Download ECON 6818-001 Econometric Theory and Methods

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Economics 6818
Spring 1997
Instructor:
Anna Alberini
Rm. 115
492-6653
Lectures:
Office Hours:
M-W 11:30-12:45
W 9-11 and 1-2
Textbook:
Basic Econometrics by Damodar N. Gujarati, 3rd edition
Course Requirements:
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One midterm and one final exam; plus a 7-page paper describing your statistical
analysis of a set of data of your choice (each worth 33 percent of your final grade).
Weekly homework assignments (not graded, but very important in preparation for
midterm and final).
Turn in a one-page outline of your proposed empirical analysis on Monday, February
17.
The course will cover the following topics:
(1) Continuous and discrete random variables: distribution and probability functions ,
expected value, and variance. Examples based on the normal (gaussian) distribution,
exponential, log normal, binomial, and Poisson distributions.
(2) Sampling techniques: random sampling, stratified sampling, endogenous sampling,
cluster sampling. Problems caused by self-selection. Cross-sectional data, time series
data, and longitudinal data.
(3) Testing hypothesis on the mean of a population and on a population rate.
(4) The regression model with the intercept term and one independent variable:
assumptions of the classical regression model (Chapter 2 and 5), estimation of the
parameters (chapter 3), test of hypotheses on individual coefficients or joint
restrictions on coefficients (Chapter 5). Properties of the OLS estimates.
Midterm about here
(5) Tackling some practical problems in the simple regression models: regression through
the origin, scaling and units of measurement, choice of the functional form (Chapter
6), using dummy variables (Chapter 15).
(6) An introduction to linear algebra (Chapter 9).
(7) Using linear algebra with the simple regression model (Chapter 9).
(8) The multiple regression model (Chapter 9)
(9) Relaxing the assumption of the linear regression model: what happens when (i) the
independent variable is a random variable; (ii) the error terms are not normally
distributed; (iii) for large sample size; (iv) the error terms are affected by
heteroskedasticity (Chapter 11 ); (v) the error terms are serially correlated (Chapter
12).
( I 0) The probit model (Chapter 16).
Empirical analyses:
We will use SAS to perform various statistical analysis and run regressions. Specifically,
. we will learn how to:
• read in data in free- and fixed-format ASCII.
• save the data as a temporary or permanent SAS dataset.
• perform basic data cleaning, create new variables, recode old variables, take
transformations of the variables.
• create histograms, tables, descriptive statistics.
• run and interpret regressions.