Introduction to Applied Econometrics
Business Knowledge Series course
Duration: 3.0 days
Course fee: $2,175
EPTO units: 4.2
CEUs: 1.8
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Presented by Oral Capps, Ph.D., 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 single-equation econometric models that enable analysts to better understand their economic/business landscape. Participants gain knowledge of the practical elements of applied econometric analysis.
Learn how to
- develop and use single-equation econometric models
- improve your ability to make sound forecasts
- sharpen your quantative, statistical, and analytical skills.
Who should attend
Academicians, forecasters, and government and business analysts
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Course Contents
The Nature of Applied Econometrics
- what is applied econometrics?
- course of action - development of formal quantitative models
- the nature of econometrics
- components of applied econometrics
- products of applied econometrics
- data and disciplines in applied econometrics
Getting Started
- the model specification phase
- generic multiple regression model
- software considerations
- importance of communication and aims for the analyst
Mathematical and Statistical Considerations of Applied Econometrics
- mathematical considerations
- statistical considerations
- the simple linear regression model
- the multiple regression model - the generic single equation econometric model
- multiple regression model - another illustration
- goodness-of-fit statistics and model selection criteria
- estimates of error variance and variances/covariances of OLS parameter estimates
- key points of econometric models
- forecasting with single-equation econometric models
Common Tests of Hypotheses in Applied Econometrics
- introduction: preliminary statistical elements
- basics of hypothesis testing
- tests of normality of residuals
- tests of hypotheses regarding structural parameters of econometric models
- nonlinear combinations of coefficients (x2-tests)
Use of Indicator or Dummy Variables in Applied Econometrics
- intercept shifters
- slope shifters
- intercept shifters and slope shifters
- final thoughts about the use of indicator or dummy variables
Diagnostic Checks - Autocorrelation or Serial Correlation
- autocorrelation or serial correlation
- tests for serial correlation
- 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
Diagnostic Checks - Multicollinearity, Collinearity, and Ill-Conditioning
- collinearity diagnostics
- solutions to the collinearity problem
Diagnostic Checks - The Detection and Assessment of Data Outliers
- influence diagnostics
- solutions to the problem of influential observations
- robust regression techniques
Structural Change and Stability of Structural Coefficients
- diagnostic tests for structural change
- illustration of sequential Chow tests
- illustration of the Farley, Hinrich, and McGuire test
- illustration of recursive coefficients
- illustration of recursive residuals, CUSUM, and CUSUMSQ tests
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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