Preamble : The course provides a basic understanding of econometric methods and their applications to real world problems. The course begins with an overview of probability distributions and statistical inference (estimation and hypothesis testing). The course then introduces the main workhorse model of applied econometrics: the linear regression model. The course develops the theory behind the linear regression model as well as its extensions. The course also aims to provide an overview of the potential departures from the assumptions underlying the linear regression model and its solutions. The course emphasizes on conceptual understanding as well as the application of econometrics methods to real world data.
Course Content:
Review of Probability and Statistics: Probability distributions and their properties; Statistical Inference (Estimation and Hypothesis Testing); Linear Regression Model: Assumptions; Estimation and properties of Least Squares Estimators; Hypothesis Testing in Linear Regression Models; Multiple Linear Regression; Relaxing the assumptions of the linear regression model (Heteroscedasticity, Autocorrelation, and Endogeneity); Application of econometric methods to real world data.
Text Books
Stock, J. H., & Watson, M. W. Introduction to Econometrics (3rd ed.), Pearson Education Ltd, 2015.
Wooldridge, J. M. Introductory Econometrics: A Modern Approach (7th ed.), Cengage Learning, 2018.
Reference Books
Angrist, J. D., & Pischke, J-S. Mastering 'Metrics: The Path from Cause to Effect (1st ed.), Princeton University Press, 2015.
Briand, G., & Hill, R. C. Using Excel for Principles of Econometrics (5th ed.), Wiley, 2015.
Cameroon, A. C., & Trivedi, P. K. Microeconometrics using Stata (2nd ed.), Stata Press, 2022.
Gujarati, D. N. Basic Econometrics (4th ed.), McGraw-Hill, 2003.
Heiss, F. Using R for Introductory Econometrics (2nd ed.), Createspace Independent Publishing Platform, 2020.
