Best Stata Courses

Find the best online Stata 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 Stata 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

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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

带有简体中文字幕的Excel中的数据分析

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

Complete STATA Workflow + Tips

Master STATA for data management, graphs and data analysis with TIPS for the best workflow.

Created by Mauricio Maroto - +10,000 students and growing!

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Students: 3909, Price: $94.99

Students: 3909, Price:  Paid

  • Most students enrolled in a Stata course: +3,800

  • Most ratings in a Stata course: +900 averaging +4,3

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Some Student Reviews are:

"STATA system display is matching my expectation." (December 2020)

"Have used Stata for advanced analysis in the past but within the first two sections of the course I got exposed to very useful tips. Kudos to the instructor for putting this together." (May 2019).

"The instructor has done a good job of introducing Stata and provides some very good examples." (March 2018).

"For someone who has never had exposure to STATA before I really enjoy the way this is taught." (December 2017).

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Hi! My name is Mauricio and I want you to be a PRO in STATA

Over the years, I've learned that STATA is a powerful data analysis software (data management, graphs and statistics):

  1. >>> If you are an undergraduate or graduate student, you may know what quantitative analysis you need, but you may experience difficulties using STATA to get those results, making your research harder.

  2. >>> If you are a professional and you already have some STATA knowledge, you can also benefit from this course by jumping straight into those sections you need the most. 

The plan of this course is to give you the BEST WORKFLOW ever.

Each video provides the best practices coupled with tips and hints that will boost your STATA work. So, less time learning STATA, and more time getting results out of it!

With more than +100 detailed lectures and +9.5 hours of video, you'll get the best way to handle STATA and you will have LIFETIME access too!

Be sure to enroll now and use all resources to get the most of it: lectures, exercises, messages and more.

See you inside,

-M.A. Mauricio M.

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

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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.

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

An Introduction to Stata

A Beginners Guide

Created by Najib Mozahem - Assistant Professor

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Students: 3183, Price: $24.99

Students: 3183, Price:  Paid

This is an introductory course to Stata. The course assumed to previous knowledge of the software nor any statistical knowledge. The course does not teach statistics. The goal of the course is to teach students about the basic functionality of Stata and how it can be used to analyze large data sets. The course contains two projects for students to work on. It also provides a step-by-step approach in covering all of the material where I go through the commands one by one. In addition to the video lectures, I have included the scripts of the lectures so that students can also study and revise the material without having to watch videos. Although Stata comes with many data sets, this course utilizes my own data sets in order to explain to students the thought process involved in collecting data.

Visualizing data using Stata

An Introduction to Graphs

Created by Najib Mozahem - Assistant Professor

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Students: 2780, Price: $24.99

Students: 2780, Price:  Paid

This course introduces the student to the graphical capabilities of Stata. The course assumes only basic knowledge of data management in Stata. The student should be familiar with the graphical user interface, as well as with loading data sets into memory. The goal of this course is to teach the student the logic of extracting meaning from data sets using visualization tools. This is accomplished by using a single data set from the start of the course up until the very end. Students will learn how to use histograms, quantile plots, and symmetry plots. In addition, students will also learn how to use these tools in order to investigate whether group differences exist. The course then introduces students to bar graphs, box plots, and dot plots, and how these graphs can be used to study differences in groups that are divided along more than one dimension. Finally, the course shows students how to produce graphs that describe the relationship between two variables. Students are taught how to decide which type of plot is best suited for their needs. Throughout the course, students will also learn how to customize the colors and shapes used in the graphs.   

Linear Regression using Stata

Theory and Application

Created by Najib Mozahem - Assistant Professor

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Students: 2410, Price: $29.99

Students: 2410, Price:  Paid

Included in this course is an e-book and a set of slides. The course is divided into two parts. In the first part, students are introduced to the theory behind linear regression. The theory is explained in an intuitive way. No math is involved other than a few equations in which addition and subtraction are used. The purpose of this part of the course is for students to understand what linear regression is and when it is used. Students will learn the differences between simple linear regression and multiple linear regression. They will be able to understand the output of linear regression, test model accuracy and assumptions. Students will also learn how to include different types of variables in the model, such as categorical variables and quadratic variables. All this theory is explained in the slides, which are made available to the students, as well as in the e-book that is freely available for students who enroll in the course.

In the second part of the course, students will learn how to apply what they learned using Stata. In this part, students will use Stata to fit multiple regression models, produce graphs that describe model fit and assumptions, and to use variable specific commands that will make the output more readable. This part assumed very basic knowledge of Stata.

