Introduction to Applied Econometrics

Duration: 3.0 days CEU: 1.8

Presented by Dr. Oral Capps, Jr., a full professor and holder of the Southwest Dairy Marketing Endowed Chair in the Department of Agricultural Economics at Texas A&M University as well as founder and managing partner of Forecasting and Business Analytics, LLC

This course, the first in a series of three, focuses on the development and use of single-equation econometric models that enable analysts to better understand their economic/business landscape and to improve their ability to make sound economic/business forecasts. Through hands-on exercises, participants gain knowledge of the practical elements of applied econometric analysis. The overall aims are to sharpen the quantitative, statistical, and analytical skills of participants in dealing with problems and issues related to business and economics as well as to improve communication skills in reporting findings to decision makers.

Learn how to

develop and use single-equation econometric models
improve your ability to make sound forecasts
sharpen your quantitative, statistical, and analytical skills
improve the effectiveness of how you translate technical information for key decision-makers
use SAS software to conduct econometric analyses.

Who should attend

Academicians, economists, forecasters, and government and business analysts

Prerequisites

Before attending this course, you should

have a basic knowledge of SAS software, including SAS procedures such as PROC UNIVARIATE and PROC REG
have a basic knowledge of statistics, especially in conjunction with tests of hypotheses (t tests and F tests) and regression analysis
be very familiar with regression analysis
know at least the basics of economic and business concepts.

Course Contents

The Nature of Applied Econometrics

What is applied econometrics?
course of action - development of formal quantitative models
disciplines in applied econometrics
empirical models and modeling approaches
components of applied econometrics
products of applied econometrics
getting started
the Generic Multiple Regression Model
software considerations
communication and aims for the analyst

Data Considerations and Ordinary Least Squares Estimation of Single-Equation Econometric Models

data
getting a feel for the data
massaging the data
estimation of the Simple Linear Regression Model
SAS output of the simple linear regression of total personal bankruptcy (TPB) on real gros domestic product (RGDP)
estimation of the Multiple Regression Model - the Generic Single-Equation Econometric Model
example: SAS output of the demand function for shrimp

Interpretation and Use of Estimated Coefficients and Forecasting with Single-Equation Econometric Models

mathematical and statistical considerations in applied econometrics
interpretation of estimated coefficients
partial correlation coefficients
alternatives to least squares estimation
criteria for estimators
interval estimation and confidence intervals
forecasting with single-equation econometric models
forecast evaluation
illustration of out-of-sample forecasting witht he demand curve for shrimp

Common Tests of Hypotheses in Applied Econometrics

introduction: preliminary statistical elements
basics of hypothesis testing
tests of hypotheses regarding structural parameters of econometric models
tests of normality of residuals
tests of hypotheses associated with the specification of econometric models

Use of Dummy (Indicator) Variables in Applied Econometrics

intercept shifters
slope shifters
intercept shifters and slope shifters
final thoughts about the use of dummy (indicator) variables
additional readings

Diagnostic Checks - Autocorrelation or Serial Correlation

autocorrelation or serial correlation
tests for serial correlation
sample problem: the demand for shrimp
sample problem: the demand for gasoline
a test for serial correlation in the presence of a lagged dependent variable
summary remarks about the issue of serial correlation

Diagnostic Checks - Heteroscedasticity

weighted least squares (WLS)
example of econometric analysis with heteroscedasticity
multiplicative and additive heteroscedasticity
common tests of heteroscedasticity
maximum likelihood (ML) as opposed to weighted least squares (WLS)
recommended procedures to combat heteroscedasticity

Software Addressed

This course addresses the following software product(s): SAS/ETS.

Course Materials

Students receive a hardcopy of the course notes and, in some courses, can choose to take home a copy of the course data.

U.S. Schedule

12NOV2008 San Francisco, CA

08DEC2008 Chicago, IL

Check for additional and updated schedule information online at support.sas.com/courses/becin.html.

Registration

To register for this course in the US, call 800-333-7660 or visit support.sas.com/training. This course is also available for on-site training, or you can create a custom course by combining material from several courses. For more details, contact SAS Education in Cary, NC at 919-531-7321 or send e-mail to training@sas.com.