Master 1 University of Franche-Comte - Besançon
Semester 1
Econometrics M1: 24h Course summary This course explores the basics of econometric analysis. The first chapter consists in a refresher in mathematics and statistics. The second chapter addresses hypothesis testing. The third chapter focuses on the definition of a clean empirical model, considering the different hypotheses needed for its estimation. In the fourth chapter, we present the Ordinary Least Square Estimator and its properties. Chapter 5 concludes with the concept of inference and the interpretation of an econometric model.
Course knowledge
Students should develop the ability to: – Analyze data through standard statistical methods. – Build an estimable and intelligible empirical model, to answer an economic problem. – Recognize and understand the properties of the Ordinary Least square estimator, as well as its limitation. General skills Statistical tests, linear regression model, ordinary least square estimator Specific skills Matrix algebra, asymptotical analysis, estimation with the R software.
Data Analysis/Softwares 24h
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Course knowledges Students should develop the ability to: – Recognize a clean data structure and be able to organize a dataset so that it can be exploited. – Use different visualization tools to provide a clear understanding of the data. – Perform and interpret a complete statistical analysis, starting from an economic problem. – Write a clean code that can be used by coworkers in the workplace. General skills Statistical analysis, R software and R Studio, data Visualization, application of econometric methods, redaction of a statistical report. Specific skills Tidyverse package, ggplot, multiple linear regression models. |
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| Microeconomics 18h |
| Cooperative Game Theory 15 h |
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| Theory and Practice of Auctions and Procurement 24h |
| Cartel Behaviors and Competition Policy M2 18 |
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| Cours de français renforcé 20h |
| Information Systems Management 14h |
| Performance Measurement: Key performance Indicators 12h |
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| Business Games (PT) 35h |
| Research seminar in Business / Competitive Intelligence 9h |
| Big Data, Business Intelligence 12h |