ECON2206: Introductory Econometrics
Week 3 Tutorials
• You will need STATA for this tutorial. If you are attending face2face tutorials is recommended you bring your laptop. For all tutorial you should have STATA installed or have the ability of accessing STATA trough the UNSW myAcess service. Please check if this is the case before the class.
• Prior to attending create a work folder on your computer, say F:\IEwork, and copy the following files to that directory lecture1. do; wage1. dta; lecture1alt. do; wage1 . raw; meap93. dta; meap93. des; Ch2 C6. do; charity. des; charity. dta and Ch2 C7 . do. You can find them all on Moodle in the Data and do-files section. Feel free to try the questions beforehand. If you save the file in another folder be sure you know in which folder is saved.
Readings
• Read Chapter 2 and Chapter 3.1-3.2 thoroughly.
• Make sure that you know the meanings of the Key Terms at the end of Chapter 2.
• Read handout “A brief introduction to Stata: 2016”.
Review Questions (these may or may not be discussed in tutorial classes)
• The minimum requirement for OLS to be carried out for the data set (xi , yi ), i = 1, . . . , n with the sample size n > 2 is that the sample variance of x is positive. In what circumstances is the sample variance of x zero?
• The OLS estimation of the simple regression model has the following properties:
— the sum of the residuals is zero;
— the sample covariance of the residuals and x is zero.
Why? How would you relate them to the “least squares” principle?
• Which of the following models is (are) nonlinear model(s)?
— sales = β0 /[1 + exp(-β1 ad expenditure)] + u;
— sales = β0 + β1 log(ad expenditure) + u;
— sales = β0 + β1 exp(ad expenditure) + u;
— sales = exp(β0 + β1 ad expenditure + u).
• Can you follow the proofs of Theorems 2.1-2.2?
Problem Set (these will be discussed in tutorial classes)
• Run lecture1 . do to reproduce the scatter plot and correlation between wages and education that were included in Lecture 1. Sometimes the data are not provided as a Stata data set. Run lecture1alt . do which does the same analysis after reading the data from wage1 . raw. Notice the diferences.
• Wooldridge Ch2 QC6
• Wooldridge Ch2 QC7