Economics 20 - Prof. Anderson1 The Simple Regression Model y = 0 + 1 x + u.
-
Upload
steve-diamond -
Category
Documents
-
view
218 -
download
0
Transcript of Economics 20 - Prof. Anderson1 The Simple Regression Model y = 0 + 1 x + u.
Economics 20 - Prof. Anderson 1
The Simple Regression Model
y = 0 + 1x + u
Economics 20 - Prof. Anderson 2
Some Terminology
In the simple linear regression model, where y = 0 + 1x + u, we typically refer to y as the Dependent Variable, or Left-Hand Side Variable, or Explained Variable, or Regressand
Economics 20 - Prof. Anderson 3
Some Terminology, cont.
In the simple linear regression of y on x, we typically refer to x as the Independent Variable, or Right-Hand Side Variable, or Explanatory Variable, or Regressor, or Covariate, or Control Variables
Economics 20 - Prof. Anderson 4
A Simple Assumption
The average value of u, the error term, in the population is 0. That is,
E(u) = 0
This is not a restrictive assumption, since we can always use 0 to normalize E(u) to 0
Economics 20 - Prof. Anderson 5
Zero Conditional Mean
We need to make a crucial assumption about how u and x are related We want it to be the case that knowing something about x does not give us any information about u, so that they are completely unrelated. That is, that E(u|x) = E(u) = 0, which implies
E(y|x) = 0 + 1x
Economics 20 - Prof. Anderson 6
..
x1 x2
E(y|x) as a linear function of x, where for any x the distribution of y is centered about E(y|x)
E(y|x) = 0 + 1x
y
f(y)
Economics 20 - Prof. Anderson 7
Ordinary Least Squares
Basic idea of regression is to estimate the population parameters from a sample
Let {(xi,yi): i=1, …,n} denote a random sample of size n from the population
For each observation in this sample, it will be the case that
yi = 0 + 1xi + ui
Economics 20 - Prof. Anderson 8
.
..
.
y4
y1
y2
y3
x1 x2 x3 x4
}
}
{
{
u1
u2
u3
u4
x
y
Population regression line, sample data pointsand the associated error terms
E(y|x) = 0 + 1x
Economics 20 - Prof. Anderson 9
Deriving OLS Estimates
To derive the OLS estimates we need to realize that our main assumption of E(u|x) = E(u) = 0 also implies that
Cov(x,u) = E(xu) = 0
Why? Remember from basic probability that Cov(X,Y) = E(XY) – E(X)E(Y)
Economics 20 - Prof. Anderson 10
Deriving OLS continued
We can write our 2 restrictions just in terms of x, y, 0 and , since u = y – 0 – 1x
E(y – 0 – 1x) = 0
E[x(y – 0 – 1x)] = 0
These are called moment restrictions
Economics 20 - Prof. Anderson 11
Deriving OLS using M.O.M.
The method of moments approach to estimation implies imposing the population moment restrictions on the sample moments
What does this mean? Recall that for E(X), the mean of a population distribution, a sample estimator of E(X) is simply the arithmetic mean of the sample
Economics 20 - Prof. Anderson 12
More Derivation of OLS
We want to choose values of the parameters that will ensure that the sample versions of our moment restrictions are true
The sample versions are as follows:
0ˆˆ
0ˆˆ
110
1
110
1
n
iiii
n
iii
xyxn
xyn
Economics 20 - Prof. Anderson 13
More Derivation of OLS
Given the definition of a sample mean, and properties of summation, we can rewrite the first condition as follows
xy
xy
10
10
ˆˆ
or
,ˆˆ
Economics 20 - Prof. Anderson 14
More Derivation of OLS
n
iii
n
ii
n
iii
n
iii
n
iiii
xxyyxx
xxxyyx
xxyyx
1
21
1
11
1
111
ˆ
ˆ
0ˆˆ
Economics 20 - Prof. Anderson 15
So the OLS estimated slope is
0 that provided
ˆ
1
2
1
2
11
n
ii
n
ii
n
iii
xx
xx
yyxx
Economics 20 - Prof. Anderson 16
Summary of OLS slope estimate
The slope estimate is the sample covariance between x and y divided by the sample variance of x If x and y are positively correlated, the slope will be positive If x and y are negatively correlated, the slope will be negative Only need x to vary in our sample
Economics 20 - Prof. Anderson 17
More OLS
Intuitively, OLS is fitting a line through the sample points such that the sum of squared residuals is as small as possible, hence the term least squares
The residual, û, is an estimate of the error term, u, and is the difference between the fitted line (sample regression function) and the sample point
Economics 20 - Prof. Anderson 18
.
..
.
