SYLLABUS: ECONOMICS 141 (Section 1)

INTRODUCTION TO ECONOMETRICS

FALL 2013

INSTRUCTOR:ProfessorCraig Gallet

OFFICE:Tahoe 3024

PHONE:278-6099; 278-5768 (Fax)

CLASS HOURS:M 5:30-8:20pm (Tahoe 1027)

E-MAIL:

OFFICE HOURS:MW 4:30-5:30pm; MW 8:15-8:45pm

WEB SITE:

PREREQUISITES:Introductory Macroeconomics and Microeconomics (i.e., ECON 1A and 1B, or their equivalents), Quantitative Economic Analysis (i.e., ECON 140 or its equivalent), and either Intermediate Macroeconomicsor Microeconomics (i.e., ECON 100A or 100B, or an equivalent)

TEXTS:Required: Using Econometrics: A Practical Guide, 6th Edition (2011), by Studenmund.

Required: Transparencies to Accompany Lectures

Recommended: Gretl (

This is a data-intensive class, and so you need an econometric software package. Gretl is a perfect fit, since it’s free and does everything you will need for this course. However, you can use whatever software package you like, provided it does the various calculations needed.

Regarding other possible software, Excel can be used to perform many of the calculations we will do throughout the semester. However, it is tedious to use and will not be able to perform calculations related to topics discussed later in the term. The university does hold site licenses for Stata, as well as Eviews, if you want to use either of them.

GRADING:

Percentage of Course Grade

Three equally-weighted exams70%

Several homework exercises30%

The following expected grade scale will be used in this class:

Percentage of Weighted Points PossibleGradeRange

90-100A’s (A, A-)

80-89B’s (B-, B, B+)

70-79C’s (C-, C, C+)

60-69D’s (D-, D, D+)

< 60F’s

Note: If scores are below expectations, the instructor reserves the right to “adjust the curve” accordingly. Also, this class will adhere to all CSUS policies regarding academic behavior.

Exams will consist of problems you will analyze using the tools learned in the course. Make sure to always have a non-programmablecalculator available. Many of the homework exercises will utilize Excel data files that you will download from my website and analyze.

Exams and homework exercises are graded on a 100-point scale. Note that no late homework’s will be accepted.

Note: The computer lab in Amador 220 is reserved for the following (tentative) date: September 23.

IMPORTANT DATES:

Exam 1:October 7

Exam 2:November 18

Exam 3:December 16 (5:15-7:15pm)

Computer Lab:September 23

No Class:November 11 (Veterans Day)

CALCULATOR: Make sure to bring a non-programmablecalculator on exam days.

PURPOSE AND OBJECTIVES:

The purpose of this course is to introduce you to the fundamentals of econometrics (i.e., regression analysis applied to economic issues). Regression is a powerful statistical tool used by many decision-makers to assess the relationships between various factors. Accordingly, the course objectives are:

 To develop an understanding of the process followed when building an econometric model.

 To apply econometric tools to data utilizing statistical software.

To develop an understanding of diagnostic tools.

LECTURE OUTLINE

I.Introduction (Chapter 1)

 Applications of econometrics

 Dependent vs. independent variables

 Types of data

 Descriptive vs. inferential statistics

Regression equation

II.Simple Regression (Chapter 2)

 Descriptive measures

 Ordinary least squares

 Measures of fit

III.Multiple Regression (Chapter 2)

Multiple regression equation

R-squared versus adjusted R-squared

  1. Data Issues (Chapter 3 and Chapter 7, Section 7.4)

Data transformations

Missing observations and proxy variables

Intercept dummy variables

V.Steps in Applied Regression (Chapter 3)

Six steps in applied regression analysis

VI.Review of Probability (Chapter 17)

Probability

Random variables and probability distributions

Measures of central tendency and dispersion

Key continuous probability density functions

VII.The Classical Model (Chapter 4)

Assumptions of the classical linear regression model

VIII.Estimation (Chapter 4 and Chapter 17)

Sampling distribution of β-hat

Unbiasedness, efficiency, consistency

Gauss-Markov theorem

IX.Hypothesis Testing (Chapter 5 (including appendix) and Chapter 17)

Null and alternative hypothesis

Type I and type II errors

Test statistic procedure

Applications to regression

X.Model Specification: The Independent Variables (Chapter 6)

Omitted relevant variables

Included irrelevant variables

Stepwise regression

XI.Model Specification: The Functional Form (Chapter 7)

Implications of incorrect functional form

Linear form

Double-log form and elasticities

Semi-log form

Polynomial form

Inverse form

Interactive terms and slope dummy variables

XII.Multicollinearity (Chapter 8)

Consequences of multicollinearity

How to detect multicollinearity

What to do about multicollinearity

XIII.Serial Correlation (Chapter 9)

First-order serial correlation

Consequences of serial correlation

How to detect serial correlation

What to do about serial correlation

XIV.Heteroskedasticity (Chapter 10)

Consequences of heteroskedasticity

How to detect heteroskedasticity

What to do about heteroskedasticity

  1. Qualitative Dependent Variables (Chapter 13)

Implications

Probit procedure

XVI.You want more? Time series techniques (Chapter 12) and Simultaneous equations techniques (Chapter 14)