This course is part of HSE University Master of Business Analytics and Master of Finance degree programs. Learn more about the admission into the program and how your Coursera work can be leveraged if accepted into the program here (Master of Business Analytics) and here https://inlnk.ru/WRM4O (Quantitative Finance)
The course builds essential skills necessary for economic, business or financial analysis. The purpose of the course is to give students solid and extended skills in both econometric tools and their application to contemporary economic problems. We will learn both theoretical foundations and practical aspects of the main econometric topics: ordinary least squares as a core approach of linear regression analysis, the choice of the model specification, dealing with main problems of econometric analysis such as multicollinearity, heteroscedasticity, autocorrelation and endogeneity.
After the course, you will be able to perform your own economic data analysis based on the understanding of described econometrics tools. You will learn how to apply the indicated tools and methods to various topics of your research and how to prevent and overcome problems, which can arise in real data analysis.
The course builds essential skills necessary for economic, business or financial analysis. The purpose of the course is to give students solid and extended skills in both econometric tools and their application to contemporary economic problems. We will learn both theoretical foundations and practical aspects of the main econometric topics: ordinary least squares as a core approach of linear regression analysis, the choice of the model specification, dealing with main problems of econometric analysis such as multicollinearity, heteroscedasticity, autocorrelation and endogeneity.
After the course, you will be able to perform your own economic data analysis based on the understanding of described econometrics tools. You will learn how to apply the indicated tools and methods to various topics of your research and how to prevent and overcome problems, which can arise in real data analysis.