ECTS - University of Pisa
| Modules | Area | Type | Hours | Teacher(s) | |
| QUANTITATIVE ECONOMICS FOR BUSINESS | SECS-P/05 | LEZIONI | 42 |
|
The aim of Quantitative Economics for Business is to provide an introduction to the practice of econometrics.
While both theoretical and practical aspects are covered, emphasis will be on intuitive understanding and concepts will be illustrated with real-world applications. Quantitative techniques are best learned by doing, and I will require you to do a fair amount of hands-on work.
The methods to learn from the data taught in this introductory course can be employed beyond the disciplines of business and economics (i.e, accounting, finance, marketing, management, political science and sociology).
Ongoing assessment to monitor academic progress will be carried out in the form of homework assignments.
Homework assignments will follow a biweekly schedule. Homework assignments count up to 25% of the final grade. The assignments will be posted on the E-learning channel and communicated during the lessons.
Empirical research project on a selected topic will count up to 25% of the final grade.
The final exam will count 50% of the final grade.
*Students who don't turn in the homework assignments or do not deliver the research project can take a longer final exam worth 100% of the grade
* Grades from the homework assignments and the research project will be considered only for the first three exam dates ("appelli") after the end of class. For the fourth date onwards, the exam will consist of a longer final exam.
Students who successfully complete Quantitative Economics for Business should be comfortable with basic statistics and probability.
They should be able to use a statistical/econometric computer package to estimate an econometric model and be able to report the results of their work in a non-technical and literate manner.
In particular, a student who successfully completes Quantitative Economics for Business will be able to estimate and interpret linear regression models and be able to distinguish between economic and statistical importance.
They should be able to critique reported regression results in applied academic papers and interpret the results for someone who is not trained as an economist.
Problem sets & lab sessions will be carried out in order to understand how to use the MATLAB software in applied econometric modelling.
Students are expected to have previous knowledge on Calculus, Linear Algebra, Statistics and both Micro and Macroeconomics as this foundational knowledge enriches the learning experience in Econometric methods.
Teaching methods consist of both theoretical lessons and computer lab sessions. These will show how econometrics can help answer real-world business, financial, political and economic questions, and test hypotheses about them.
Theoretical lessons will cover the algorithms, statistical procedures and conceptual insights needed to succesfully built and interpret regression models. Theory lessons will rigoruously cover the math needed, but will also offer intuitive explanations to introduce key concepts, such as variables, correlation, causality, regression, and estimation. Visual aids, such as graphs, charts, or diagrams, to illustrate concepts will be frequently employed.
In the computer lab sessions students will learn how to use the MATLAB software to manipulate data and practice the skills and techniques of econometrics.
1. Review of Probability
2. Review of Statistics
3. The Simple Linear Regression Model
4. The Multiple Linear Regression Model
5. Non-linear Regression Models
6. Instrumental Variables Regressions & Systems of Equations
7. Panel Data Models
8. Forecasting Macroeconomic Time-Series
Main Reference: Stock, J. H. & Watson, M.W. (2020): Introduction to Econometrics, 4th edition. Pearson.
Alternative Reference: Wooldridge, J. M. (2019): Introductory Econometrics: A Modern Approach. 7th edition. Cengage Learning.
The mathematical, programming, and statistical methods studied in this course provide the basic knowledge for becoming a quantitative analyst and/or a data scientist.
Data scientists and analysts that master the use econometric methods to analyze, forecast and learn from the data, are equipped to develop models and provide solutions for various problems in the industry.
The analysis and consultation tasks related to forecasting, trading, risk management, analyzing market trends, and performance evaluation that can be developed in the private sector after taking this course are in high demand and enjoy lucrative salaries.
