Undergraduate Econometrics I

Prerequisite (s)
Mathematics, Mathematics for Economists, Statistics I, and Statistics II.

Course Aim
This course introduces the econometric models employed in testing and evaluating economic models.

Course Goals
Learning about economic variables and the behavioral relationships among them.
Ability to analyze the characteristics of economic models.
Learning how to transform economic models into econometric models
Learning how to estimate econometric models
Learning how to assess the results obtained from econometric models
Learning how to analyze the deviations from the assumptions of the econometric models


Course Learning Outputs and Proficiencies

The main learning output of this course is to learn how economic theory models the behavioral relationships among economic variables. In addition, the course aims at teaching how economic models are transformed into econometric models and how econometric models are estimated.

Required Readings:
—Damodar N. Gujurati (2001), Temel Ekonometri, Çevirenler: Gülay Güllük Şenesen ve Ümit Şenesen, Literatür Yayınları, İstanbul.
Suggested Readings:
— Peter Kennedy (2006), Ekonometri Kılavuzu, Çevirenler: Muzaffer Sarımeşeli ve Şenay Açıkgöz, Gazi Kitabevi, Ankara.
— Tümay Ertek (1996), Ekonometriye Giriş, Beta, İstanbul.

Assessment Criteria

Midterm 30% and Final 70%

Semester Course Plan

Week/Subjects
1
The behavioral relationships among economic variables
2
Economic Models
3
Data types, Data Sources, and Data Collecting
4
Classical Linear Regression Analysis: Two-Variable Case
5
Hypothesis Testing in Two-Variable Regression Analysis
6
Classical Linear Regression Analysis: Multiple Variable Case
7
Hypothesis Testing in Multiple Variable Regression Analysis
8
The Matrix Approach to Linear Regression Model I
9
The Matrix Approach to Linear Regression Model II
10
Deviations from the Assumptions the Classical Model: Multicollinearity
11
Deviations from the Assumptions the Classical Model: Heteroscedasticity
12
Deviations from the Assumptions the Classical Model: Autocorrelation
13
Deviations from the Assumptions the Classical Model: Model Specification Errors
14
Nonlinear Regression Models