In the fifth post in this series we discussed the issues of the use of data mining and machine learning techniques. Today, I will present other commandments related to being prepared to compromise when implementing solutions based on the theory of statistics and being mindful in the interpretation of statistical significance
In the fourth post of the 10 Commandments of Applied Econometrics series we discussed the issues of keeping the solutions sensibly simple and applying model validation. Today, I will present another commandment related to data mining techniques. Use data mining reasonably. In the econometric community, data mining is a controversial and highly emotional
In the third post of the 10 Commandments of Applied Econometrics series we discussed the issue of data exploration. Today, I will present the next commandments: keep the solutions simple and use model validation. Keep the models sensibly simple Striking the right balance between simplicity and sophistication: the models created should be neither
In the second post of the 10 Commandments of Applied Econometrics series we discussed the issue of embedding statistical tools in the context of business problems. Today, I will present another commandment related to exploration and inspecting the data.
The first post of the 10 Commandments of Applied Econometrics series discussed the importance of the use of common sense and understanding of the theory of econometrics in data analysis. Today, I will present the next two commandments related to putting the statistical tools in the business context of a problem. 2. Avoid type
It is no secret at all that there is a world of difference between theoretical and applied econometrics. Every analyst, as a practising econometrician, experiences this moment in their processional career – usually at the beginning of it – when the theory acquired during their academic time clashes with the practice. They
