Quiz

Front 1

Suppose that the linear probability model yields a predicted value of Y that is equal to 1.3. Explain why this is nonsensical.

Back 1

The predicted value of Y must be between 0 and 1

Front 2

For W to be an effective control variable in IV estimation, the following condition must hold:

Back 2

E(U_i|Z_i,W_i)=E(U_i|Wi)

Front 3

One of your friends is using data on individuals to study the determinants of smoking at your university. She is particularly concerned with estimating marginal effects on the probability of smoking at the extremes. She asks you whether she should use probit, logit, or linear probability model. What advice do you give her?

Back 3

She should use the logit or probit, but not the linear probability model.

Front 4

Assume that for the T=2 time periods case, you have estimated a simple regression in changes model and found a statistically significant positive intercept. This implies:

Back 4

A positive mean change in the LHS variable in the absence of a change in the RHS variable

Front 5

In his study of the effect of incarceration on crime rates, suppose that Levitt had used the number of lawyers per capita as an instrument.

Would this instrument be (i) relevant? Would it be (ii) exogenous? Would it be (iii) a valid instrument?

Back 5

(i) only, bitch.

Front 6

F-statistics computed using maximum likelihood estimators:

Back 6

Can be used to test joint hypotheses

Front 7

A researcher is using a panel data set on n=1000 workers over T=10 years (from 2001 through 2010) that contains the workers' earnings, gender, education, and age. The researcher is interested in the effect of education on earnings. Suppose you run a regression of earnings on person-specific and time-specific control variables.

Can this regression be used to estimate the effect of gender on an individual's earnings or the effect of the national unemployment rate on an individual's earnings?

Back 7

Neither effect can be estimated using this regression. Sorry boutcha.

Front 8

See question 9 for logit,probit, linear probability model workout problem

Back 8

asdfs

Front 9

Your textbooks gives several examples of quasi experiments that were conducted. The following is not an example of a quasi experiment

Back 9

The effect of unemployment on the inflation rate

Front 10

In the panel regression analysis of beer taxes on traffic deaths, the estimation period is 1982-1988 for the 48 contiguous U.S. states.

To test for the significance of entity fixed effects, you should calculate the F-statistic and compare it to the critical value from your Fq, ∞ distribution, where q equals:

Back 10

47.

Front 11

When you add state fixed effects to a simple regression model for U.S. states over a certain time period, and the regression R^2 increases significantly, then it is safe to assume that:

Back 11

State fixed effects account for a large amount of the variation in the data

Front 12

Back 12

1 = D

2= B

Front 13

Maximum likelihood estimations yields the values of the coefficients that :

Back 13

Maximize the likelihood function

Front 14

The linear probability model is:

Back 14

The application of the linear multiple regression model to a binary dependent variable! [punctuation added]

Front 15

The logit model can be estimed and yields consistent estimates if you are using

Back 15

maximum likelihood estimation

Front 16

Consider a panel data set and the following regression model.

Y_it = Beta_0 + Beta_1X_it + U_it

What doe the subscripts i and t refer to?

Back 16

i and t identify the entity and time period, respectively, duh.

Front 17

The fixed effects regression model:

Back 17

Has n different intercepts

Front 18

the two conditions for a valid instrument are

Back 18

corr(Z_i,X_i) =/= 0 and corr(Z_i,U_i) = 0

Front 19

Back 19

A

Front 20

In practice , the most difficult aspect of IV estimation is

Back 20

finding instruments that are both relevant and exogenous

Front 21

Program evaluation

Back 21

is the field of study that concerns estimating the effect of a program, policy, or some other intervention or "treatment".

Front 22

Back 22

1) D

2) D

3) A

Front 23

Let W be the included exogenous variables in a regression function that also has endogenous regressors (x)

The W variables can:

Back 23

A: be control variables

B: make an instrument uncorrelated with u

C: have the property E(U_i|W_i)=0

D: ALL DE ABUVE.

Front 24

Instrument relevance

Back 24

Means that some of the variance in the regressor is related to variation in the instrument

Front 25

Instrumental variables regression uses instruments to:

Back 25

isolate the movements in X that are uncorrelated with u.

Front 26

Back 26

Beta 1

Front 27

In the time fixed effects regression model, you should exclude one of the binary variables for the time periods when an intercept is present in the equation:

Back 27

To avoid perfect multicollinearity.

Front 28

Back 28

1= D

2= C

Front 29

Back 29

Front 30

in the binary dependent variable model, a predicted value of .6 means that:

Back 30

given the values of the explanatory variables, there is a 60 percent probability that the dependent variable will equal one.

Front 31

The following does not represent a threat to internal validity of randomized controlled experiments:

Back 31

a large sample size