{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

Problem Set 5 Solutions

# Problem Set 5 Solutions - Problem Set 5 Economics 103 Due...

This preview shows pages 1–4. Sign up to view the full content.

1 Problem Set 5 Economics 103 Due: Thursday, March 12, 2009 Question 1 True/False/Explain: 1) The linear probability model is the application of the multiple regression model with a continuous left-hand side variable and a binary variable as at least one of the regressors. False, the linear probability model is an application of the multiple regression model with a binary left-hand side or dependent variable. It can include continuous and/or binary regressors 2) The linear probability model has predicted values that lie between 0 and 1. False, the predicted values of the linear probability model do not necessarily lie between 0 and 1. This is only true for the probit and logit models 3) The probit model is the same as the logit model. False. Probit regression models the probability that Y=1 using the cumulative standard normal distribution function, while the Logit model uses the cumulative standard logistic distribution function (in practice, they give similar results and have almost the same advantages and disadvantages) 4) In the expression Pr(Y=1)= ( β 0 + β 1 X) from a probit model, 1 cannot be negative since probabilities have to lie between 0 and 1. False . β 1 measures the effect of a unit change in X on the z-score, so it can be negative. 5) In the probit model 12 0 1 1 2 Pr( 1| , ,..., ) ( ... ) kx k k YX XX X X X   , the slopes tell you the effect of a unit increase in X on the probability of Y . False. In the probit model 1 2 Pr( , ,..., ) ( ... ) k k X X X the slopes tell you the effect of a unit increase in X on the z-score. (The argument of the function) 6) In the expression Pr( deny = 1| P/I Ratio , black ) = (–2.26 + 2.74 P/I ratio + 0.71 black ), the effect of increasing the P/I ratio from 0.3 to 0.4 for a white person is 2.74 percentage points.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
2 7) In the binary dependent variable model, a predicted value of 0.6 means that the most likely value the dependent variable will take on is 60 percent. False, this means that the probability that the binary dependent variable equals 1, given the values of X, is .6. 8) In the linear probability model, the interpretation of the slope coefficient is the change in probability that Y =1 associated with a unit change in X , holding other regressors constant. True. 9) The distinction between endogenous and exogenous variables is that exogenous variables are determined inside the model and endogenous variables are determined outside the model. False, exogenous variables are determined outside the model (this is our usual assumption about X) and endogenous variables are determined inside the model.
3 Question 2 1. Describe why you would need to use an instrumental variables estimation strategy? We need an instrumental variables strategy in the case of omitted variables. (We can also use IV if X is measured with measurement error or there is endogeneity –these are not stressed in this class).

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

### Page1 / 14

Problem Set 5 Solutions - Problem Set 5 Economics 103 Due...

This preview shows document pages 1 - 4. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online