Find online courses made by experts from around the world.
Take your courses with you and learn anywhere, anytime.
Learn and practice realworld skills and achieve your goals.
"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 Chisquared 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
Not for you? No problem.
30 day money back guarantee.
Forever yours.
Lifetime access.
Learn on the go.
Desktop, iOS and Android.
Get rewarded.
Certificate of completion.
Section 1: Simple Linear Regression  

Lecture 1  05:17  
Introductory lecture introducing the concept of linear regression 

Lecture 2  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. 

Lecture 3  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.  
Lecture 4  13:01  
Lecture on how hypothesis testing can go wrong if our estimators are biased. 

Lecture 5  09:34  
An overview of the causes of bias as well as a setup of OLS estimator's unbiasedness. 

Lecture 6  09:04  
Lecture on the intuition of estimator variance and why we care about it within the context of hypothesis testing. 

Lecture 7  11:53  
Mathematical derivation of OLS Decomposition formula. This decomposition will prove useful when proving OLS' unbiasedness 

Lecture 8  13:03  
Mathematical proof of OLS' unbiasedness. 

Lecture 9  05:02  
Mathematical proof of OLS' unbiasedness.  
Lecture 10  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 GaussMarkov theorem and assumptions. 

Lecture 11  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 GaussMarkov theorem and assumptions.  
Lecture 12  06:41  
Lecture on OLS estimator variance and its importance in determining which estimator we want to choose. 

Section 2: Multiple Linear Regression  
Lecture 13  13:40  
Introducing matrix notation which will, ultimately, making working in the multiple linear regression model easier. 

Lecture 14  04:25  
Introducing matrix notation which will, ultimately, making working in the multiple linear regression model easier.  
Lecture 15  07:11  
Gauss Markov assumptions in matrix notation and the multiple linear regression model context. 

Lecture 16  06:05  
Mathematical proof of OLS' unbiasedness in matrix notation within the multiple linear regression model context. 

Lecture 17  05:47  
OLS' estimator variance in matrix notation within the multiple linear regression model context. 

Section 3: Hypothesis Testing  
Lecture 18  10:07  
Introduction of the RSS and Wald hypothesis testing methods. 

Lecture 19  05:37  
Lecture on some relevant notation that we need to properly look at the RSS and Wald Hypothesis Testing methods. 

Lecture 20  11:18  
RSS Method in full. 

Lecture 21  07:56  
Wald Method in full for both sigma u^2 being known and unknown 

Lecture 22  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. 

Section 4: GaussMarkov assumptions not holding...  
Lecture 23  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 GaussMarkov assumptions don't hold? In this lecture we focus on how A1 can not hold. 

Lecture 24  05:01  
A brief lecture on the relevant matrix notation we need to explore variable misspecification further. 

Lecture 25  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. 

Lecture 26  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. 

Lecture 27  10:51  
A lecture on the way that A4 can not hold, specifically we can get multicollinearity! 

Lecture 28  08:52  
A lecture on the way that A3 may not hold, specifically we might get heteroskedastic rather than homoskedastic errors! 

Section 5: Section 5: The End!  
Lecture 29 
Concluding comments

03:35 
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.