Best Econometrics Courses

Find the best online Econometrics Courses for you. The courses are sorted based on popularity and user ratings. We do not allow paid placements in any of our rankings. We also have a separate page listing only the Free Econometrics Courses.

Econometrics in Excel for MBA and professionals

Gentle introduction to key regression techniques using the Excel Analysis ToolPak from an experienced MBA instructor

Created by Maksym Obrizan - MBA Instructor and Business Consultant


Students: 5525, Price: $29.99

Students: 5525, Price:  Paid

There are many great Econometrics courses on Udemy which sell in thousands of copies. However, busy MBA students and professionals might not have sufficient programming experience to run econometric models in Python, R, Stata or Eviews. MS Excel, on the other hand, is a familiar and accessible tool which can be used for advanced data analytics. 

In this concise course I have carefully selected the most important econometric tools and techniques that are really needed to understand the complicated relationships that exist in your data. Linear regression is the absolute must for data analysis and Analysis ToolPak is a simple yet powerful feature available in MS Excel. The key concepts used in this course have been distilled based on my experience with many groups of business students that I have taught in three MBA programs and various industries.

Improved English subtitles (not automatically generated)

تحليل البيانات في Excel مع ترجمة باللغة العربية

Datenanalyse in Excel mit Untertiteln in deutscher Sprache


हिंदी भाषा में उपशीर्षक के साथ एक्सेल में डेटा विश्लेषण

한국어 자막으로 Excel 데이터 분석

Uchambuzi wa data huko Excel na manukuu kwa lugha ya Kiswahili

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.

Created by nkaizu lectures - Startup education company


Students: 3286, Price: $29.99

Students: 3286, Price:  Paid

"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.


*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

Econometrics for Business in R and Python

Learn Causal Inference & Statistical Modeling to solve finance and marketing business problems. Code templates included.

Created by Diogo Alves de Resende - Econometrics and Data Science enthusiast


Students: 1652, Price: $24.99

Students: 1652, Price:  Paid

Econometrics has horrible fame. The complex theorems, combined with boring classes where it feels like you are learning Greek, give every student nightmares. This course stays away from that. It will focus on (1) giving you the intuition and tools to apply the techniques learned, (2) making sure everything that you learn is actionable in your career, and (3) offer you a tool kit of peer-reviewed econometric causal inference techniques that will make you stand out and give you the ability to answer the tough questions.


In each section, you will learn a new technique. The learning process is split into three parts. The first is an overview of Use Cases. Drawing from business literature and my own experience, I will show examples where each Econometric technique has been applied. The goal here is to show that Econometric methods are actionable. The second part is the Intuition tutorials. The aim is for you to understand why the technique makes sense. All intuition tutorials are based on business situations. The last part is the Practice tutorials, where we will code and solve a business or economic problem. There will be at least one practice tutorial per section.

Below are 4 points on why this course is not only relevant but also stands out from others.


The techniques in this course are the ones I believe will be most impactful in your career. Like HR, Marketing, Finance, or Operations, all company departments can use these causal techniques. Here is the list:

  1. Difference-in-differences

  2. Google's Causal Impact

  3. Granger Causality

  4. Propensity Score Matching

  5. CHAID


Each section starts with an overview of business cases and studies where each econometric technique has been used. I will use examples that come from my own professional experience and business literature. The aim is to give you the intuition where to apply them in your current job. By the end of each intuition tutorial, you will be able to easily explain the concepts to your colleagues, manager, and stakeholders.

One of the benefits of giving actual business problems as examples is that you will find similar or even equal issues in your current company. In turn, this enables you to apply what you have learned immediately. Here are some examples:

  1. Impact of M&A on companies.

  2. Understanding how weather influences sales.

  3. Measuring the impact of brand campaigns.

  4. Whether Influencer or Social Media Marketing results in sales.

  5. Investigating the drivers of customer satisfaction.


For each section, we will have at least one real business or economic dataset. We will apply what we learned during the intuition tutorials.

Here are some examples of problems we will solve and code together:

  1. Measuring the impact of the Cambridge Analytica Scandal on Facebook's stock price.

  2. Assessing the results of giving training to employees.

  3. Challenge the idea that increasing the minimum wage decreases employment.

  4. Ranking the drivers on why people quit their jobs.

  5. Solving the thousand-year-old riddle of who came first: "Chicken or the egg?".


We will code together. In every single practice tutorial, we will start from scratch, building the code line by line. As also an online coding student, I feel this has been the easiest way to learn.

