Lecture 1 Key Terms

Question 1

Conditional mean function

Answer 1

the mean of the outcome y conditional on x , the expected value of y conditional on x, conditional expectation of y given x

Question 2

Population regression function

Answer 2

e( y | x ) = beta0 + beta1x

Question 3

simple linear regression model

Answer 3

y = beta0 + beta1x + u

Question 4

time series

Answer 4

a sequence of data points indexed by time

Question 5

cross sectional data

Answer 5

consists of a sample of units (e.g. individuals, states, households) taken at a given point in time

Question 6

panel data

Answer 6

consists of a time series for each cross sectional member in the set

Question 7

perfect multicollinearity

Answer 7

the matrix X is of full rank if none of the columns are linear transformations of each other

Question 8

unbiased estimator

Answer 8

the difference between the expected value of the estimator and the true value of the parameter is zero

Question 9

consistent estimator

Answer 9

the estimator converges in probability to the true parameter value as the sample size approaches infinity

Question 10

homoskedastic

Answer 10

all random variables in the sequence have the same variance

Question 11

heteroskedastic

Answer 11

at least 1 random variable in the sequence has different variance

Question 12

asymptotically normally distributed

Answer 12

as the number of observations increases to infinity, the distribution of random variables converges to a normal distribution

Question 13

gauss markov theorem

Answer 13

under homoskedasticity the ols estimator is efficient and the best linear unbiased estimator of beta

Question 14

efficiency

Answer 14

the spread / variance of an estimator around a parameter

Question 15

standard error

Answer 15

square root of the variance