The Complete R Programming for Data Science – 7 courses in 1

Beginner to Pro: Learn R programming language, R studio, ggplot2, dplyr, statistics, caret, machine learning, projects

Created by Numyard Data Science Team - Data Science educational team

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Students: 1998, Price: $89.99

Students: 1998, Price:  Paid

In The Complete R-Programming for Data Science & Statistics program, we have carefully designed 7 Full-Fledged courses into 1 Master Course of 200+ videos, 50+ R-Packages, Core Machine Learning and statistics concepts, 75+ practice problems and 2 Industrial projects

By end of this course, you will be able to solve Industry Data Science project in R starting including model building, model diagnostics and presenting actionable business insights

Here's how you will progress across the 7 courses in the Master Course:

  1. Getting started with R-programming: First, you will learn to write your own R code and perform basic programming tasks. You will begin with the base R programming course, where you will master the fundamental data structures such as vectors, lists, dataframes , understand the core programming constructs and get enough coding practice. You will also create full featured plots for data analysis using base graphics.

  2. Advanced coding with Tidyverse: Then you will move to advanced coding in R based on the tidyverse using the dplyr package. You will start using the elegant pipe syntax provided by the magrittr package and the data manipulation verbs.

  3. Data.table for data wrangling in R: Then You will move on to master the data.table package which has advanced capabilities for fast data manipulation. Data Scientists love this package for its incredible speed gains. Here, you will do fast data imports, create pivot tables and get comfortable with wrangling data. You will learn techniques to make your R code run super fast.

  4. Ggplot2 Graphics in R: Once you gather the core R programming skills, you start creating professional looking plots using the famous ggplot2 package. You will be able to create any data analysis plot. Be it box plots, scatterplots, dual axis time series plots, because you will not just learn the syntax, but also learn the underlying structure behind it.

  5. Statistical Foundations for Machine Learning: You will gain mastery over the ‘statistical foundations for machine learning’, which by itself is a full fledged statistics course. You will understand the core statistics concepts such as the law of large numbers, central limit theorem, normal distribution, how statistical significance tests such as the t-Test and ANOVA work and more, by solving multiple use cases of when and how to use them. You will know exactly how they work by following step-by-step hand computations and then implement in R to match the results. All the concepts are completely explained and demonstrated.

  6. Statistical Modeling with Linear Regression and Case Study: After mastering statistics, you will achieve professional-level R skills with linear regression. You will understand:

    1. What sort of industrial problems you can apply them on

    2. Understand the math behind it

    3. You will build the algorithm itself from scratch

    4. Learn how to interpret the results

    5. Perform post model building diagnostics

    6. Learn how to present the model results in a way that is valuable to the business and project stakeholders

7. Logistics Regression for Business and Case Problem:  You will understand: Then you learn the logistics regression with the same methodology of application, mathematics, building algorithm, interpreting results, diagnosing models and presenting insights

Professional Level Industry Projects: Finally, to gain and end-to-end professional Data Science project skills, you will solve two Industry projects -

> Predict Customer Purchase Propensity (Banking Domain)

> Predict U.S. Institute performance (Education Sector)

Throughout the program you will get interesting challenges, forum support for your queries and R-DataScience certification for your CV.

Logistic Regression using Stata

Theory and Application

Created by Najib Mozahem - Assistant Professor

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Students: 1686, Price: $29.99

Students: 1686, Price:  Paid

Included in this course is an e-book and a set of slides. The course is divided into two parts. In the first part, students are introduced to the theory behind logistic regression. The theory is explained in an intuitive way. The math is kept to a minimum. The course starts with an introduction to contingency tables, in which students learn how to calculate and interpret the odds and the odds ratios. From there, the course moves on to the topic of logistic regression, where students will learn when and how to use this regression technique. Topics such as model building, prediction, and assessment of model fit are covered. In addition, the course also covers diagnostics by covering the topics of residuals and influential observations.

In the second part of the course, students learn how to apply what they learned using Stata. In this part, students will walk through a large project in order to understand the type of questions that are raised throughout the process, and which commands to use in order to address these questions.

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

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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.

WHY ECONOMETRICS FOR BUSINESS IN R AND Python?

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.

1| THOROUGH COURSE STRUCTURE OF MOST IMPACTFUL ECONOMETRIC TECHNIQUES

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

2| BUSINESS EXAMPLES TO FOSTER INTUITION

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.

3| CHALLENGING AND INTERESTING PROBLEMS TO APPLY WHAT YOU LEARNED

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?".

4| HANDS-ON CODING

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.

***SUMMARY

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!