On top, the code will be built so that you download it and apply the causal inference techniques in your work and projects. Additionally, I will explain what you have to change to use in your dataset and solve the problem you have at hand.

Econometrics for Business in R and Python is a course that naturally extends into your career.


The course is packed with use cases, intuition tutorials, hands-on coding, and, most importantly, is actionable in your career.

Feel free to reach out if you have any questions, and I hope to see you inside!


Econometrics: Solved Questions and Mathematical Proofs

Step by step solutions to 60+ Econometrics Questions. Ideal for university students who are new to Econometrics.

Created by Shubham Kalra - Author, Founder and Econometrics Tutor at Eduspred


Students: 582, Price: $49.99

Students: 582, Price:  Paid

'Econometrics: Solved Questions and Mathematical Proofs' is a course for anyone studying Introductory Econometrics at University Level.

What other students are saying about this course?
''Clear and well organised course'' - Johnson Nyella
''This is surely very helpful. Whenever I have some doubt regarding concepts, I go through this course and the questions help a lot'' - Ananya Nath

Most of the times, even if students understand the Econometrics concepts, they struggle with connecting the dots. Consequently, they end up getting confused and make silly mistakes in the exam. This course can help you in building a strong foundation of Econometrics so that you could avoid that confusing state of mind and ace your exam.

This course contains solutions to exam style questions for the following topics:
•Hypothesis Testing and Confidence Intervals
•Simple Linear Regression
•Multiple Linear Regression
•Functional forms
•Dummy Variables

This course comes with:

  • A 30 day money-back guarantee.

  • Support in the Q&A section - ask me if you get stuck!

I really hope you enjoy this course!


Econometrics: Simple Linear Regression (University Students)

Step by step explanation to the concepts of simple linear regression. Ideal for students who are new to Econometrics.

Created by Shubham Kalra - Author, Founder and Econometrics Tutor at Eduspred


Students: 127, Price: $49.99

Students: 127, Price:  Paid

This course is the key to build a strong foundation for the Econometrics module. This is what some of our students have to say about this Econometrics course:

''The videos are more detailed as compared to how I was taught Econometrics at school. He explains everything in a calm, not so rushed manner''

''I am taking Econometrics this year at the University and this course makes a lot of sense. Everything is explained in a step by step and detailed manner''


Many students who are new to Econometrics describe it as a difficult subject.
Do you think the same?
Do you get lost among the mathematical equations and notations? 

If yes, then this is the first thing that you need to do - 'Change your approach to study this subject'

Take it from me, Econometrics is quite an interesting subject. Whether you like it or not depends on how you tackle this subject. If you want to master Econometrics, then you need to understand the intuition of each and every concept and the logic behind each and every equation. And this is what I am going to help you with!!

In this course, I will take you through:

1) All the equations that you will encounter in Simple Linear Regression (Population side as well as the sample side)

2) The method of Ordinary Least Squares

Econometrics A-Z: Explaining Theories, Models and Functions

Hypothesis Testing, Regression, Correlation, Eviews, Predictive and Econometric Modeling, and Descriptive Statistics

Created by Sayed Sekandar Sadat - Researcher


Students: 63, Price: $29.99

Students: 63, Price:  Paid

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 not only give you a solid foundation to prepare for your specific university or College's Econometrics exam but apply econometric models in real scenarios.

Over 26 hours of content offers exceptional value by giving you unlimited access to the material and allowing you to pause, rewind, fast forward, and generally review the content to increase retention.


Chapter 1- Sample moments and Software: Before we begin this course, we will look at sample moments (numbers you can calculate from a sample) and introduce some econometric software.

Chapter 2 - Least squares principle: This chapter introduces the least squares principle. The basic problem is how to fit a straight line through a scatter plot. We will cover the ordinary least squares (OLS) formula which will provide us with an intercept and a slope. We will the derive the OLS formula from the least squares principle. This chapter focuses on the algebra of least squares. There is no probability theory or statistics in this chapter. Important concepts introduced in this chapter: Trendline, residuals, fitted values and R-squared. In addition to Excel, we will also use demonstrate how to find trendlines using EViews and Stata.

Chapter 3 - Introduction to probability theory: We now know how to fit a straight line through a scatter plot. The next step is to introduce appropriate assumptions on how our data was generated. We will model our data as a random sample. More specifically, we will model our data as drawings from random variables. This idea turns out to be very fruitful. Random variables are concepts in probability theory which this chapter is about. This chapter covers the absolute minimum from probability theory that we need to progress: random variables, distribution functions, expected value, variance, covariance and conditional expectations.

