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Econometric Method-I (E-640 / ETS-640/ EFN-620)
- Instructor Name: Dr. Ahsan ul Haq Satti
- Credit Hours: 03
- PIDE School of Economics (PSE)
- E-mail: [email protected]
Prerequisites For this Course:
None
Text Book(s):
List of Topics
1. Some Basic Ideas: Statistical Concepts for Economists
- Chapter 2: Statistical Learning: Introduction to Statistical Learning with Application in R. by
- Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
- Chapter 2. Learning and Practicing Econometrics. William E. Griffiths R. Carter Hill, George G Judge
2. Estimation methods, Ordinary Least Square (OLS), Maximum Likelihood Estimation (MLE) in case of Discrete and continuous variables, Method of Moment (MM)
- Chapter 3. Introductory Econometrics, Second Edition R L. Thomas
- A Comparison of Three Estimation Methods In Linear Regression Analysis. https://download.atlantis-press.com/article/25868137.pdf
3. Classical linear regression models (K variable), Assumptions, Small and large sample Properties of Estimators, Testing Linear Restrictions, Goodness of fit.
- Chapter 3. Introduction to Statistical Learning with Application in R. by Gareth James, Daniela
- Witten, Trevor Hastie, Robert Tibshirani
- Chapter 2. Introductory Econometrics, Second Edition R L. Thomas
- Chapter 10,11. Learning and Practicing Econometrics. William E. Griffiths R. Carter Hill, George G Judge
4. Wald, Lagrange Multiplies, Likely hood ratio test
- Chapter 4. Introductory Econometrics, Second Edition R L. Thomas
5. Breakdowns in Classical Assumptions: Autocorrelation, Heteroskedasticity, Multicollinearity
- Chapter 5. Introductory Econometrics, Second Edition R L. Thomas
6. Bootstrapping Principals for Regression
- Chapter 5. Resampling Methods. Introduction to Statistical Learning with Application in R. by
- Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
7. Non-linear Regression Models
- Taylor Approximation, Direct Search methods, Iterative process Generalized least square
- Chapter 9. Econometric Analysis William H. Greene
8. Dummy variables, Limited dependent Variable: Linear probability model, Logit & Probit model, Ordered Logit and Probit, Tobit model
- Chapter 4. Classification. Introduction to Statistical Learning with Application in R. by Gareth
- James, Daniela Witten, Trevor Hastie, Robert Tibshirani
- Book. Introduction to Econometrics, 3rd Edition (MacMillan) by G.S.Maddala
9. Simultaneous Equation System: IVLS, 2SLS & Estimating the parameter of a set of Error related Economic Relation: 3SLS, Seemingly Unrelated Models:
- Chapter 17. Learning and Practicing Econometrics. William E. Griffiths R. Carter Hill, George G Judge
10. Method of Moment, Generalized method of moment
11. Latent Variables Models (SEM), Mediation, Moderation, MIMIC Modeling
- Handbook of Structural Equation Modeling, Edited by Rick H. Hoyle
12. Panel Data Model, Fixed effect, Random effect models
13. Linear Model Selection and Regularization. Shrinkage Methods: Ridge Regression, The Lasso, Dimension Reduction Methods: PCA, PLS.
- Chapter 6. An Introduction to Statistical Learning (with application in R): Introduction to Statistical
- Learning with Application in R. by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
14. Bayesian Estimation and Inference: Some basic concepts and applications.
- Chapter 24, 25. Learning and Practicing Econometrics. William E. Griffiths R. Carter Hill, George G Judge
Online Lectures
15. Introduction – Problems with Standard Econometric Methodology
16. The Encompassing Approach, and its implications for Valid Regression models
17. Methodological Mistakes and Econometric Consequence
Reference Book(s):
- Introduction to Statistical Learning with Application in R. by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
- Econometric Analysis by William H. Greene
- Learning and Practicing Econometrics. William E. Griffiths R. Carter Hill, George G Judge
- Introduction to Econometrics, 3rd Edition (MacMillan) by G.S.Maddala
- Hand book of Structural Equation Modeling, Edited by Rick H. Hoyle
General Book for Reading
- Economics Rules , Rights and Wrongs of the Dismal Science by Dani Rodrik
Online Courses R
- edX HarvardX PH125.1xData Science: R Basics
- R Programming Tutorial – Learn the Basics of Statistical Computing – YouTube
Course Description
Econometric Methods-I is a graduate-level course designed to provide a comprehensive and rigorous introduction to the theory and practice of econometrics. The course focuses on equipping students with the tools necessary to understand and apply a wide range of econometric techniques used in economic research and policy analysis. Starting from foundational statistical concepts, the course covers key estimation methods including Ordinary Least Squares (OLS), Maximum Likelihood Estimation (MLE), and the Method of Moments (MM), advancing toward more sophisticated topics such as generalized method of moments (GMM), nonlinear regression, limited dependent variable models, and simultaneous equation systems. Students will also explore diagnostic techniques for detecting and correcting model assumption violations such as heteroskedasticity, autocorrelation, and multicollinearity. Emphasis is placed on practical application using R for data handling, estimation, and inference. Additional focus is given to emerging topics in econometrics such as bootstrapping, shrinkage methods, panel data models, structural equation modeling (SEM), and Bayesian estimation.
This course bridges theoretical understanding with empirical application, preparing students to critically evaluate econometric models, conduct robust quantitative research, and contribute meaningfully to academic and policy debates.
Course Objectives
This course aims to provide advanced graduate students with a rigorous foundation in econometric theory and its application using modern statistical and computational tools. Emphasizing both classical and contemporary approaches, the course equips students with the conceptual understanding and technical skills necessary to specify, estimate, test, and interpret econometric models, with a special focus on model validity, data limitations, and robust inference. The use of R for statistical computing is integrated throughout the course to enhance empirical analysis and reproducibility.
Learning Outcomes
By the end of this course, students will be able to:
- Understand and Apply Fundamental Econometric Concepts:
- Analyze Model Validity and Diagnose Assumption Violations:
- Implement Advanced Econometric Techniques:
- Utilize Modern Computational and Statistical Tools:
- Explore Specialized Econometric Models and Techniques:
- Critically Engage with Econometric Methodologies:
Lecture Plan
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