You are hired by a bank to help determine its lending standards. You decide to start by estimating a simple linear regression model to help predict whether a loan will become past-due (i.e. if any of...

Extracted text: You are hired by a bank to help determine its lending standards. You decide to start by estimating a simple linear regression model to help predict whether a loan will become past-due (i.e. if any of the payments are tate). You ask the bank to give you data on the following two random variables: PD is an indicator variable equal to 1 if the loan is past-due, and zero if all payments have been made on time. credit score is the client's credit score at the time the loan was originated. The table below shows sample descriptive statistics for these variables: Variable PD credit_score Sample Mean 0.1 Sample Standard Deviation 0.08 550 25 The sample covariance between PD and credit score is -0.9 Use OLS to estimate the following simple linear regression model: PD = Bo + Bicredit_score + u Round your answers to four decimals. What should be the average value of the credit score among the bank's clients if the banks wants to have a predicted share of past due loans equal to 0.05 (i.e: 5%)? Round your answer to the nearest integer.


Extracted text: You realize that a person's credit score is affected when they default on a loan. If you estimate the following linear regression model instead credit_score = Bo + B1 PD+ u, then the value of the coefficients estimated using OLS would be the same as those you estimated in the previous question, because both models use the same random variables. O True O False