OAGE 2045 - Advanced Econometrics: Time Series

The general objectives of this course are as follows:

• to introduce students to the main time series econometric methods used in the analysis of issues related to economics and finance, in order to equip them with the knowledge required for performing quantitative analyses.

ii) Strengthen students with the use of general econometric software in empirical research using Eviews

Learning Outcomes

• Explain the defining characteristics of various types of univariate stochastic processes.
• Identify the appropriate time series model for a given data series.
• Evaluate the accuracy of predictions using various metrics.
• Explain the relative advantages and disadvantages of VAR modelling.
• Discuss the features of data that motivate the use of a VAR, or a VECM model.
• Conduct Granger non-causality tests.

Professional Skills

• Estimate univariate and multivariate time series models and produce forecasts from them in EViews
• Compare and contrast single equation and systems-based approaches to building model.

Installation of EViews 12 student version before starting the course.

Good initial knowledge in probability theory and statistics (expectation, variance, covariance, vectors and matrices, tests).

Good initial knowledge on econometrics: OLS estimation, inference and tests (Student and Fisher tests), confidence intervals.

Students should be aware that this is an Advanced course for those who have mastered the basics and are comfortable with the software.

The acquisition of all theoretical notions will be insured through the resolution of practical problems. There will be some lab component to the course. Students are also expected to spend time outside of class sessions in order to replicate the estimations since that is essential to becoming proficient in the methods discussed in this course. The data for the replications as well as lecture notes for each chapter, and solutions to problems will be posted on the course web page:

http://chrault3.free.fr/sc-po-2026.htm

A take home paper in small groups (80%), and a mark for student participation 20%

• Class Attendance: 4 hours a week / 24 hours a semester
• Online learning activities: 2 hours a week / 24 hours a semester
• Reading and Preparation for Class: 3 hours a week / 36 hours a semester
• Research and Preparation for Group Work: 2 hours a week / 24 hours a semester
• Research and Writing for Individual Assessments: 3 hours a week / 36 hours a semester