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Introduction to Applied Econometrics

Business Knowledge Series course

This course is not currently scheduled.

Duration: 3.0 days
Course fee:
EPTO units: .
CEUs: 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

Who should attend

Academicians, economists, forecasters, and government and business analysts

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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
This course addresses 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.

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Course fee and EPTO units will differ for on-site training.

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