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Chapter 2 The Simple Linear Regression Model: Specification and Estimation. Walter R. Paczkowski Rutgers University. Chapter Contents. 2.1 An Economic Model 2.2 An Econometric Model 2.3 Estimating the Regression Parameters 2.4 Assessing the Least Squares Estimators - PowerPoint PPT Presentation
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Principles of Econometrics, 4th Edition Page 1Chapter 2: The Simple Linear Regression Model
Chapter 2The Simple Linear Regression
Model: Specification and Estimation
Walter R. Paczkowski Rutgers University
Principles of Econometrics, 4th Edition Page 2Chapter 2: The Simple Linear Regression Model
2.1 An Economic Model2.2 An Econometric Model2.3 Estimating the Regression Parameters2.4 Assessing the Least Squares Estimators2.5 The Gauss-Markov Theorem2.6 The Probability Distributions of the Least
Squares Estimators2.7 Estimating the Variance of the Error Term2.8 Estimating Nonlinear Relationships2.9 Regression with Indicator Variables
Chapter Contents
Principles of Econometrics, 4th Edition Page 3Chapter 2: The Simple Linear Regression Model
2.1 An Economic Model
Principles of Econometrics, 4th Edition Page 4Chapter 2: The Simple Linear Regression Model
As economists we are usually more interested in studying relationships between variables– Economic theory tells us that expenditure on
economic goods depends on income – Consequently we call y the ‘‘dependent
variable’’ and x the independent’’ or ‘‘explanatory’’ variable
– In econometrics, we recognize that real-world expenditures are random variables, and we want to use data to learn about the relationship
2.1An Economic
Model
Principles of Econometrics, 4th Edition Page 5Chapter 2: The Simple Linear Regression Model
The pdf is a conditional probability density function since it is ‘‘conditional’’ upon an x– The conditional mean, or expected value, of y is
E(y|x)• The expected value of a random variable is
called its ‘‘mean’’ value, which is really a contraction of population mean, the center of the probability distribution of the random variable• This is not the same as the sample mean,
which is the arithmetic average of numerical values
2.1An Economic
Model
Principles of Econometrics, 4th Edition Page 6Chapter 2: The Simple Linear Regression Model
Figure 2.1a Probability distribution of food expenditure y given income x = $1000
2.1An Economic
Model
Principles of Econometrics, 4th Edition Page 7Chapter 2: The Simple Linear Regression Model
The conditional variance of y is σ2 which measures the dispersion of y about its mean μy|x – The parameters μy|x and σ2, if they were known,
would give us some valuable information about the population we are considering
2.1An Economic
Model
Principles of Econometrics, 4th Edition Page 8Chapter 2: The Simple Linear Regression Model
Figure 2.1b Probability distributions of food expenditures y given incomes x = $1000 and x = $2000
2.1An Economic
Model
Principles of Econometrics, 4th Edition Page 9Chapter 2: The Simple Linear Regression Model
In order to investigate the relationship between expenditure and income we must build an economic model and then a corresponding econometric model that forms the basis for a quantitative or empirical economic analysis– This econometric model is also called a
regression model
2.1An Economic
Model
Principles of Econometrics, 4th Edition Page 10Chapter 2: The Simple Linear Regression Model
Eq. 2.1xxyE y 21)|(
The simple regression function is written as
where β1 is the intercept and β2 is the slope
Eq. 2.1 2 2( | ) μ β βyE y x x
2.1An Economic
Model
Principles of Econometrics, 4th Edition Page 11Chapter 2: The Simple Linear Regression Model
xxyE y 21)|( It is called simple regression not because it is easy, but because there is only one explanatory variable on the right-hand side of the equation
2.1An Economic
Model
Principles of Econometrics, 4th Edition Page 12Chapter 2: The Simple Linear Regression Model
2.1An Economic
ModelFigure 2.2 The economic model: a linear relationship between average per person food expenditure and income
Principles of Econometrics, 4th Edition Page 13Chapter 2: The Simple Linear Regression Model
