Below you will find teaching materials for two courses I lectured while teaching in the School of Economics in De La Salle University, Manila, Philippines. These materials rely heavily on the following textbooks: (1) Introductory Econometrics: A Modern Approach by Jeffrey M. Wooldridge; and (2) Basic Econometrics by Damodar M. Gujarati and Dawn C. Porter. Please feel free to re-use these materials with attribution to the authors.
This course introduces undergraduate economics majors to the rudiments of econometric model building with emphasis on the theoretical underpinnings and the use of the Classical Linear Regression Model. It involves discussion of research methods, the specification of economic models and building of econometric models, measurement and collection of data, estimation of model parameters, testing of relevant hypotheses, treating problems of model specification and estimation, and analysis, interpretation and presentation of econometric results.
Lecture 0: Introduction to Econometrics: Preliminaries
Lecture 1: The Nature of Econometrics and Economic Data
Lecture 2: The Simple Regression Model
Lecture 3: Multiple Regression Analysis: Estimation
Lecture 4: Multiple Regression Analysis: Inference
Lecture 5: Multiple Regression Analysis with Qualitative Information: Dummy Variables
Lecture 7: Basic Regression Analysis with Time Series Data
This course is designed to teach Economics majors a range of advanced econometric models, tools, and techniques which are widely used in the empirical literature and fields such as applied microeconomics and macroeconomics. The class aims to equip students with necessary tools to carry out empirical research in economics. Students taking this class are expected to have finished a basic econometrics course (ECONMET) and should be familiar with estimation and inference for the linear regression model. Furthermore, the class requires a working knowledge of statistics, matrix algebra, and multivariable calculus.Â
Lecture 1: Qualitative Response Regression Models
Lecture 2: The Linear Probability Model
Lecture 3: Nonlinear Probability Models: Logit and Probit Models
Lecture 4: Further Topics: Limited Dependent Variable Models
Lecture 5: Pooling Cross Sections across Time: Simple Panel Data Models and Methods
Lecture 6: Advanced Panel Data Methods
Lecture 7: Instrumental Variable (IV) Estimation and Two-Stage Least Squares (2SLS)
Lecture 8: Simultaneous Equations Models
Lecture 9: Dynamic Econometric Models: Autoregressive and Distributed-Lag Models