Diogo

Modeling Count Data using Stata

Poisson and Negative Binomial Regression Techniques

Created by Najib Mozahem - Assistant Professor

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Students: 1644, Price: $29.99

Students: 1644, Price:  Paid

Included in this course is an e-book and a set of slides. The course is divided into two parts. In the first part, students are introduced to the theory behind count models. The theory is explained in an intuitive way while keeping the math at a minimum. The course starts with an introduction to count tables, where students learn how to calculate the incidence-rate ratio. From there, the course moves on to Poisson regression where students learn how to include continuous, binary, and categorical variables. Students are then introduced to the concept of overdispersion and the use of negative binomial models to address this issue. Other count models such as truncated models and zero-inflated models are discussed.

In the second part of the course, students learn how to apply what they have learned using Stata. In this part, students will walk through a large project in order to fit Poisson, negative binomial, and zero-inflated models. The tools used to compare these models are also introduced.

The STATA OMNIBUS: Regression and Modelling with STATA

4 COURSES IN 1! Includes introduction to Linear and Non-Linear Regression, Regression Modelling and STATA. Updated Freq.

Created by F. Buscha - Professor

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Students: 1379, Price: $49.99

Students: 1379, Price:  Paid

Make sure to check out my twitter feed for promo codes and other updates (easystats3).

4 COURSES IN ONE!

Learn everything you need to know about linear regression, non-linear regression, regression modelling and STATA in one package.

Linear and Non-Linear Regression.

Learning and applying new statistical techniques can often be a daunting experience.

"Easy Statistics" is designed to provide you with a compact, and easy to understand, course that focuses on the basic principles of statistical methodology.

This course will focus on the concept of linear regression and non-linear regression. Specifically Ordinary Least Squares, Logit and Probit Regression.

This course will explain what regression is and how linear and non-liner regression works. It will examine how Ordinary Least Squares (OLS) works and how Logit and Probit models work. It will do this without any complicated equations or mathematics. The focus of this course is on application and interpretation of regression. The learning on this course is underpinned by animated graphics that demonstrate particular statistical concepts.

No prior knowledge is necessary and this course is for anyone who needs to engage with quantitative analysis.

The main learning outcomes are:

  1. To learn and understand the basic statistical intuition behind Ordinary Least Squares

  2. To be at ease with general regression terminology and the assumptions behind Ordinary Least Squares

  3. To be able to comfortably interpret and analyze complicated linear regression output from Ordinary Least Squares

  4. To learn tips and tricks around linear regression analysis

  5. To learn and understand the basic statistical intuition behind non-linear regression

  6. To learn and understand how Logit and Probit models work

  7. To be able to comfortably interpret and analyze complicated regression output from Logit and Probit regression

  8. To learn tips and tricks around non-linear Regression analysis

Specific topics that will be covered are:

  • What kinds of regression analysis exist

  • Correlation versus causation

  • Parametric and non-parametric lines of best fit

  • The least squares method

  • R-squared

  • Beta's, standard errors

  • T-statistics, p-values and confidence intervals

  • Best Linear Unbiased Estimator

  • The Gauss-Markov assumptions

  • Bias versus efficiency

  • Homoskedasticity

  • Collinearity

  • Functional form

  • Zero conditional mean

  • Regression in logs

  • Practical model building

  • Understanding regression output

  • Presenting regression output

  • What kinds of non-linear regression analysis exist

  • How does non-linear regression work?

  • Why is non-linear regression useful?

  • What is Maximum Likelihood?

  • The Linear Probability Model

  • Logit and Probit regression

  • Latent variables

  • Marginal effects

  • Dummy variables in Logit and Probit regression

  • Goodness-of-fit statistics

  • Odd-ratios for Logit models

  • Practical Logit and Probit model building in Stata

The computer software Stata will be used to demonstrate practical examples.

Regression Modelling

Understanding how regression analysis works is only half the battle. There are many pitfalls to avoid and tricks to learn when modelling data in a regression setting. Often, it takes years of experience to accumulate these. In these sessions, we will examine some of the most common modelling issues. What is the theory behind them, what do they do and how can we deal with them? Each topic has a practical demonstration in Stata. Themes include:

  • Fundamental of Regression Modelling - What is the Philosophy?

  • Functional Form - How to Model Non-Linear Relationships in a Linear Regression

  • Interaction Effects - How to Use and Interpret Interaction Effects

  • Using Time - Exploring Dynamics Relationships with Time Information

  • Categorical Explanatory Variables - How to Code, Use and Interpret them

  • Dealing with Multicollinearity - Excluding and Transforming Collinear Variables

  • Dealing with Missing Data - How to See the Unseen

The Essential Guide to Stata

Learning and applying new statistical techniques can be daunting experience.