Eq. 2.2
“Δ” denotes “change in” and “dE(y|x)/dx” denotes the derivative of the expected value of y given an x value
dxxydE
xxyE )|()|(
2
The slope of the regression line can be written as:
where “Δ” denotes “change in” and “dE(y|x)/dx” denotes the derivative of the expected value of y given an x value
Eq. 2.2 2( | ) ( | )β E y x dE y x
x dx
2.1An Economic
Model
Principles of Econometrics, 4th Edition Page 14Chapter 2: The Simple Linear Regression Model
2.2 An Econometric Model
Principles of Econometrics, 4th Edition Page 15Chapter 2: The Simple Linear Regression Model
2.2An Econometric
Model Figure 2.3 The probability density function for y at two levels of income
Principles of Econometrics, 4th Edition Page 16Chapter 2: The Simple Linear Regression Model
There are several key assumptions underlying the simple linear regression–More will be added later
2.2An Econometric
Model
Principles of Econometrics, 4th Edition Page 17Chapter 2: The Simple Linear Regression Model
ASSUMPTIONS OF THE SIMPLE LINEAR REGRESSION MODEL - I
Assumption 1:The mean value of y, for each value of x, is
given by the linear regression
1 2( | ) β βE y x x
2.2An Econometric
Model
Principles of Econometrics, 4th Edition Page 18Chapter 2: The Simple Linear Regression Model
Assumption 2:For each value of x, the values of y are
distributed about their mean value, following probability distributions that all have the same
variance2var( | ) σy x
ASSUMPTIONS OF THE SIMPLE LINEAR REGRESSION MODEL - I2.2
An Econometric Model
Principles of Econometrics, 4th Edition Page 19Chapter 2: The Simple Linear Regression Model
Assumption 3:The sample values of y are all uncorrelated,
and have zero covariance, implying that there is no linear association among them
0),cov( ji yy
This assumption can be made stronger by assuming that the values of y are all statistically independent
ASSUMPTIONS OF THE SIMPLE LINEAR REGRESSION MODEL - I2.2
An Econometric Model
Principles of Econometrics, 4th Edition Page 20Chapter 2: The Simple Linear Regression Model
Assumption 4:The variable x is not random, and must take at
least two different values
2.2An Econometric
Model ASSUMPTIONS OF THE SIMPLE LINEAR REGRESSION MODEL - I
Principles of Econometrics, 4th Edition Page 21Chapter 2: The Simple Linear Regression Model
Assumption 5:
(optional) The values of y are normally distributed about their mean for each value of x
21 2~ (β β ,σ )y N x
2.2An Econometric
Model ASSUMPTIONS OF THE SIMPLE LINEAR REGRESSION MODEL - I
Principles of Econometrics, 4th Edition Page 22Chapter 2: The Simple Linear Regression Model
2.2.1Introducing the
Error TermThe random error term is defined as
– Rearranging gives
where y is the dependent variable and x is the independent variable
1 2| β βe y E y x y x Eq. 2.3
1 2β βy x e Eq. 2.4
2.2An Econometric
Model
Principles of Econometrics, 4th Edition Page 23Chapter 2: The Simple Linear Regression Model
The expected value of the error term, given x, is
The mean value of the error term, given x, is zero
1 2( | ) ( | ) β β 0E e x E y x x
2.2An Econometric
Model
2.2.1Introducing the
Error Term
Principles of Econometrics, 4th Edition Page 24Chapter 2: The Simple Linear Regression Model
Figure 2.4 Probability density functions for e and y2.2
An Econometric Model
2.2.1Introducing the
Error Term
Principles of Econometrics, 4th Edition Page 25Chapter 2: The Simple Linear Regression Model
ASSUMPTIONS OF THE SIMPLE LINEAR REGRESSION MODEL - II
Assumption SR1:The value of y, for each value of x, is:
1 2β βy x e
2.2An Econometric
Model
2.2.1Introducing the
Error Term
Principles of Econometrics, 4th Edition Page 26Chapter 2: The Simple Linear Regression Model
Assumption SR2:The expected value of the random error e is:
This is equivalent to assuming that
0)( eE
1 2( ) β βE y x
2.2An Econometric
Model
2.2.1Introducing the
Error Term
ASSUMPTIONS OF THE SIMPLE LINEAR REGRESSION MODEL - II
Principles of Econometrics, 4th Edition Page 27Chapter 2: The Simple Linear Regression Model
Assumption SR3:The variance of the random error e is:
2var( ) σ var( )e y
The random variables y and e have the same variance because they differ only by a constant.
