Explaining the Core Theories of Econometrics
4.3 (49 ratings)
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Explaining the Core Theories of Econometrics

This is an introductory College level econometrics course. Ideal for students who want to learn in a more intuitive way.
4.3 (49 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
417 students enrolled
Last updated 3/2013
English
Current price: $10 Original price: $30 Discount: 67% off
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Includes:
  • 4 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What Will I 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.
View Curriculum
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

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.

"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 is the target audience?
  • 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.
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Curriculum For This Course
Expand All 29 Lectures Collapse All 29 Lectures 03:51:43
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Simple Linear Regression
12 Lectures 01:52:57

Introductory lecture introducing the concept of linear regression

Preview 05:17

Lecture about the underlying intuition behind hypothesis testing, including why it is important and then an overview of how we go about it.
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
11:40

Lecture on how hypothesis testing can go wrong if our estimators are biased.
Estimator Bias
13:01

An overview of the causes of bias as well as a setup of OLS estimator's unbiasedness.
Causes of Bias
09:34

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

Estimator Variance
09:04

Mathematical derivation of OLS Decomposition formula. This decomposition will prove useful when proving OLS' unbiasedness
OLS Decomposition Derivation
11:53

Mathematical proof of OLS' unbiasedness.
OLS Estimators are Unbiased Part A
13:03

Mathematical proof of OLS' unbiasedness.
OLS Estimators are Unbiased Part B
05:02

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 A
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
04:55

Lecture on OLS estimator variance and its importance in determining which estimator we want to choose.
OLS Estimator Variance
06:41
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Multiple Linear Regression
5 Lectures 37:08
Introducing matrix notation which will, ultimately, making working in the multiple linear regression model easier.
Matrix Notation Part A
13:40

Introducing matrix notation which will, ultimately, making working in the multiple linear regression model easier.
Matrix Notation Part B
04:25

Gauss Markov assumptions in matrix notation and the multiple linear regression model context.
Gauss-Markov Assumptions
07:11

Mathematical proof of OLS' unbiasedness in matrix notation within the multiple linear regression model context.
OLS is Unbiased
06:05

OLS' estimator variance in matrix notation within the multiple linear regression model context.
OLS Estimator Variance
05:47
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Hypothesis Testing
5 Lectures 40:04
Introduction of the RSS and Wald hypothesis testing methods.
Intuition of the Two Methods for Hypothesis Testing
10:07

Lecture on some relevant notation that we need to properly look at the RSS and Wald Hypothesis Testing methods.
Notation
05:37

RSS Method in full.
RSS Method
11:18

Wald Method in full for both sigma u^2 being known and unknown
Wald Method
07:56

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.

Single Linear Restriction
05:06
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Gauss-Markov assumptions not holding...
6 Lectures 37:59
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 Introduction
07:31

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

Variable Misspecification Matrix Notation
05:01

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 Misspecification: Exclusion of a Relevant Variable
02:49

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.

Variable Misspecifcation: Inclusion of an Irrelevant Variable
02:55

A lecture on the way that A4 can not hold, specifically we can get multicollinearity!
Multicollinearity
10:51

A lecture on the way that A3 may not hold, specifically we might get heteroskedastic rather than homoskedastic errors!
Heteroskedasticity
08:52
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Section 5: The End!
1 Lecture 03:35
Concluding comments
03:35
About the Instructor
nkaizu Lectures
4.3 Average rating
49 Reviews
417 Students
1 Course
Startup education company

After studying modules like Linear Algebra, Calculus, Macro and Micro Economics as well as of course Econometrics at the London School of Economics and finding learning these subjects very difficult ourselves, we were inspired to set up an Education company: nkaizu that specialises in University level Economics and Math courses. After being successful with a Linear Algebra Course we created which got more than 100,000 views (on youtube), the next step was to work on this Econometrics course. We hope that if you are a student feeling understandably, overwhelmed by Econometrics, this will help you build a strong, intuitive foundation of understanding of the subject, which you can use to tackle preparing for your own specific Econometrics course and ultimately exam.