This is especially true once one engages with “real life” data sets that do not allow for easy “click-and-go” analysis, but require a deeper level of understanding of programme coding, data manipulation, output interpretation, output formatting and selecting the right kind of analytical methodology.

In this course you will receive a comprehensive introduction to Stata and its various uses in modern data analysis. You will learn to understand the many options that Stata gives you in manipulating, exploring, visualizing and modelling complex types of data. By the end of the course you will feel confident in your ability to engage with Stata and handle complex data analytics. The focus of this course will consistently be on creating a “good practice” and emphasising the practical application – and interpretation – of commonly used statistical techniques without resorting to deep statistical theory or equations.

This course will focus on providing an overview of data analytics using Stata.

No prior engagement with is Stata needed. Some prior statistics knowledge will help but is not necessary.

Like for other professional statistical packages the course focuses on the proper application - and interpretation - of code.

The course is aimed at anyone interested in data analytics using Stata.

Some basic quantitative/statistical knowledge will be required; this is not an introduction to statistics course but rather the application and interpretation of such using Stata.

Topics covered include:

  1. Getting started with Stata

  2. Viewing and exploring data

  3. Manipulating data

  4. Visualising data

  5. Correlation and ANOVA

  6. Regression including diagnostics (Ordinary Least Squares)

  7. Regression model building

  8. Hypothesis testing

  9. Binary outcome models (Logit and Probit)

  10. Fractional response models (Fractional Logit and Beta Regression)

  11. Categorical choice models (Ordered Logit and Multinomial Logit)

  12. Simulation techniques (Random Numbers and Simulation)

  13. Count data models (Poisson and Negative Binomial Regression)

  14. Survival data analysis (Parametric, Cox-Proportional Hazard and Parametric Survival Regression)

  15. Panel data analysis (Long Form Data, Lags and Leads, Random and Fixed Effects, Hausman Test and Non-Linear Panel Regression)

  16. Difference-in-differences analysis (Difference-in-Difference and Parallel Trends)

  17. Instrumental variable regression (Endogenous Variables, Sample Selection, Non-Linear Endogenous Models)

  18. Epidemiological tables (Cohort Studies, Case-Control Studies and Matched Case-Control Studies)

  19. Power analysis (Sample Size, Power Size and Effect Size)

  20. Matrix operations (Matrix operators, Matrix functions, Matrix subscripting)

Fundamentals of Data Analysis for Big Data

This course prepares participants to begin running data analysis on databases.

Created by Illumeo Learning - Condensed and Efficient Courses for Busy Professionals

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Students: 1360, Price: $24.99

Students: 1360, Price:  Paid

This course prepares participants to begin running data analysis on databases. Both univariate and multivariate analysis are covered with a particular focus on regression analysis. Regression analysis is done in Excel, SAS, and Stata to give viewers a sense of familiarity with a variety of different software package structures. The focus in this course is on financial data though the techniques are also applicable to more general forms of data like that used in marketing or management analyses.

If you would like Continuing Education Credit (e.g. CPE, CE, CPD, etc.) for this course, it is available if you take this course on the Illumeo dot com platform under course title: Business Intelligence - Fundamentals of Data Analysis. Illumeo is certified to provide CPE in over two dozen different professional certifications covering finance, accounting, treasury, internal audit, HR, and more. However, in order to receive CPE credit the courses must be taken on an ‘approved-by-the-governing-body’ CPE platform, and for over two dozen corporate professional certifications, that is the Illumeo platform. Go to Illumeo dot com to learn more.

Master 2-Stage Least Squares Without Any Mathematics

Learn the intuition of logic of 2-Stage Least Squares Without Any Mathematics At All!

Created by David Tan - University Lecturer & Youtube Statistics Teacher

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Students: 1140, Price: $19.99

Students: 1140, Price:  Paid

Welcome to my on 2-Stage Least Squares (2SLS). This course is carefully designed for students/researchers who are learning 2SLS for the first time and who are not quantitatively inclined.

In fact, this course is entirely NON-MATHEMATICAL!

This course is perfect for learning the intuition and logic of 2SLS and its corresponding diagnostic tests before formally learning the derivation and mathematics from an econometrics course or textbook.

Moreover, this course covers the application of 2SLS and its diagnostic tests using two of the most popular econometrics software packages, Stata and EViews.

At the end of this course, the student will have a clear understanding of why 2SLS is used and how it is implemented, and be able to estimate a 2SLS model using empirical data.

