Suppose we are interested in predicting the stock prices of companies on the S&P 500, price.
Suppose that, in the real world, the true model (which satisfies the MLR assumptions) is given by
price =0+1 earnings +2 pricelast +3 industrygrowth +u, in which the stock price depends on a
company's earnings, its previous price, and the growth of the industry. Suppose that we estimate
the regression price=^0+^1earnings+^2pricelast+^3industrygrowth+^4sp500+u1 Where we add
the S&P 500 index into our regression. i) What is the expected value of the coefficient on sp500,E(
^4) ? ii) Do you expect the R2 of this regression be greater than or less than the regression which
does not include sp500 ? Why or why not? iii) If your interests are in prediction, should you
automatically choose the model with the highest R2 ? Why or why not? If you run this regression
on 500 different datasets, how many estimates of ^4 do you expect will be significant at the 5%
level? The 10% level?.
Suppose we are interested in predicting the stock prices of .pdf
1. Suppose we are interested in predicting the stock prices of companies on the S&P 500, price.
Suppose that, in the real world, the true model (which satisfies the MLR assumptions) is given by
price =0+1 earnings +2 pricelast +3 industrygrowth +u, in which the stock price depends on a
company's earnings, its previous price, and the growth of the industry. Suppose that we estimate
the regression price=^0+^1earnings+^2pricelast+^3industrygrowth+^4sp500+u1 Where we add
the S&P 500 index into our regression. i) What is the expected value of the coefficient on sp500,E(
^4) ? ii) Do you expect the R2 of this regression be greater than or less than the regression which
does not include sp500 ? Why or why not? iii) If your interests are in prediction, should you
automatically choose the model with the highest R2 ? Why or why not? If you run this regression
on 500 different datasets, how many estimates of ^4 do you expect will be significant at the 5%
level? The 10% level?
