Get hands-on experience at the leading models of Econometrics, seeing structures, and providing methods of identification, estimation and inferences on real data-sets
Classes : 30 Days : 3 months Duration : Weekdays / Weekends
Do you wish to know how to analyze and solve business and economic questions with data analysis tools? Then this Econometrics is the right path for you, as you learn how to translate data into models to make forecasts and to support decision making.
Econometrics has become one of the most important skills that every student of Management & Engineering should acquire, if they are serious about their career.
The use of Econometrics across industries for decision making, problem solving, and driving organizational innovation makes it essential skill to develop. Econometrics is used as a competitive strategy by all successful companies. A question comes first, then data are to be collected, and then finally the model or method comes in. Depending on the data, however, it can happen that methods need to be adapted.
Experts from the field of Maths, Data Science and Management, each with over 25 years of International experience working in EU/US/Australia
Who this course is for:
People aiming at a hands-on approach to Econometrics & Decision Making
What you will learn:
- Explain the basic methods of Econometrics
- Apply different econometrics models to examples of daily life
- Interpret and carry out several basic analysis strategies (correlation, regression and distribution)
- and a Deep Dive in the various real life data sets. Check the curriculum for more details
- Review of regression and partialling out. Simultaneous inference
- Linear Structural Equations Models (SEM), Wright`s IV Principle, linear GMM
- Nonlinear SEMs, Hansen`s Euler Equations, nonlinear GMM
- Bootstrap and simulation. Inference using BS
- Binary choice models, distributional regression, and M-estimators
- Linear and non-linear panel models, basic principles
- GMM and M estimators in high dimensions. Bias correction
- Program evaluation and Treatment Effect Models (TEMs)
- Modern principles for estimation of high-dimensional models
- Post-selection and post-regularization inference in SEMs and TEMs
I have eleven plus years of experience taking training courses. I do not usually complete surveys.
Your instructor was excellent, the best I've experienced on a software subject, and I couldn't imagine him doing a better job of seamlessly walking students through a breadth of information for such complex subject like AI and ML. he did a fabulous job pacing everything and addressing student questions. I am very impressed.
The course was well structured and easy to understand. Good pace of learning.
The institute believes to provide knowledge as well as guidance in detail to each & every student.
I completed my ML course from the institute. Their international exp does help a lot !
Thanks for the training sir.