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Regular version of the site

Machine Learning in Economics

2020/2021
Academic Year
ENG
Instruction in English
6
ECTS credits
Course type:
Elective course
When:
4 year, 2, 3 module

Instructor

Course Syllabus

Abstract

Economists use time-series methods in many circumstances. They estimate economic models, build policy analyses and forecast economic variables. In this course we will cover some crucial concepts to establish a solid background for diving deeper in the world of time-series econometrics. For some of the methods we will go into details to learn why and how they work. We will revisit concepts like stationarity, consistency, asymptotic normality.
Learning Objectives

Learning Objectives

  • Students will feel comfortable orienting among different statistical methods and develop a feeling of why these methods work and how to extend them
Expected Learning Outcomes

Expected Learning Outcomes

  • Understand the concept of data generating process and how it is different to the concept of model
  • Learn more details on hypotheses testing and concepts like stationarity, and ergodicity
Course Contents

Course Contents

  • Opening and Intro to TS concepts
  • Probability Models and Data Generating Processes
  • Presentations and Questions
  • Asymptotic Results for Unit-root processes
Assessment Elements

Assessment Elements

  • non-blocking Exam
  • non-blocking presentation
  • non-blocking assignment
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.18 * assignment + 0.7 * Exam + 0.12 * presentation
Bibliography

Bibliography

Recommended Core Bibliography

  • Brockwell, P. J., & Davis, R. A. (2002). Introduction to Time Series and Forecasting (Vol. 2nd ed). New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=108031

Recommended Additional Bibliography

  • Ragnar Nymoen. (2019). Dynamic Econometrics for Empirical Macroeconomic Modelling. World Scientific Publishing Co. Pte. Ltd. https://doi.org/10.1142/11479