2.2An Econometric
Model
2.2.1Introducing the
Error Term
ASSUMPTIONS OF THE SIMPLE LINEAR REGRESSION MODEL - II
Principles of Econometrics, 4th Edition Page 28Chapter 2: The Simple Linear Regression Model
Assumption SR4:The covariance between any pair of random
errors, ei and ej is:
The stronger version of this assumption is that the random errors e are statistically independent, in which case the values of the dependent variable y are also statistically independent
0),cov(),cov( jiji yyee
2.2An Econometric
Model
2.2.1Introducing the
Error Term
ASSUMPTIONS OF THE SIMPLE LINEAR REGRESSION MODEL - II
Principles of Econometrics, 4th Edition Page 29Chapter 2: The Simple Linear Regression Model
Assumption SR5:The variable x is not random, and must take at
least two different values
2.2An Econometric
Model
2.2.1Introducing the
Error Term
ASSUMPTIONS OF THE SIMPLE LINEAR REGRESSION MODEL - II
Principles of Econometrics, 4th Edition Page 30Chapter 2: The Simple Linear Regression Model
Assumption SR6:(optional) The values of e are normally
distributed about their mean if the values of y are normally distributed, and vice versa
2~ (0,σ )e N
2.2An Econometric
Model
2.2.1Introducing the
Error Term
ASSUMPTIONS OF THE SIMPLE LINEAR REGRESSION MODEL - II
Principles of Econometrics, 4th Edition Page 31Chapter 2: The Simple Linear Regression Model
Figure 2.5 The relationship among y, e and the true regression line2.2
An Econometric Model
2.2.1Introducing the
Error Term
Principles of Econometrics, 4th Edition Page 32Chapter 2: The Simple Linear Regression Model
2.3 Estimating the Regression Parameters
Principles of Econometrics, 4th Edition Page 33Chapter 2: The Simple Linear Regression Model
2.3Estimating the
Regression Parameters
Table 2.1 Food Expenditure and Income Data
Principles of Econometrics, 4th Edition Page 34Chapter 2: The Simple Linear Regression Model
Figure 2.6 Data for food expenditure example2.3
Estimating the Regression Parameters
Principles of Econometrics, 4th Edition Page 35Chapter 2: The Simple Linear Regression Model
The fitted regression line is:
The least squares residual is:
2.3.1The Least Squares Principle
1 2ˆi iy b b x
iiiii xbbyyye 21ˆˆ
Eq. 2.5
Eq. 2.6
2.3Estimating the
Regression Parameters
Principles of Econometrics, 4th Edition Page 36Chapter 2: The Simple Linear Regression Model
Figure 2.7 The relationship among y, ê and the fitted regression line2.3
Estimating the Regression Parameters
2.3.1The Least Squares Principle
Principles of Econometrics, 4th Edition Page 37Chapter 2: The Simple Linear Regression Model
Suppose we have another fitted line:
The least squares line has the smaller sum of squared residuals:
* * *1 2ˆi iy b b x
*
1
2**
1
2 thenˆandˆif SSESSEeSSEeSSEN
ii
N
ii
2.3Estimating the
Regression Parameters
2.3.1The Least Squares Principle
Principles of Econometrics, 4th Edition Page 38Chapter 2: The Simple Linear Regression Model
Least squares estimates for the unknown parameters β1 and β2 are obtained my minimizing the sum of squares function:
21 2 1 2
1
(β ,β ) ( β β )N
i ii
S y x
2.3Estimating the
Regression Parameters
2.3.1The Least Squares Principle
Principles of Econometrics, 4th Edition Page 39Chapter 2: The Simple Linear Regression Model
22 )())((
xxyyxx
bi
ii
xbyb 21
Eq. 2.7
Eq. 2.8
2.3Estimating the
Regression Parameters
2.3.1The Least Squares Principle
THE LEAST SQUARES ESTIMATORS
Principles of Econometrics, 4th Edition Page 40Chapter 2: The Simple Linear Regression Model
2.3.2Estimates for
the Food Expenditure
Function
2096.107876.18282684.18671
)())((
22
xxyyxx
bi
ii
4160.83)6048.19)(2096.10(5735.28321 xbyb
