ACG 6455 Regression Assignment

The file "Fees15.csv" includes data about audit fees paid by public companies for fiscal year 2015 (Note: This could include companies with fiscal year ends up to March 31, 2016 or even a little later). This file also incudes data about CIK, SIC code, ASSETS, auditor type (Big 4 or not), and Fiscal year end of the companies.

First, subset called "fin" that will include ONLY financial sector clients. The command in R Studio is

fin <- subset(Fees15, SIC < 7000 & SIC > 5999)

Notice the command to create the "fin" dataset.

We want only those clients with SIC codes from 6000 to 6999.

So, the first part says "SIC < 7000" while the second part says "SIC > 5999".

The "&" is for "AND" command. So, both conditions,

SIC < 7000, and SIC > 5999

have to be satisfied before an observation is included in the new subset.

This way, we only get companies with SIC codes between 6000 and 6999.

Then, proceed as before and linear model regression for audit fees of public companies in the financial sector. The audit fee model is:

LN(FEES) = ? + ?1*LN(ASSETS) + ?2*BIG4 One way to run such a regression in R Studio is as follows:

ff <- fin$FEES

af <- fin$ASSETS

LFF <- log(ff)

LFA <- log(af)

B4F <-factor(fin$BIG4)

resultsf <-lm(LFF~LFA+B4F) summary(resultsf)

Assignment:

What is the predicted audit fees for a bank with $500 million in assets?

How does this number compare with the predicted fee if we used the full dataset (i.e., the entire sample of 4426 observations without deleting the non-financial companies)? (Recall that we did this earlier, and you have the regression results from the full sample.)

Bonus question: How does the predicted audit fee for a bank with $500 million in assets compare with the predicted audit fees for a non-financial firm with $500 million in assets? What are the lessons to learn from such a comparison?