Chapter 4 - The linear regression model with one explanatory variable: This chapter formalizes the most important model in econometrics, the linear regression model. The entire chapter is restricted to a special case, nameley when you have only one explanatory variable. The key assumtion of the linear regression model, exogeneity, is introduced. Then, the OLS formula from chapter 1 is reinterpreted as an estimator of unknown parameters in the linear regression model. This chapter also introduces the variance of the OLS estimator under an important set of assumptions, the Gauss-Markov assumptions.

Chapter 5 - Inference in the linear regression model with one explanatory variable: Inference means something like "a conclusion reached on the basis of evidence and reasoning". We now know how to estimate the parameters of the linear regression model (with one explanatory variable). However, these estimates are uncertain. In this section, we see what conclusions we can draw from all of this. But first, we must investigate a few more distributions (in addition to the normal distribution).

Chapter 6 - The linear regression model with several explanatory variable: In this chapter, we allow for several explanatory variables. We begin by setting up the linear regression with several explanatory variables including the assumptions that we need to make. As in the simpler model with one explanatory variable, the main focus is on estimating the beta-parameters. However, we will no longer be able to present general formulas, such as the OLS formula for our beta-estimates. To do this, we need matrix algebra which is outside the scope of this course. Instead, we rely on the fact that they have been correctly programmed into software such as Excel, EVies, Stata and more. Once we have fully understood the general linera regression model, we move on to inference.

Chapter 7 - Nonlinear and logarithmic regression model: So far, the dependent variable has been modeled as a linear function of the explanatory variables plus an additive error term. In this section, we will look at nonlinear models. First, we look at general non-linear models. Then, we focus on the most important class of non-linear models, logarithmic models.

Chapter 8 - Dummy variables: If all observations belong to one out of two groups, then a dummy variable can be used to encode this information. A dummy variable will take the value zero for all observations belong to one group and one for all the remaining observations belonging to the other group. We can use a dummy variable as an explanatory variable in a linear regression model in the same way that we use an ordinary explanatory variable. Dummy variables can be used even if you have more than two groups.

Chapter 9 - Heteroscedasticity: Heteroscedasticity means that the variance of the error term is different between different observations and this is very common in economics. We begin by looking at tests helping us figuring out if our data is homoscedastic or heteroscedasticity. If we find that we have heteroscedasticity, then the standard errors derived by assuming homoscedasticity are no longer valid. Instead, we can use robust standard errors. Also, with heteroscedasticity OLS is no longer efficient. In this case, the efficient estimator is called the weighted least squares.

Chapter 10 - Endogeneity and instrumental variables: In this chapter we will look at cases when explanatory variables cannot be expected to be exogenous (we then say that they are endogenous). We will also look at the consequence of econometric analysis with endogenous variables. Specifically, we will look at misspecification of our model, errors in variables and the simultaneity problem. When we have endogenous variables, we can sometimes find instruments for them, variables which are correlated with our endogenous variable but not with the error term. This opens for the possibility of consistently estimate the parameters in our model using the instrumental variable estimator and the generalized instrumental variable estimator.

Chapter 11 - Time series models

Chapter 12 - Models based on panel data

Introductory Applied Econometrics

An Introduction to Potential Outcomes, Regression, IV, DiD, RDD, Panel Data, Matching,Synthetic Control and Applications

Created by Econ Academy - Economist & LSE MSc Grad


Students: 42, Price: $19.99

Students: 42, Price:  Paid

This course, jointly with the Introductory Econometrics course, provides the most comprehensive and serious overview of first-year Econometrics available, to date, on Udemy.

I don't have to be here. But if I am going to be here, I am going to do it right and set the benchmark as to how Economics should be taught. Because I take students and their exams and their personal development seriously (on that note, feel absolutely free to reach out for any question or doubt that may arise as you delve into the material). Because, when I was taken seriously by my professors, everything became clearer and more engaging. Because the world is in dire need of engaged, curious people who act according to the brains instead of their stomach, people who let serious social science guide their gaze upon the surrounding world instead of random nonsense. A thoughtful world is a better world. I am strongly convinced that a serious study of proper economics helps moving toward that end.