y4
y1
y2
y3
x1 x2 x3 x4
}
}
{
{
û1
û2
û3
û4
x
y
Sample regression line, sample data pointsand the associated estimated error terms
xy 10ˆˆˆ
Economics 20 - Prof. Anderson 19
Alternate approach to derivation
Given the intuitive idea of fitting a line, we can set up a formal minimization problem
That is, we want to choose our parameters such that we minimize the following:
n
iii
n
ii xyu
1
2
101
2 ˆˆˆ
Economics 20 - Prof. Anderson 20
Alternate approach, continued
If one uses calculus to solve the minimization problem for the two parameters you obtain the following first order conditions, which are the same as we obtained before, multiplied by n
0ˆˆ
0ˆˆ
110
110
n
iiii
n
iii
xyx
xy
Economics 20 - Prof. Anderson 21
Algebraic Properties of OLS
The sum of the OLS residuals is zero
Thus, the sample average of the OLS residuals is zero as well
The sample covariance between the regressors and the OLS residuals is zero
The OLS regression line always goes through the mean of the sample
Economics 20 - Prof. Anderson 22
Algebraic Properties (precise)
xy
ux
n
uu
n
iii
n
iin
ii
10
1
1
1
ˆˆ
0ˆ
0
ˆ
thus,and 0ˆ
Economics 20 - Prof. Anderson 23
More terminology
SSR SSE SSTThen
(SSR) squares of sum residual theis ˆ
(SSE) squares of sum explained theis ˆ
(SST) squares of sum total theis
:following thedefine then Weˆˆ
part, dunexplainean and part, explainedan of up
made being asn observatioeach ofcan think We
2
2
2
i
i
i
iii
u
yy
yy
uyy
Economics 20 - Prof. Anderson 24
Proof that SST = SSE + SSR
0 ˆˆ that know weand
SSE ˆˆ2 SSR
ˆˆˆ2ˆ
ˆˆ
ˆˆ
22
2
22
yyu
yyu
yyyyuu
yyu
yyyyyy
ii
ii
iiii
ii
iiii
Economics 20 - Prof. Anderson 25
Goodness-of-Fit
How do we think about how well our sample regression line fits our sample data?
Can compute the fraction of the total sum of squares (SST) that is explained by the model, call this the R-squared of regression
R2 = SSE/SST = 1 – SSR/SST
Economics 20 - Prof. Anderson 26
Using Stata for OLS regressions
Now that we’ve derived the formula for calculating the OLS estimates of our parameters, you’ll be happy to know you don’t have to compute them by hand
Regressions in Stata are very simple, to run the regression of y on x, just type
reg y x
Economics 20 - Prof. Anderson 27
Unbiasedness of OLS
Assume the population model is linear in parameters as y = 0 + 1x + u Assume we can use a random sample of size n, {(xi, yi): i=1, 2, …, n}, from the population model. Thus we can write the sample model yi = 0 + 1xi + ui
Assume E(u|x) = 0 and thus E(ui|xi) = 0
Assume there is variation in the xi
Economics 20 - Prof. Anderson 28
Unbiasedness of OLS (cont)
In order to think about unbiasedness, we need to rewrite our estimator in terms of the population parameter
Start with a simple rewrite of the formula as
22
21 where,ˆ
xxs
s
yxx
ix
x
ii
Economics 20 - Prof. Anderson 29
Unbiasedness of OLS (cont)
ii
iii
ii
iii
iiiii
uxx
xxxxx
uxx
xxxxx
uxxxyxx
10
10
10
Economics 20 - Prof. Anderson 30
Unbiasedness of OLS (cont)
211
21
2
ˆ
thusand ,
asrewritten becan numerator the,so
,0
x
ii
iix
iii
i
s
uxx
uxxs
xxxxx
xx
Economics 20 - Prof. Anderson 31
Unbiasedness of OLS (cont)
1211
21
1ˆ
then,1ˆ
thatso ,let
iix
iix
i
ii
uEds
E
uds
xxd
Economics 20 - Prof. Anderson 32
Unbiasedness Summary
The OLS estimates of 1 and 0 are unbiased Proof of unbiasedness depends on our 4 assumptions – if any assumption fails, then OLS is not necessarily unbiased Remember unbiasedness is a description of the estimator – in a given sample we may be “near” or “far” from the true parameter
Economics 20 - Prof. Anderson 33
Variance of the OLS Estimators
Now we know that the sampling distribution of our estimate is centered around the true parameter Want to think about how spread out this distribution is Much easier to think about this variance under an additional assumption, soAssume Var(u|x) = 2 (Homoskedasticity)
Economics 20 - Prof. Anderson 34
Variance of OLS (cont)
Var(u|x) = E(u2|x)-[E(u|x)]2
E(u|x) = 0, so 2 = E(u2|x) = E(u2) = Var(u)
Thus 2 is also the unconditional variance, called the error variance
, the square root of the error variance is called the standard deviation of the error
Can say: E(y|x)=0 + 1x and Var(y|x) = 2
Economics 20 - Prof. Anderson 35
..
x1 x2
Homoskedastic Case
E(y|x) = 0 + 1x
y
f(y|x)
Economics 20 - Prof. Anderson 36
.
x x1 x2
yf(y|x)
Heteroskedastic Case
x3
..
E(y|x) = 0 + 1x
Economics 20 - Prof. Anderson 37
Variance of OLS (cont)
12
222
22
22
2222
2
2
22
2
2
2
211
ˆ1
11
11
1ˆ
Vars
ss
ds
ds
uVards
udVars
uds
VarVar
xx
x
ix
ix
iix
iix
iix
Economics 20 - Prof. Anderson 38
Variance of OLS Summary
The larger the error variance, 2, the larger the variance of the slope estimate
The larger the variability in the xi, the smaller the variance of the slope estimate
As a result, a larger sample size should decrease the variance of the slope estimate
Problem that the error variance is unknown
Economics 20 - Prof. Anderson 39
Estimating the Error Variance
We don’t know what the error variance, 2, is, because we don’t observe the errors, ui
What we observe are the residuals, ûi
We can use the residuals to form an estimate of the error variance
Economics 20 - Prof. Anderson 40
Error Variance Estimate (cont)
2/ˆ2
1ˆ
is ofestimator unbiasedan Then,
ˆˆ
ˆˆ
ˆˆˆ
22
2
1100
1010
10
nSSRun
u
xux
xyu
i
i
iii
iii
Economics 20 - Prof. Anderson 41
Error Variance Estimate (cont)
21
21
1
2
/ˆˆse
, ˆ oferror standard the
have then wefor ˆ substitute weif
ˆsd that recall
regression theoferror Standardˆˆ
xx
s
i
x