Exam 3 Cumulative Review

Question 1

after a simple p and q regression, how do we find the elasticity of something at a certain point?

Answer 1

(p/q) x (coefficient)

Question 2

does elasticity along a linear demand curve change?

Answer 2

yes, depending on where you are on the curve

Question 3

whats the most basic way to evaluate curvilinear data?

Answer 3

log-log regression!

convert all the data to Ln’s

Question 4

how do convert a basic logged q and p regression back?

Answer 4

1. take the log of the intercept
2. take the coefficient (which is the elasticity) and use that as the exponent for p
3. multiply both of these

q = ln (coefficient) x p ^ (elasticity)
Q_demanded = 23,156* P(-.873)

Question 5

what happens to the interpretation of a log-log?

Answer 5

taking the log of both variables changes the interpretation to percentages

a certain percent change in X is associated with a certain percentage change in Y

Question 6

what is the value of beta if our curve is convex and starts near the origin?

Answer 6

beta will be greater than 1

Question 7

what is the value of our beta if our curve is concave, originating near the origin?

Answer 7

beta is between 0 and 1

Question 8

what is the value of beta if our curve starts high and is convex down?

Answer 8

beta is less than 0

Question 9

log linear regression

Answer 9

• convert DV variable to logs
• leave the IV as they are
• interpretation? changes to the dependent variable are interpreted as percentages, not units

Question 10

interpretation of:

Ln(selling price) = 6.21 - 0.08*(age)

Answer 10

it’s a log linear, so …

a one year increase in the age of the house translates to an 8% increase in the selling price of the house

Question 11

can you compare the R^2 or the adjusted R^2’s between a linear and log-linear regression?

Answer 11

NO, because the DV’s are two different things

Question 12

linear log regression

Answer 12

convert the IV to logs
-leave the DV as it is

changes in the IV are not interpreted as percentages, not units

Question 13

interpretation of:

number of dining out experiences = -37.9 + 11.33*(LnIncome)

Answer 13

this is a linear log soooo….

a 9% increase in income translates to (9*.11 or .99) or 1 time increase in dining out per month

Question 14

indifference curves

Answer 14

curves that indicate pairs of things you like equally well

-come from utility functions…

Question 15

if given this, how would you create an indifference curve?:

U = 3.2(# good X)^(.46)(# Good Y)^(.64)

Answer 15

you find the different combinations that will give you an x amount of utility, and graph all of those points to make a nice little curve

Question 16

how do you get a utility function?

Answer 16

data from market research

• ask consumers to rate any combination of goods
• ex: beers, pizzas, satisfaction score

Question 17

estimated utility function formula

Answer 17

Ln(utility) = ln(a) + (b)(Ln_beer) + (c)(Ln_pizza)

now convert it back

utility = (exp(.635))beer^(.502)pizza^(.163)
OR
utility = 1.89beer^(.502)pizza^(.163)

Question 18

Hedonic regression

Answer 18

• dissecting a product into it’s characteristics
• used to understand how consumers view the trade-offs among those characteristics
• fantastic use of utility functions
• often used in real estate

Question 19

example of Hedonic regression

Answer 19

P = .202429*Bed^(.155)Bath^(.2843)Squarefeet^(.334)

Question 20

omitted variable bias

Answer 20

a constant tacked onto the end of a regression equation to account for unaccounted for variables

Question 21

panel data

Answer 21

• cross section and time series data combined!
• data varies across entities and across time
• diff markets and years
• diff states and diff months

Question 22

what is one way we account for time in different cities in a panel data regression?

Answer 22

1. instead of compared the price and quantity demanded of all of the cities in each year, we look at the average price and average quantity demanded for EACH city
2. we then take “P - avg. P” And ‘Q - Avg. Q” for each city and for each year
3. regress data found in step 2

Question 23

whats another easier way that we can run a regression with panel data?

Answer 23

1. create a dummy variable for all cities except one (the intercept will be the city without a dummy)
2. create another binary dummy variable for time

Question 24

fixed effects regression

Answer 24

• when we hold constant the average effects of each city

- create state-specific binary variables to capture fixed effects

Question 25

time fixed effects regression

Answer 25

just running a dummy variable for time now

Question 26

how can we test, with a time fixed effects regression, if the coefficients are indistinguishable from zero?

Answer 26

you know dis!

(RSSrestricted-RSSunrestricted/q)
__________________________________
(RSSunrestricted / n-k-1)

simply use your time fixed effects regression as your unrestricted and your very first regression with no effects as your restricted

Question 27

whats the command to run timed fixed effects and fixed effects regressions in STATA

Answer 27

xi: regress y x1 x2 x3 x4 i.x5 i.x6