A convenient way to report the values for b1 and b2 is to write out the estimated or fitted regression line:
ii xy 21.1042.83ˆ
2.3Estimating the
Regression Parameters
Principles of Econometrics, 4th Edition Page 41Chapter 2: The Simple Linear Regression Model
Figure 2.8 The fitted regression line2.3
Estimating the Regression Parameters
2.3.2Estimates for
the Food Expenditure
Function
Principles of Econometrics, 4th Edition Page 42Chapter 2: The Simple Linear Regression Model
The value b2 = 10.21 is an estimate of 2, the amount by which weekly expenditure on food per household increases when household weekly income increases by $100. Thus, we estimate that if income goes up by $100, expected weekly expenditure on food will increase by approximately $10.21– Strictly speaking, the intercept estimate b1 =
83.42 is an estimate of the weekly food expenditure on food for a household with zero income
2.3.3Interpreting the
Estimates
2.3Estimating the
Regression Parameters
Principles of Econometrics, 4th Edition Page 43Chapter 2: The Simple Linear Regression Model
Income elasticity is a useful way to characterize the responsiveness of consumer expenditure to changes in income. The elasticity of a variable y with respect to another variable x is:
In the linear economic model given by Eq. 2.1 we have shown that
2.3.3aElasticities
yx
xy
xy
in change percentagein change percentage
2( )β E yx
2.3Estimating the
Regression Parameters
Principles of Econometrics, 4th Edition Page 44Chapter 2: The Simple Linear Regression Model
The elasticity of mean expenditure with respect to income is:
A frequently used alternative is to calculate the elasticity at the “point of the means” because it is a representative point on the regression line.
2( ) ( ) ( ) β
( ) ( )E y E y E y x x
x x x E y E y
Eq. 2.9
71.057.283
60.1921.10ˆ 2 yxb
2.3Estimating the
Regression Parameters
2.3.3aElasticities
Principles of Econometrics, 4th Edition Page 45Chapter 2: The Simple Linear Regression Model
Suppose that we wanted to predict weekly food expenditure for a household with a weekly income of $2000. This prediction is carried out by substituting x = 20 into our estimated equation to obtain:
We predict that a household with a weekly income of $2000 will spend $287.61 per week on food
2.3.3bPrediction
61.287)20(21.1042.8321.1042.83ˆ ixy
2.3Estimating the
Regression Parameters
Principles of Econometrics, 4th Edition Page 46Chapter 2: The Simple Linear Regression Model
2.3.3cComputer
Output
Figure 2.9 EViews Regression Output2.3
Estimating the Regression Parameters
Principles of Econometrics, 4th Edition Page 47Chapter 2: The Simple Linear Regression Model
2.3.4Other Economic
Models
The simple regression model can be applied to estimate the parameters of many relationships in economics, business, and the social sciences – The applications of regression analysis are
fascinating and useful
2.3Estimating the
Regression Parameters
Principles of Econometrics, 4th Edition Page 48Chapter 2: The Simple Linear Regression Model
2.4 Assessing the Least Squares Fit
Principles of Econometrics, 4th Edition Page 49Chapter 2: The Simple Linear Regression Model
We call b1 and b2 the least squares estimators.– We can investigate the properties of the estimators
b1 and b2 , which are called their sampling properties, and deal with the following important questions:
1. If the least squares estimators are random variables, then what are their expected values, variances, covariances, and probability distributions?
2. How do the least squares estimators compare with other procedures that might be used, and how can we compare alternative estimators?