In this course, I set out to complete the first-year sequence by introducing the main methods in Applied Econometrics. Its purpose, shared by the Introductory Econometrics course too, is to lay the foundations for deeper and more comprehensive studies in Econometrics. Hence, I spend little time dwelling on the mathematical derivation and statistical nuances, covering only the bare minimum, and more time trying to convey the intuition, the concept and, essentially, why should you care. Namely, after having introduced the Potential Outcomes Framework, I set out to illustrate Randomised Experiments (RCTs), Linear Regression, Instrumental Variables (IVs), Difference-in-Differences (DiD), Regression Discontinuity Design (RDD) and provide a brief overview of Panel Data, Matching and Synthetic Control. I cap it off by providing you with a list of Applications, real world examples of what kind of questions these models allow you to address, in the hope that they fire your curiosity up. Which is the only thing that matters.

Basic & Advanced Methods in Econometrics

Regression Analysis and Statistical Modeling

Created by Germinal Van - Instructor of Econometrics


Students: 34, Price: $19.99

Students: 34, Price:  Paid

This course is essentially designed for economics students. Econometrics is a mandatory course to be completed in every Economics curriculum at university. The core classes an economics student has to complete in order to graduate are Microeconomics, Macroeconomics, Mathematical Economics, and Econometrics. And Econometrics is known to be the most difficult of all.

Econometrics has the reputation of being a fearful discipline and many students have been intimidated by the subject. Econometrics is used everywhere and in almost every professional industry on a daily basis to make decisions. Businesses use it to decide how to promote a product and their brand, schools use it to determine their admission rate, the government uses it to predict voting patterns and outcomes, hospitals use it to predict the spread of diseases, Banks and hedge funds use it to determine the price of the stock market and the trend of financial markets...etc. Econometrics is part of our lives.

The primary goal of this course is to demystify the field of Econometrics. It is meant to facilitate the understanding of complex econometric subjects. This course is structured to provide the foundational knowledge required to do econometric analysis and statistical modeling. It teaches the basic and advanced methods of econometric modeling.

Learn Econometrics: The (Basic) Econometrics Course

A Course For Beginners

Created by Pedro Planas - Profesor y Coach de Idiomas


Students: 25, Price: $19.99

Students: 25, Price:  Paid

You like econometrics and would like to start learning about it? Do you find interesting the world of economy and finances? Then this is the COURSE FOR YOU! With Learn Econometrics: The (Basic) Econometry Course, you will learn some basic aspects of econometrics and we will make it as fun as possible for you so that you do not feel you are watching a boring black and white book but instead a nicely colored videocourse made for beginners in econometrics

Hands-on Econometrics for Beginners and Advanced Users

Comprehensive tutorials on hands-on applied econometrics using Stata and EViews analytical software

Created by Bosede Ngozi ADELEYE - Hands-on Econometrics (Using Stata/EViews) Tutor


Students: 17, Price: $89.99

Students: 17, Price:  Paid

Hands-on Econometrics for Beginners and Advanced Users (H.E.B.A.) is strictly about practical econometrics. A “Do-As-I-Do” approach is adopted to engage students, enrolees and users. This HEBA course will cover topics from beginner category to advanced econometrics with practical real-life applications. You will be able to finish your dissertations/theses and manuscripts and interpret your results with greater confidence. Topics will include but not limited to the following: Classical Linear Regression Model, Dummy Variables, Causality Models, Limited Dependent Variable Models, Cointegration and Error Correction Models, Time-Varying Coefficient Models, Dynamic Heterogeneous Panels, Threshold Analysis, Quantile and Inter-Quantile Analysis, Distributed Lag Models, Structural Equation Models, Time Series Modelling and Forecasting, Duration Models, Modelling Long-Run Relationships, Modelling Volatility and Correlation, Switching and State Space Models, Traditional Panel Data Models, Panel Cointegration, Difference-in-Difference Models, Cross-Sectional Analysis…and so much more!

At the end of my course, students will be able to proficiently use both Stata and EViews analytical packages. In addition, they will be able to apply the knowledge gained to specify econometric models that fit their research hypotheses, analyse data, interpret results and complete their theses/manuscripts (publish in reputable Journals) within the shortest possible time frame. Specifically, the learning outcomes are:

1) Students will be able to use the EViews and Stata econometric software with much confidence.

2) Students will be able to learn and apply different estimation techniques.

3) Enrollees will be able to construct and interpret econometric models.

3) Students will improve on their analytical and results interpretation skills.

5) Improved report writing skills

6) Self-testing assessments

7) Increased confidence to publish research outputs in reputable high-impact Journals.