The Essential Guide to Stata

The comprehensive guide to Stata! Continuously updated.

Created by F. Buscha - Professor

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Students: 957, Price: $34.99

Students: 957, Price:  Paid

Make sure to check out my twitter feed for promo codes and other updates (easystats3).

The Essential Guide to Data Analytics with Stata

Learning and applying new statistical techniques can be daunting experience.

This is especially true once one engages with “real life” data sets that do not allow for easy “click-and-go” analysis, but require a deeper level of understanding of programme coding, data manipulation, output interpretation, output formatting and selecting the right kind of analytical methodology.

In this course you will receive a comprehensive introduction to Stata and its various uses in modern data analysis. You will learn to understand the many options that Stata gives you in manipulating, exploring, visualizing and modelling complex types of data. By the end of the course you will feel confident in your ability to engage with Stata and handle complex data analytics. The focus of each session will consistently be on creating a “good practice” and emphasising the practical application – and interpretation – of commonly used statistical techniques without resorting to deep statistical theory or equations.

This course will focus on providing an overview of data analytics using Stata.

No prior engagement with is Stata needed. Some prior statistics knowledge will help but is not necessary.

The course is aimed at anyone interested in data analytics using Stata.

Like for other professional statistical packages the course focuses on the proper application - and interpretation - of code.

Some basic quantitative/statistical knowledge will be required; this is not an introduction to statistics course but rather the application and interpretation of such using Stata.

Topics covered include:

  1. Getting started with Stata

  2. Viewing and exploring data

  3. Manipulating data

  4. Visualising data

  5. Correlation and ANOVA

  6. Regression including diagnostics (Ordinary Least Squares)

  7. Regression model building

  8. Hypothesis testing

  9. Binary outcome models (Logit and Probit)

  10. Fractional response models (Fractional Logit and Beta Regression)

  11. Categorical choice models (Ordered Logit and Multinomial Logit)

  12. Simulation techniques (Random Numbers and Simulation)

  13. Count data models (Poisson and Negative Binomial Regression)

  14. Survival data analysis (Parametric, Cox-Proportional Hazard and Parametric Survival Regression)

  15. Panel data analysis (Long Form Data, Lags and Leads, Random and Fixed Effects, Hausman Test and Non-Linear Panel Regression)

  16. Difference-in-differences analysis (Difference-in-Difference and Parallel Trends)

  17. Instrumental variable regression (Endogenous Variables, Sample Selection, Non-Linear Endogenous Models)

  18. Epidemiological tables (Cohort Studies, Case-Control Studies and Matched Case-Control Studies)

  19. Power analysis (Sample Size, Power Size and Effect Size)

  20. Matrix operations (Matrix operators, Matrix functions, Matrix subscripting)

An Introduction to Factor Analysis

Theory and Application

Created by Najib Mozahem - Assistant Professor

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Students: 801, Price: $19.99

Students: 801, Price:  Paid

Included in this course is an e-book and a set of slides. The purpose of the course is to introduce students to factor analysis, when it is used and how it is used. The course does not assume the use of any specific statistical software. Therefore, this course should be of use to anyone intending interested in factor analysis. The theory is explained in an intuitive way while keeping the math at a minimum. The course starts with a simple one-dimensional example where the concepts of reliability, loadings, and eigenvalues are explained. The course then moves to two-dimensions where the concept of rotation is explained. Different rotation techniques are discussed in addition to the differences between them.

In the second part of the course, students walk through a case study in a step-by-step approach in order to see how the techniques are applied and what sort of logic is used in each step. In this part, students will walk through a large project in order to understand the type of questions that are raised throughout the process.

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

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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
•Multicollinearity
•Heteroscedasticity
•Autocorrelation

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!

Shubham

Data Management and Analysis with Stata.

2 in 1: Learn Stata and Statistics. A Comprehensive and Intuitive Guide for a Beginner.

Created by Ihsan Ullah - Assistant Professor, Finance

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Students: 383, Price: $24.99

Students: 383, Price:  Paid

Note: The course is COMPLETE now.

This course, extended over seven sections, provides a comprehensive introduction to Stata and Statistics. The aim of the course is to teach all the variables, and the relevant Stata commands, used in Statistics. These variables are nominal, ordinal, interval, and ratio variables. 

There are two alternative ways to undertake the course.

