Econometrics I (2012-13 Fall, Atilim University)
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January 17. 2013 13:31:24

# Syllabus

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 hands-on 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

• Doç. Dr. H. Ozan ERUYGUR
• Gazi University, FEAS, Department of Economics, Room: 112.
• E-mail: oeruygur@gmail.com, Phone: 216 1112

Course Schedule

• Thursday              13:30-16:20         Class Room 315

Textbook

• Gujarati, D., and Porter, D. (2009) Basic Econometrics, Fifth Edition, McGraw-Hill.

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 17-21 Introduction 2 September 24-28 Central Limit Theorem (Refreshment) 3 October 1-5 Simple Linear Regression Model  1:  Ordinary Least Squares (OLS) Method; Derivation of OLS Estimator 4 October 8-12 Simple Linear Regression Model  2:  Variance and Covariances of OLS Estimators 5 October 15-19 Simple Linear Regression Model  3:  Gauss-Markov Theorem, Probability Distributions of OLS Estimators HOLIDAY (October 24-28) 6 October 29  November 2 Simple Linear Regression Model  4: Variance of Disturbance and Its Estimation, Estimation of Non-Linear Relationships. 7 November 5-9 Confidence Interval and Hypothesis Testing  1: Confidence Intervals, Hypothesis Testing 8 November 12-16 Confidence Interval and Hypothesis Testing  2: Rejection Regions, Examples for Hypothesis Testing 9 November 19-23 Goodness of Fit and Issues Related to Modeling  1: Measuring Goodness of Fit, Issues related to modeling 10 November 26-30 Goodness of Fit and Issues Related to Modeling  2: Polynomial Forms, Log-Linear 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 10-14 Multiple Linear Regression Model  2: Hypothesis Testing, Polynomial Forms, Measuring Goodness of Fit. 13 December 17-21 Inference in Multiple Linear Regression Model  1: Testing Combined Hypothesis 14 December 24-28 Inference in Multiple Linear Regression Model  2: Inference FINAL

Note: This program is subject to change if necessary.