Exam 3 Cumulative Review Flashcards
after a simple p and q regression, how do we find the elasticity of something at a certain point?
(p/q) x (coefficient)
does elasticity along a linear demand curve change?
yes, depending on where you are on the curve
whats the most basic way to evaluate curvilinear data?
log-log regression!
convert all the data to Ln’s
how do convert a basic logged q and p regression back?
- take the log of the intercept
- take the coefficient (which is the elasticity) and use that as the exponent for p
- multiply both of these
q = ln (coefficient) x p ^ (elasticity) Q_demanded = 23,156* P(-.873)
what happens to the interpretation of a log-log?
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
what is the value of beta if our curve is convex and starts near the origin?
beta will be greater than 1
what is the value of our beta if our curve is concave, originating near the origin?
beta is between 0 and 1
what is the value of beta if our curve starts high and is convex down?
beta is less than 0
log linear regression
- convert DV variable to logs
- leave the IV as they are
- interpretation? changes to the dependent variable are interpreted as percentages, not units
interpretation of:
Ln(selling price) = 6.21 - 0.08*(age)
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
can you compare the R^2 or the adjusted R^2’s between a linear and log-linear regression?
NO, because the DV’s are two different things
linear log regression
convert the IV to logs
-leave the DV as it is
changes in the IV are not interpreted as percentages, not units
interpretation of:
number of dining out experiences = -37.9 + 11.33*(LnIncome)
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
indifference curves
curves that indicate pairs of things you like equally well
-come from utility functions…
if given this, how would you create an indifference curve?:
U = 3.2(# good X)^(.46)(# Good Y)^(.64)
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
how do you get a utility function?
data from market research
- ask consumers to rate any combination of goods
- ex: beers, pizzas, satisfaction score
estimated utility function formula
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)
Hedonic regression
- 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
example of Hedonic regression
P = .202429*Bed^(.155)Bath^(.2843)Squarefeet^(.334)
omitted variable bias
a constant tacked onto the end of a regression equation to account for unaccounted for variables
panel data
- cross section and time series data combined!
- data varies across entities and across time
- diff markets and years
- diff states and diff months
what is one way we account for time in different cities in a panel data regression?
- 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
- we then take “P - avg. P” And ‘Q - Avg. Q” for each city and for each year
- regress data found in step 2
whats another easier way that we can run a regression with panel data?
- create a dummy variable for all cities except one (the intercept will be the city without a dummy)
- create another binary dummy variable for time
fixed effects regression
- when we hold constant the average effects of each city
- create state-specific binary variables to capture fixed effects
