OLS

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Chapter 5 Ordinary Least Square Regression

• We will be discussing
• The Linear Regression Model
• Estimation of the Unknowns in the Regression
  Model
• Some Special Cases of the Model

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The Basic Regression Model
The model expressed in scalars
The model elaborated in matrix terms
A succinct matrix expression for it
y X? e
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Prediction Based on a Linear Combination
The E(y) is given by
Together with the previous slide, we can say that
Key Question How Do We Get Values for the ?
vector?
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Parameter Estimation of ?

• We will cover two philosophies of parameter
  estimation, the least squares principle
• and maximum likelihood. Each of these has the
  following steps
• Pick an objective function to optimize
• Find the derivative of f with respect to the
  unknown parameters
• Find values of the unknowns where that
  derivative is zero

The two differ in the first step. The least
squares principle would have us pick f e'e
as our objective function which we will
minimize.
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We Wish to Minimize e'e
We want to minimize f e'e over all possible
values of elements in the vector ?
The function f depends on ?
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Minimizing e?e Cont'd
These two terms are the same so
f y?y 2y?X? ??X?X?.
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What Is the Derivative?
Our objective function is the sum f y?y
2y?X? ??X?X?
Now we need to determine the derivative,
and set it to a column of k zeroes.
The derivative of a sum is equal to the sum of
the derivatives, so we can handle it in pieces.
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A Quickie Review of Some Derivative Rules
The derivative of a constant
The derivative of a linear combination
The derivative of a transpose
The derivative of a quadratic form
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The Derivative of the Sum Is the Sum of the
Derivatives
f y?y 2y?X? ??X?X?
(The derivative of a constant)
(The derivative of a quadratic form)
(The derivative of a linear combination and the
derivative of a transpose)
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Beta Gets a Hat
Add these all together and set equal to zero
And with some algebra
(This one has a name)
(This one has a hat)
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Is Our Formula Any Good?
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What Do We Really Mean by Good?
Unbiasedness
as n ? ?.
Consistency
Sufficiency
does not depend on ?
Efficiency
is smaller than other estimators
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Two Key Assumptions
The behavior of the estimator is driven by the
error input
According to the Gauss-Markov Assumption, V(e)
? looks like
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The Likelihood Principle
Consider a sample of 3 10, 11 and 12. What is
??
5 10 11 12
10 11 12
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Maximum Likelihood

• According to ML, we should pick values for the
  parameters that maximize the probability of the
  sample.
• To do this we need to follow these steps
• Derive the probability of an observation
• Assuming independent observations, calculate the
  likelihood of the sample using multiplication
• Take the log of the sample likelihood
• Derive the derivative of the log likelihood with
  respect to the parameters
• Figure out what the parameters must be so that
  the derivative is equal to a vector of zeroes
• With linear models we can do this analytically
  using algebra
• With non-linear models we sometimes have to use
  brute force hill climbing routines

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The Probability of Observation yi
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Multiply Out the Probability of the Whole Sample
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Take the Log of the Sample Likelihood
ln exp(a) a
ln 1 0
ln ab b ln a
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Figure Out the Derivative and Set Equal to Zero
From here we are just a couple of easy algebraic
steps away from the normal equations, and the
least squares formula,
If ML estimators exist for a model, that
estimator is guaranteed to be consistent,
asymptotically normally distributed and
asymptotically efficient.
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Sums of Squares
SSError y?y - y?X(X?X)-1X?y SSError
SSTotal - SSPredictable
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Using the Covariance Matrix Instead of the Raw
SSCP Matrix
The covariance matrix of the y and x variables
looks like
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Using Z Scores
We can calculate a standard version of using
Z scores
Or use the correlation matrix of all the variables
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The Concept of Partialing
Imagine that we divided up the x variables into
two sets
so that the Beta's were also divided the same way
The model becomes
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The Normal Equations
The normal equations would then be
or
from the first equation gives us
Subtract
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The Estimator for the First Set
Solving for the Estimator for the first set yields
The usual formula
Factoring
What is this?
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The P and M Matrices
Define   P X(X?X)-1X?   and define   M
I P, I - X(X?X)-1X?.
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An Intercept Only Model
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The Intercept Only Model 2
The model becomes  
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The P Matrix in This Case
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The M Matrix
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Response Surface Models Linear vs Quadratic
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Response Surface Models The Sign of the Betas