Time Series and Panel Data Analysis

2024/2025

Academic Year

ENG

Instruction in English

4

ECTS credits

Delivered at:

International College of Economics and Finance

Course type:

Elective course

When:

4 year, 1, 2 module

Instructors

Budanova, Sofya

Колесников Иван Андреевич

Course Syllabus

Abstract

Time Series and Panel Data Analysis (intermediate level) is a two-module course designed for fourth year ICEF students. The course is divided into two parts. The first part covers time series theory and methods, while the second part goes over panel data analysis. Students will learn basic theoretical results and how to estimate time series and panel data in practice with the help of computational software. The course is taught in English. Course Pre-requisites: Statistics, Mathematics for Economists, Introduction to Econometrics, Introduction to Economics. You need to be comfortable using matrices.

Learning Objectives

• introduce the students to the modern methods of time series and panel data analysis
• prepare students for individual work, in particular on their bachelor's theses

Expected Learning Outcomes

• be able to estimate ADL models
• be able to estimate different time series models with the help of statistical software
• compute Arellano-Bond estimator
• compute the pooled OLS, fixed effects, and random effects estimators
• construct and estimate linear models with unobserved heterogeneous effects
• construct forecasts for macroeconomic and financial variables
• construct nonlinear models for panel data, in particular, binary choice models, and estimate those models in practice
• estimate the basic models of conditional heteroskedacticity using statistical software
• explain specifics of panel data: when it is used and what flexibility it adds to econometric models
• model dependence in conditional variance of times series data
• model the dynamics of several variables simultaneously, and analyze relations between different time series
• test data for stationarity and transform non-stationary series into stationary ones.
• understand specifics of time series data and be able to construct linear models for time series data and apply the Box-Jenkins procedure

Course Contents

• Time series: basic concepts and ARMA models: review
• ADL Models
• Nonstationary time series
• Conditional heteroskedasticity
• Multivariate time series
• Estimation and forecasting
• Panel data: Introduction
• Linear Panel Data Models
• Dynamic Panel Data Models
• Nonlinear panel models

Assessment Elements

• Midterm
• Homework
• Final Exam
  In order to get a passing grade for the course, the student must sit (all parts) of the final examination.
• Project

Interim Assessment

• 2024/2025 2nd module
  0.5 * Final Exam + 0.15 * Homework + 0.2 * Midterm + 0.15 * Project

Bibliography

Recommended Core Bibliography

• Applied econometric time series, Enders, W., 2004
• Econometric analysis of cross section and panel data, Wooldridge, J. M., 2002
• Elements of forecasting, Diebold, F. X., 2007
• Stock, J. H., & Watson, M. W. (2015). Introduction to Econometrics, Update, Global Edition (Vol. Updated third edition). Boston: Pearson Education. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1419285

Recommended Additional Bibliography

• Introductory econometrics : a modern approach, Wooldridge, J. M., 2009

Authors

• BUDANOVA Sofia ILINICHNA