PMBA P1

PMBA

P1.  The data below contains information on profits and advertising expenditures, both measured in thousands of dollars.
 

Use MS Excel as a fancy calculator to “fill in” the table below.

P2. Define and compute b1, b2, σ2, σ, varb2, and seb2 using the sample data above (note: you will have to use a couple of formulas from chapter 3 in addition to those at the end of chapter two).

P3. Now, use Stata or MS Excel to estimate a model that examines the relationship between profits and advertising expenditures.  Before you estimate your model, create a scatter diagram that relates profits to advertising expenditures.  Why would we do the former before the latter?  Copy both as “pictures” into your homework.

P4. What is the true model you are trying to estimate in equation form (there will be no numbers in this equation, and parameters will be denoted as capitals B’s)?  What is the sample regression function in equation form (again, don’t put numbers in this equation, and parameters will be denoted with lowercase b’s)? Finally, what is your estimated equation (this equation will include numerical values for b1 and b2)?

P5. For each firm, what is the actual value for profits?  What are the predicted values estimated from your model above (you can do this in MS Excel or using Stata’s “predict” command, a regression postestimation command)?

P6. Are your results statistically significant?  Does advertising have a statistically significant relationship with profits? How do you know?  Is the model statistically significant?  How do you know?

P7.  Finally, in your Stata output, please locate and define the following: b1, b2, σ2, σ, varb1, seb1, varb2, seb2.   As you know, b1 and b2 are estimators.  What is an estimator?  What are, b1 and b2 so special?