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EC226 Econometrics Course at the University of Warwick: Overview, Syllabus, Key Details and Course Plan

Discover the world of econometrics at the University of Warwick with EC226, a comprehensive course designed to equip students with the necessary skills to analyze economic data and draw meaningful conclusions. This course delves into the principles and techniques of econometrics, combining economic theory with statistical methods to provide a robust foundation for understanding and interpreting economic phenomena.



Course Overview: EC226 is an intermediate-level course that explores the application of statistical methods to economic data. It is suitable for students who have completed foundational courses in both economics and statistics. The course aims to bridge the gap between theory and real-world data, offering a hands-on approach to econometric analysis.



Syllabus: The syllabus covers a range of topics essential for mastering econometrics. Please note that the following syllabus is a general template and may not reflect the current content of EC226 at the University of Warwick:

  1. Introduction to Econometrics

  • Basic concepts and principles

  • Types of economic data

  1. Simple and Multiple Regression Analysis

  • Linear regression models

  • Estimation and hypothesis testing

  1. Violations of Classical Assumptions

  • Multicollinearity, heteroscedasticity, and autocorrelation

  • Remedies and diagnostics

  1. Time Series Econometrics

  • Time series data analysis

  • Autoregressive Integrated Moving Average (ARIMA) models

  1. Panel Data Analysis

  • Pooled, fixed effects, and random effects models

  • Applications in economics

  1. Instrumental Variables

  • Endogeneity and instrumental variable estimation

  • Two-stage least squares (2SLS) method



Assessment: The course assessment may include a combination of exams, assignments, and possibly a final project, allowing students to apply their econometric skills to real-world scenarios.



Why Choose EC226 at the University of Warwick?

  • Experienced faculty with expertise in both economics and statistics.

  • Practical applications and case studies for a hands-on learning experience.

  • Access to cutting-edge research and resources in econometrics.





COMPLETE COURSE PLAN OFFERED BY SOURAV SIR'S CLASSES


This comprehensive course is designed to provide students with a strong foundation in econometric methods, enabling them to analyze and interpret economic data effectively. Through a combination of theoretical concepts and practical applications, students will gain the skills necessary to make informed decisions in the field of economics.


Course Duration:

  • Semester: 12 weeks


Week 1-2: Introduction to Econometrics

  • Overview of econometrics and its applications in economics.

  • Basic statistical concepts: mean, variance, correlation.

  • Introduction to probability and probability distributions.

  • Simple and multiple regression analysis.

Week 3-4: Linear Regression Model

  • Assumptions and diagnostics in linear regression.

  • Estimation methods: OLS (Ordinary Least Squares).

  • Hypothesis testing and confidence intervals.

  • Model specification and interpretation of coefficients.

Week 5-6: Violations of Classical Assumptions

  • Multicollinearity, heteroscedasticity, and autocorrelation.

  • Remedies and robust regression techniques.

Week 7-8: Time Series Analysis

  • Introduction to time series data.

  • Autoregressive (AR) and moving average (MA) models.

  • Stationarity and unit root tests.

  • Introduction to forecasting.

Week 9-10: Panel Data Analysis

  • Overview of panel data and its advantages.

  • Fixed effects and random effects models.

  • Hausman test and model selection.

Week 11-12: Instrumental Variables (IV) and Causality

  • Endogeneity issues in regression models.

  • Two-stage least squares (2SLS) estimation.

  • Granger causality and other tests for causality.

Week 13-14: Advanced Topics

  • Limited dependent variable models: Probit and Logit.

  • Time series econometrics: ARCH/GARCH models.

  • Machine learning techniques in econometrics.

Week 15: Review and Exam Preparation

  • Comprehensive review of course content.

  • Practice exams and discussion of sample problems.

Assessment:

  • Weekly assignments and problem sets (30%)

  • Midterm examination (20%)

  • Group project on applied econometrics (20%)

  • Final examination (30%)

Resources:

  • "Introductory Econometrics" by Jeffrey M. Wooldridge

  • "Econometric Analysis of Cross Section and Panel Data" by Jeffrey M. Wooldridge

  • Online resources, datasets, and software tutorials.



By the end of EC226, students will have a solid foundation in econometrics, enabling them to apply statistical methods to economic data and contribute to empirical research in economics. The course emphasizes practical skills and critical thinking, preparing students for advanced studies or careers in various fields such as finance, policy analysis, and research.

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