Simple linear regression model (L2)

Question 1

Simple linear regression model

Answer 1

Defines a conditional expectation
E{y|x}=uy|x= B1+B2x

Question 2

Describe the econometric model

Answer 2

y=B1+B2+e
see image in lecture 2 slide 9

Question 3

DGP

Answer 3

Data generating process
when not all determinants of variable y are known, we treat y & x as random
yi=B1+B2xi+ei

Question 4

assumptions of simple linear regression model (LINEARITY)

Answer 4

x&y are random variables
both x & y are identically distributed +independent
y=dependent (observed) variable x= independent (observed) e=error(unobserved, anything not y or x is unobserved
linearity-a change in x makes a change in y if the error term has no effect

Question 5

assumptions of simple linear regression model

Answer 5

linearity
strict exogeneity
homoskedasticity
no autocorrelation
sample variation
normality

Question 6

assumptions of simple linear regression model (STRICT EXOGENEITY)

Answer 6

E(e|x)=0 error term in linear equation=0 the mean of omitted data gives 0
cov(e,x)=0- you can’t talk about e just looking at x, eg you can’t just look at income to find the error term

Question 7

assumptions of simple linear regression model (VARIATION)

Answer 7

Data must vary, if it doesn’t, we can test the relationship between variables to see what affects it

Question 8

How can we tell if an estimator is biased

Answer 8

E(b)=B , if not then it is biased
A good estimator also has the smallest variance possible