1. If you have a basic understanding of Stata, you can directly start from section 3, which teaches Data Management. You should then proceed to section 4 on Descriptive Statistics, which is common to all types of research. Section 5 analyses a relationship and interprets it between Nominal/Ordinal variables. Examples of these types of variables are gender, race, employment status, ethnicity, levels of satisfaction, customer service quality, hair color, and religion among others. Section 6 investigates a relationship and interprets it between the Nominal/Ordinal variable and the Interval/Ratio variable. Section 7 finds an effect of one Interval/Ratio variable on another Interval/Ratio variable. Examples of these types of variables are age, income, prices, exam scores, temperature, distance, and area among others.   Note: If you adopt this strategy, you may need to go back to the second section, if you have any trouble understanding a particular Stata command in sections 3, 4, 5, 6, and 7. The advantage of this strategy is you will study the more important content first.

2. Alternatively, you can follow the exact order of the course, starting from section 1 and then proceeding to the next section until you reach the section 7.  If you follow this strategy, make sure you do not give up in the middle of the course. The research shows that, and this course is not an exception, some students do not complete the entire course. In this course, the first 3 sections are meant to prepare you for the next 4 sections. Therefore, quitting in the first half of the course will deprive you of the intended benefits.   

Whichever alternative you choose, you must download the resources and practice with me during the lectures. In addition, you must attempt all exercises given at the end of each section.

Captions: Each video/lecture is accompanied by accurate captions to enhance your comprehension of the course contents.

Resources: You will be provided with a separate data set for each section to practice with me during the lectures. You will also be given a separate data set to attempt the exercises at the end of each section. You will obtain five do-files, one on data management, and the remaining four on data analysis. The only prerequisites for the course are to install Stata on your computer and remain committed.

Good Luck!

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

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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''

COURSE DESCRIPTION:

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

Easy Statistics: Linear and Non-Linear Regression

An easy introduction to Ordinary Least Squares, Logit and Probit regression and tips for regression modelling.

Created by F. Buscha - Professor

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Students: 119, Price: $34.99

Students: 119, Price:  Paid

Make sure to check out my twitter feed for promo codes and other updates.

Three courses combined. Linear and Non-Linear Regression and Regression Modelling.

Learning and applying new statistical techniques can often be a daunting experience.

"Easy Statistics" is designed to provide you with a compact, and easy to understand, course that focuses on the basic principles of statistical methodology.

This course will focus on the concept of linear regression, non-linear regression and regression modelling. Specifically Ordinary Least Squares, Logit and Probit Regression.

The first two parts will explain what regression is and how linear and non-liner regression works. It will examine how Ordinary Least Squares (OLS) works and how Logit and Probit models work. It will do this without any complicated equations or mathematics. The focus of this course is on application and interpretation of regression. The learning on this course is underpinned by animated graphics that demonstrate particular statistical concepts.

No prior knowledge is necessary and this course is for anyone who needs to engage with quantitative analysis.

The main learning outcomes are:

  1. To learn and understand the basic statistical intuition behind Ordinary Least Squares

  2. To be at ease with general regression terminology and the assumptions behind Ordinary Least Squares

  3. To be able to comfortably interpret and analyze complicated linear regression output from Ordinary Least Squares

  4. To learn tips and tricks around linear regression analysis

  5. To learn and understand the basic statistical intuition behind non-linear regression

  6. To learn and understand how Logit and Probit models work

  7. To be able to comfortably interpret and analyze complicated regression output from Logit and Probit regression

  8. To learn tips and tricks around non-linear Regression analysis

Specific topics that will be covered are:

  • What kinds of regression analysis exist

  • Correlation versus causation

  • Parametric and non-parametric lines of best fit

  • The least squares method

  • R-squared

  • Beta's, standard errors

  • T-statistics, p-values and confidence intervals

  • Best Linear Unbiased Estimator

  • The Gauss-Markov assumptions

  • Bias versus efficiency

  • Homoskedasticity

  • Collinearity

  • Functional form

  • Zero conditional mean

  • Regression in logs

  • Practical model building

  • Understanding regression output

  • Presenting regression output

  • What kinds of non-linear regression analysis exist

  • How does non-linear regression work?

  • Why is non-linear regression useful?

  • What is Maximum Likelihood?

  • The Linear Probability Model

  • Logit and Probit regression

  • Latent variables

  • Marginal effects

  • Dummy variables in Logit and Probit regression

  • Goodness-of-fit statistics

  • Odd-ratios for Logit models

  • Practical Logit and Probit model building in Stata

The computer software Stata will be used to demonstrate practical examples.

Regression Modelling

The third part provides useful practical tips for regression modelling.

Understanding how regression analysis works is only half the battle. There are many pitfalls to avoid and tricks to learn when modelling data in a regression setting. Often, it takes years of experience to accumulate these. In these sessions, we will examine some of the most common modelling issues. What is the theory behind them, what do they do and how can we deal with them? Each topic has a practical demonstration in Stata. Themes include:

  • Fundamental of Regression Modelling - What is the Philosophy?

