misspecification (excluding a relevant variable, including an irrelevant variable).

*Understand Homoskedasticity and Heteroskedasticity.

"Truly outstanding. The reinforcement of the global view helped me understand the context and motivation of regression analysis. Plus, the reinforcement of the purpose of the regression intuition made the applied methods logical and easier for me to comprehend and thus learn. Nkaizu's Econometrics course taught me a lot! I wish there were a continuation of this course with advance applications. Thank you nkaizu!"- Edward Dunn

Who this course is for:

If you are studying Econometrics at university or college and would like some assistance understanding it then we can help. Our course may also be useful if you are a top student who wants to learn faster and in a more efficient manner. Either way, this course is best watched all at once to give you a complete overview of the subject in a relatively short period of time, after which you can then return to your studies.

Course content

5 sections • 29 lectures

Simple Linear Regression 12 lectures

Using mathematics to get a straight line of best fit Preview 05:17

Introductory lecture introducing the concept of linear regression

Intuition of Hypothesis Testing & OLS Formula Part A Preview 09:38

Lecture about the underlying intuition behind hypothesis testing, including why it is important and then an overview of how we go about it.

Intuition of Hypothesis Testing & OLS Formula Part B Preview 11:40

Lecture about the underlying intuition behind hypothesis testing, including why it is important and then an overview of how we go about it.

Estimator Bias Preview 13:01

Lecture on how hypothesis testing can go wrong if our estimators are biased.

Causes of Bias Preview 09:34

An overview of the causes of bias as well as a setup of OLS estimator's unbiasedness.

Estimator Variance Preview 09:04

Lecture on the intuition of estimator variance and why we care about it within the context of hypothesis testing.

OLS Decomposition Derivation Preview 11:53

Mathematical derivation of OLS Decomposition formula. This decomposition will prove useful when proving OLS' unbiasedness

OLS Estimators are Unbiased Part A Preview 13:03

Mathematical proof of OLS' unbiasedness.

OLS Estimators are Unbiased Part B Preview 05:02

Mathematical proof of OLS' unbiasedness.

Gauss Markov Theorem & Assumptions Part A Preview 13:09

The reason why we usually prefer OLS as an estimation method when we want to hypothesis test is put in the context of the Gauss-Markov theorem and assumptions.

Gauss Markov Theorem & Assumptions Part B Preview 04:55

The reason why we usually prefer OLS as an estimation method when we want to hypothesis test is put in the context of the Gauss-Markov theorem and assumptions.

OLS Estimator Variance Preview 06:41

Lecture on OLS estimator variance and its importance in determining which estimator we want to choose.

Multiple Linear Regression 5 lectures

Matrix Notation Part A Preview 13:40

Introducing matrix notation which will, ultimately, making working in the multiple linear regression model easier.

Matrix Notation Part B Preview 04:25

Introducing matrix notation which will, ultimately, making working in the multiple linear regression model easier.

Gauss-Markov Assumptions Preview 07:11

Gauss Markov assumptions in matrix notation and the multiple linear regression model context.

OLS is Unbiased Preview 06:05

Mathematical proof of OLS' unbiasedness in matrix notation within the multiple linear regression model context.

OLS Estimator Variance Preview 05:47

OLS' estimator variance in matrix notation within the multiple linear regression model context.

Hypothesis Testing 5 lectures

Intuition of the Two Methods for Hypothesis Testing Preview 10:07

Introduction of the RSS and Wald hypothesis testing methods.

Notation Preview 05:37

Lecture on some relevant notation that we need to properly look at the RSS and Wald Hypothesis Testing methods.

RSS Method Preview 11:18

RSS Method in full.

Wald Method Preview 07:56

Wald Method in full for both sigma u^2 being known and unknown

Single Linear Restriction Preview 05:06

The Wald and RSS Methods were hypothesis testing at a model level. For this lecture we introduce testing one specific linear restriction rather than the whole model.

Gauss-Markov assumptions not holding... 6 lectures

Variable Misspecification Introduction Preview 07:31

This lecture is when we start to look at what happens when things start going wrong. We ask the question what happens when our Gauss-Markov assumptions don't hold? In this lecture we focus on how A1 can not hold.

Variable Misspecification Matrix Notation Preview 05:01

A brief lecture on the relevant matrix notation we need to explore variable misspecification further.

Variable Misspecification: Exclusion of a Relevant Variable Preview 02:49

A lecture on the first way that A1 can not hold: exclusion of a relevant variable. This is the really fatal one and the one we must avoid at all costs.

Variable Misspecifcation: Inclusion of an Irrelevant Variable Preview 02:55

A lecture on the second way that A2 can not hold: inclusion of an irrelevant variable. Although by no means ideal this is most certaintly the lesser evil when compared to excluding a relevant variable.