Principles of Econometrics I
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This web-based course is designed to introduce working professionals and students to the concepts of basic econometrics without requiring the use of advanced matrix algebra or calculus. This course is the first in a two-course series which provides a comprehensive introduction to econometric techniques that are valuable not only to business and economics students, but to students of other social and physical sciences as well. The first course addresses the basics of regression analysis with cross-sectional data and time series data while the second course focuses on more advanced topics. The course may be completed using SAS, SPSS, Minitab, or EViews software.
Course Specifications:
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Length:
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12 weeks
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Weekly time commitment:
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Approximately 5 to 10 hours
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Prerequisites:
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Knowledge of basic algebra and basic
statistics is recommended.
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Tuition:
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US $1,595
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Additional expenses:
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Textbook and econometric software package
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Course Materials - Text and Software
The course is based upon the text, Introductory Econometrics: A Modern Approach, by Jeffrey Wooldridge. Published by South-Western Cengage Learning, the text is in its 4th Edition.
The text is available at Amazon.com
System Requirements
The minimum system requirements necessary to complete the course are as follows:
- A PC-based computer
- RAM: 1GM or more
- Processor: 1GHz 32-bit or 64-bit
- Hard disk spack: 200MB minimum
- Operating System: Microsoft Windows 2000, XP, Vista or Windows 7
- Access to the internet and an email account with the ability to send and receive large Microsoft Excel and Word files
- Adobe Reader: Version 5.0 or higher
Course Format
The course runs 12 weeks. Each week the student will read a chapter in the text and complete assigned exercises using the econometric software. A weekly self-assessment will enable the student to test his or her knowledge of the chapter material and application of the software. Students will also complete an independent project in order to apply the econometric techniques they have learned to a real world situation of personal or professional interest.
Course Content
Unit 1: The Nature of Econometrics and Economic Data
The first unit introduces the student empirical economic analysis and the structure of economic data. Basic features of the econometric software are introduced.
Unit 2: The Simple Regression Model
Unit two defines the simple regression model and introduces ordinary least squares estimates and OLS properties. Units of measurement and functional form are discussed. Expected values and variances of OLS estimators are outlined. Software techniques are demonstrated.
Unit 3: Multiple Regression Analysis: Estimation
This unit introduces the multiple regression model which employs more than one independent variable. The mechanics of using the model and interpreting its results are discussed. The implications of misspecified models are introduced and the discussion includes irrelevant variables, omitted variables and multicollinearity. Additional software analysis techniques are demonstrated.
Unit 4: Multiple Regression Analysis: Inference
Unit four covers sampling distributions; testing hypotheses about a single population parameter; confidence intervals; and testing hypotheses about a combination of parameters. Computing and interpreting t-tests, F-tests, and p-values are detailed. Additional techniques of data analysis are demonstrated using the software.
Unit 5: Multiple Regression Analysis: OLS Asymptotics
This unit discusses OLS asymptotics or the large sample properties of estimators and test statistics. The Lagrange Multiplier statistic is introduced. Additional software analysis techniques are demonstrated.
Unit 6: Multiple Regression Analysis: Further Issues
Unit 6 introduces miscellaneous issues associated with multiple regression such as the effects of data scaling; logarithmic functional form; quadratics; interaction terms; measuring goodness of fit and techniques for selecting regressors; nested models; prediction and the analysis of residuals. Additional software analysis techniques are demonstrated.
Unit 7: Multiple Regression Analysis with Qualitative Information: Binary (Dummy) Variables
The use of binary variables to include qualitative information in a multiple regression model is discussed in this unit. The Linear Probability model is introduced. Implications for policy analysis and program evaluation are demonstrated. Additional software analysis techniques are demonstrated.
Unit 8: Heteroskedasticity
Unit 8 defines heteroskedasticity and discusses its consequences for OLS. Testing for the presence of heteroskedasticity and computing heteroskedasticity-Robust statistics are detailed. The technique of Weighted Least Squares Estimation is introduced. The Linear Probability Model is revisited. More practical techniques are demonstrated using the econometrics software.
Unit 9: More on Specification and Data Issues
This unit details issues that may arise due to model misspecification or data problems. The RESET test for functional form misspecification is explained. The use of proxy variables for unobservable explanatory variables is demonstrated. Techniques for handling measurement error, missing data, nonrandom samples, and outlying observations are explained. The Least Absolute Deviations Estimation method is introduced. More practical techniques are demonstrated using the econometrics software.
Unit 10: Basic Regression Analysis with Time Series Data
Unit 10 discusses the nature of time series data and introduces static and distributed lag models. Functional form is addressed including the use of dummy variables and index numbers. Issues associated with trend and seasonality of the data are discussed. Time series techniques using the econometric software are illustrated.
Unit 11: Further Issues in Using OLS with Time Series Data
This unit defines stationary and weakly dependent time series. Asymptotic properties of OLS are outlined. The use of highly persistent time series in regression and data transformation is discussed. Dynamically complete models are explained. The homoskedasticity assumption for time series models is outlined. Additional software techniques are demonstrated.
Unit 12: Serial Correlation and Heteroskedasticity in Time Series Regressions
In this final unit, the problems of serial correlation and heteroskedasticity are addressed. Tests for identifying these problems are detailed and strategies for correcting them are outlined.
Independent Econometric Project
At the beginning of the term, students select a topic of personal or professional
interest. On a weekly basis, they will use statistical software and the new econometric skills introduced to analyze their data. At the end of the term, students submit a final
summary report which demonstrates their understanding of the concepts covered
in the course and their ability to apply these newly acquired techniques of analysis
using the statistical software.
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