  • Functional Form - How to Model Non-Linear Relationships in a Linear Regression

  • Interaction Effects - How to Use and Interpret Interaction Effects

  • Using Time - Exploring Dynamics Relationships with Time Information

  • Categorical Explanatory Variables - How to Code, Use and Interpret them

  • Dealing with Multicollinearity - Excluding and Transforming Collinear Variables

  • Dealing with Missing Data - How to See the Unseen

110 Quick Stata Tips

Becomes a Stata Pro! One hundred plus, no-nonsense, professional-grade tips to raise your Stata skills.

Created by F. Buscha - Professor

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Students: 104, Price: $19.99

Students: 104, Price:  Paid

Make sure to check out my twitter feed for promo codes and other updates (easystats3)

If you want to learn more about Stata but don't have a lot of time, this is the course for you!

In this course I provide 110 fast and to-the-point tips for Stata. These tips are professional grade and aimed at helping you become a Stata master! They cover a wide range of issues in data management, graphing, statistics and programming.

Each video is designed to be stand-alone and will take no more than 2 minutes.

Learn years worth of hard Stata knowledge in 2 hours!

You should have basic knowledge of Stata and do-files. If you do not have this check out my "Essential Guide to Stata".

The following themes are covered:

Data Management

  • How to create a code book

  • How to create a label book

  • How to list only variable names

  • How to describe unopened data

  • How to search in variables

  • How to drop/keep variables sequentially

  • How to check a digital data signature

  • How to verify data

  • How to compare two datasets

  • How to compare variables

  • How to use tabulate to generate dummy variables

  • How to avoid many logical OR operators

  • How to number labels

  • How to use labels in expressions

  • How to attach one value label to many variables

  • How to store single values

  • How to use Stata's hand-calculator

  • How to use text with Stata's hand-calculator

  • How to select column of data in a do-file

  • How to rectangularize data

  • How to check if variables uniquely identify observations

  • How to drop duplicate observations

  • How to draw a sample

  • How to transpose a dataset

  • How to quickly expand and interact many variables

  • How to create publication quality tables in word

  • How to create publication quality tables in excel

  • How to export regression results

Statistics

  • How to create many one-way tables quickly

  • How to create many two-way tables quickly

  • How to sort and plot one-way tables

  • How to expand data instead of using weights

  • How to contract data to frequencies and percentages

  • How to compute immediate statistics without loading data

  • How to compute elasticities

  • How to set the default confidence level

  • How to show base levels of factor variables

  • How to estimate a constrained linear regression

  • How to bootstrap any regression

  • How to interpolate missing values

  • How to compute row statistics

  • How to compute standardized coefficients after linear regression

  • How to compute faster marginal effects

  • How to reduce collinearity in polynomial variables

  • How to use contrasting margins

  • How to use pairwise comparison with margins

  • How to define the constant in a regression

  • How to visualise complex polynomial models

  • How to identify outliers from a regression

Programming

  • How to hide unwanted output

  • How to force show wanted output

  • How to hide a graph

  • How to suppress error messages

  • How to force do-files to run to the end

  • How to execute programmes outside Stata

  • How to check memory usage

  • How to reduce files sizes

  • How timestamp commands

  • How to set a stopwatch

  • How to pause Stata

  • How to debug error messages

  • How to pause for large output

  • How to add custom ado folders

  • How to create a custom user profile

  • How to add comments to do-files

  • How to loop over non-integer values

  • How to monitor a loop

  • How to show more in the results window

  • How to display coefficient legends

  • How to squish a table

  • How to use and modify the Function keys

  • How to view command sourcecode

  • How to create custom correlations

  • How to insert current time & date into log files

  • How to save interactive commands

Graphing

  • How to recover data from a graph

  • How to generate a combined graph with one legend

  • How to display RGB colors in graphs

  • How to make colors opaque

  • Why are SVG graphs useful?

