The
course provides an elementary but comprehensive introduction to the practice of
econometrics. It deals with applications of statistical methods to the testing
and estimation of economic relationships. The main topics covered include the
simple linear regression model and ordinary least squares estimation (OLS),
extensions of the simple linear regression model, statistical inference, prediction, two
variable regression model, multiple regression model, estimation and inference,
specification analysis and model evaluation.
Course Description
The aim of this course is to introduce students to the study of econometrics, which deals with the application of statistical methods to test economic theory. Econometrics uses observational data to estimate economic relationships, test hypotheses about economic behaviour, and predict future values of economic variables. Software applications are introduced during the course in order to provide handson experience with data collection, analysis and interpretation.
Learning Outcomes
(1) distinguish between different types of data used in econometric analysis, (2) understand the use of econometric methods in estimating causal relationships and building models in economics and related fields, (3) estimate and interpret the results of empirical models, and (4) use econometric software in simple applications.
Lecturer
Course Schedule
Textbook
Softwares
Gretl, Eviews
Further Requirements You are expected to attend classes regularly.
Grading First Midterm Exam (%) [Date: November 15, 2012]
Second Midterm Exam (%) [Date: December 13, 2012]
Final Exam (%)
Participation/Attendance (%)
Homework (%)
Course Outline
Week 

Date 

Subject 
1 

September 1721 

Introduction 
2 

September 2428 

Central Limit Theorem (Refreshment) 
3 

October 15 

Simple Linear Regression Model 1: Ordinary
Least Squares (OLS) Method; Derivation of OLS Estimator 
4 

October 812 

Simple Linear Regression Model 2: Variance and
Covariances of OLS Estimators 
5 

October 1519 

Simple Linear Regression Model 3: GaussMarkov
Theorem, Probability Distributions of OLS Estimators 
HOLIDAY (October 2428) 

6 

October 29 November 2 

Simple Linear Regression Model 4: Variance of Disturbance and Its Estimation,
Estimation of NonLinear Relationships. 
7 

November 59 

Confidence Interval and Hypothesis Testing 1: Confidence Intervals, Hypothesis Testing 
8 

November 1216 

Confidence Interval and Hypothesis Testing 2: Rejection
Regions, Examples for Hypothesis Testing 
9 

November 1923 

Goodness of Fit and Issues Related to Modeling 1: Measuring Goodness of Fit, Issues related to
modeling 
10 

November 2630 

Goodness of Fit and Issues Related to Modeling 2: Polynomial Forms, LogLinear Models 
11 

December 3 7 

Multiple Linear
Regression Model 1: Estimation of Multiple Regression Model
Parameters, Sampling Properties of OLS Estimators, Confidence Intervals 
12 

December 1014 

Multiple Linear
Regression Model 2: Hypothesis Testing, Polynomial Forms,
Measuring Goodness of Fit. 
13 

December 1721 

Inference in Multiple
Linear Regression Model 1: Testing Combined Hypothesis 
14 

December 2428 

Inference in Multiple
Linear Regression Model 2: Inference 
FINAL 
Note: This program is subject
to change if necessary.