  • How to apply log scaling to a graph

  • How to reverse and switch off axes

  • How to have multiple axes on a graph

  • How to display ASCII characters in graphs

  • How to graph the variance-covariance matrix

  • How to quickly plot estimated results

  • How to randomly displace markers

  • How to range plot

  • How to download word frequencies from a webpage

  • How to create a violin plot

  • How to show the Stata color palette

  • How to create custom titles

  • How to customize the look of graphs

  • How to show a correlation matrix as graphical table

  • How to plot a histogram with a boxplot

  • How to draw histograms with custom bins

  • How to graph a one/two/three-way table

  • How to recover graph code

  • How to do polar smoothing

Statistics Explained Easy 5 – STATA

Learning how to navigate through Stata. Nice tricks and pitfalls. Just the essentials and keeping it easy to remember

Created by Antonie van Voorden - Education Specialist

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Students: 86, Price: $89.99

Students: 86, Price:  Paid

Take me a long time to create this short 40 minutes course. Just teach you all the essentials.  and if you know them, you just need to find the commands you have to use (will be my next course)  and you're ready to go. 

so 

- How to use Stata

- How to use the menu's 

- What are the pitfalls (yes there are pitfalls, and I stepped in them, and I felt bad since all my data was lost...)

- what are easy to remember ways of doing everything. 

- How to learn yourself how you can do things even faster ;)

It will all be easier than you think ;) and that within 40 minutes ;)

Stata Level 1 Fundamentals of Data Analysis

Stata for complete beginners - All the basics for using Stata in quantitative data analysis

Created by Juan Sebastian Cuervo Sánchez - Research Associate - Innovations for Poverty Action

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Students: 68, Price: $24.99

Students: 68, Price:  Paid

In this introductory course, you will learn how to use Stata for data analysis. You will learn how to manipulate and create databases, manage variables and information, construct datasets from several data sources , perform simple quantitative data analysis, reproduce your work for further analysis using do-files and solve common quantitative problems found in real world scenarios in data management. The course follows a goal-oriented approach. Each lesson is oriented to solve a common problem or challenge you may find in your work or research with quantitative data. The course uses real - world exercises   to check your understanding and lessons are short to encourage your learning and commitment. The course doesn't examine statistical methods (regression analysis, logistic regression, ANOVA , etc).

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

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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.

COURSE TOPICS COVERED

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

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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

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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

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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

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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.

Prerequisites for Econometric – The Best Course Ever

Beginners to intermediate level course, providing intuitive understanding of probability theory for econometricians

Created by Sagar Raut - Professor

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Students: 1, Price: $19.99

Students: 1, Price:  Paid

This course aims at making Economics students love  and excel  at Econometrics, by giving them an intuitive understanding of the  statistical concepts that Econometrics is based. Often students dread Econometrics course at school or university, for they have lack understanding of the prerequisites. The course is meant for absolute beginners. It is especially meant for students who are planning to take up econometrics and have no prior understanding of statistics. The course creates a solid base enabling intuitive understanding of econometrics. This course can especially prove to beneficial for undergraduate/graduate students who are struggling to keep up in their econometrics courses. The course  does not require any prior understanding econometrics. Basic understanding of descriptive statistics can prove to be helpful, but is not mandatory.

The course is meant for absolute beginners. Students who  are struggling with understanding econometrics or lack an intuitive understanding of econometrics, will benefit the most from this course. There are no mathematical prerequisites required. Post this course, students will be equipped with understanding of econometrics and advance statistics easily. Course is not just beneficial for students of economics, but shall prove to be beneficial for everyone who needs to learn intermediate statistics.

This course focuses on starting from the very basics of probability theory and explains all probability distributions in depth, so as to equip students with understanding of statistics for better applications. Students who want to enter the field of research and would be requiring to use Econometric tools like Regression or ANOVA are recommended to take this course prior to taking up Econometrics.

Eviews Basics (Importing and Estimating Data)

Estimation Using Eviews

Created by Wali Ullah - Economist

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Students: 1, Price: $19.99

Students: 1, Price:  Paid

In this course, you will learn:

  • Types of Data in Economics

  • Downloading data from World Development Indicators (WDI)

  • Importing Time series, Cross-sectional and Panel Data into Eviews

  • Estimating and Interpreting OLS

  • Assumptions of Classical Linear Regression Model (CLRM)

  • Detection and Removal of Autocorrelation

  • Detection and Removal of Heteroskedasticity

  • Normality using Jorque Bera test

I have created this course in such a manner that if you follow the videos in a sequential manner and then connect the quizzes and assignment with the videos, you will have a great benefit in your professional career.

There are many types of data in economics, but in this course, I have only mentioned and discussed those data types which are mostly used for estimation and prediction when it comes to economics.

When it comes to downloading data, it is an easy yet very difficult task because most of the students don’t know from where and how they should download data. Therefore it is also discussed how and from where you can download data.

In this course, each and every step is discussed, not only discussed, but I have shown them practically. So it will be beneficial for the learners to apply them in future life.'

In the assumption of classical linear regression model, I have just discussed the assumptions and have given reference books for more understandings.