2.4Assessing the Least Squares
Fit
Principles of Econometrics, 4th Edition Page 50Chapter 2: The Simple Linear Regression Model
The estimator b2 can be rewritten as:
where
It could also be writen as:
2.4.1The Estimator b2
N
iii ywb
12
2)( xxxxw
i
ii
2 2β i ib w e
Eq. 2.10
Eq. 2.11
Eq. 2.12
2.4Assessing the Least Squares
Fit
Principles of Econometrics, 4th Edition Page 51Chapter 2: The Simple Linear Regression Model
We will show that if our model assumptions hold, then E(b2) = β2, which means that the estimator is unbiased. We can find the expected value of b2 using the fact that the expected value of a sum is the sum of the expected values:
using and
2.4.2The Expected
Values of b1 and b2
2 2 2 1 1 2 2
2 1 1 2 2
2
2
2
( ) ( ) (β ... )
(β ) ( ) ( ) ... ( )
(β ) ( )
β ( )
β
i i N N
N N
i i
i i
E b E b w e E w e w e w e
E E w e E w e E w e
E E w e
w E e
)()( iiii eEwewE 0)( ieE
Eq. 2.13
2.4Assessing the Least Squares
Fit
Principles of Econometrics, 4th Edition Page 52Chapter 2: The Simple Linear Regression Model
The property of unbiasedness is about the average values of b1 and b2 if many samples of the same size are drawn from the same population– If we took the averages of estimates from many
samples, these averages would approach the true parameter values b1 and b2
– Unbiasedness does not say that an estimate from any one sample is close to the true parameter value, and thus we cannot say that an estimate is unbiased
– We can say that the least squares estimation procedure (or the least squares estimator) is unbiased
2.4Assessing the Least Squares
Fit
2.4.2The Expected
Values of b1 and b2
Principles of Econometrics, 4th Edition Page 53Chapter 2: The Simple Linear Regression Model
2.4.3Repeated Sampling
Table 2.2 Estimates from 10 Samples2.4
Assessing the Least Squares
Fit
Principles of Econometrics, 4th Edition Page 54Chapter 2: The Simple Linear Regression Model
Figure 2.10 Two possible probability density functions for b2
The variance of b2 is defined as 2222 )]([)var( bEbEb
2.4Assessing the Least Squares
Fit
2.4.3Repeated Sampling
Principles of Econometrics, 4th Edition Page 55Chapter 2: The Simple Linear Regression Model
If the regression model assumptions SR1-SR5 are correct (assumption SR6 is not required), then the variances and covariance of b1 and b2 are:
2.4.4The Variances
and Covariances of b1 and b2
22
1 2var( ) σ i
i
xb
N x x
2
2 2
σvar( )i
bx x
2
1 2 2cov( , ) σi
xb bx x
Eq. 2.14
Eq. 2.15
Eq. 2.16
2.4Assessing the Least Squares
Fit
Principles of Econometrics, 4th Edition Page 56Chapter 2: The Simple Linear Regression Model
1. The larger the variance term σ2 , the greater the uncertainty there is in the statistical model, and the larger the variances and covariance of the least squares estimators.
2. The larger the sum of squares, , the smaller the variances of the least squares estimators and the more precisely we can estimate the unknown parameters.
3. The larger the sample size N, the smaller the variances and covariance of the least squares estimators.
4. The larger the term , the larger the variance of the least squares estimator b1.
5. The absolute magnitude of the covariance increases the larger in magnitude is the sample mean , and the covariance has a sign opposite to that of .
2xxi
2ix
xx
MAJOR POINTS ABOUT THE VARIANCES AND COVARIANCES OF b1 AND b2
2.4Assessing the Least Squares
Fit
2.4.4The Variances
and Covariances of b1 and b2
Principles of Econometrics, 4th Edition Page 57Chapter 2: The Simple Linear Regression Model
Figure 2.11 The influence of variation in the explanatory variable x on precision of estimation (a) Low x variation, low precision (b) High x variation, high precision
The variance of b2 is defined as 2222 )()var( bEbEb
2.4Assessing the Least Squares
Fit
2.4.4The Variances
and Covariances of b1 and b2
Principles of Econometrics, 4th Edition Page 58Chapter 2: The Simple Linear Regression Model
2.5 The Gauss-Markov Theorem
Principles of Econometrics, 4th Edition Page 59Chapter 2: The Simple Linear Regression Model
2.5The Gauss-
Markov Theorem
Under the assumptions SR1-SR5 of the linear
regression model, the estimators b1 and b2 have the
smallest variance of all linear and unbiased
estimators of b1 and b2. They are the Best Linear
Unbiased Estimators (BLUE) of b1 and b2
GAUSS-MARKOV THEOREM
Principles of Econometrics, 4th Edition Page 60Chapter 2: The Simple Linear Regression Model
MAJOR POINTS ABOUT THE GAUSS-MARKOV THEOREM
1. The estimators b1 and b2 are “best” when compared to similar estimators, those which are linear and unbiased. The Theorem does not say that b1 and b2 are the best of all possible estimators.
2. The estimators b1 and b2 are best within their class because they have the minimum variance. When comparing two linear and unbiased estimators, we always want to use the one with the smaller variance, since that estimation rule gives us the higher probability of obtaining an estimate that is close to the true parameter value.
3. In order for the Gauss-Markov Theorem to hold, assumptions SR1-SR5 must be true. If any of these assumptions are not true, then b1 and b2 are not the best linear unbiased estimators of β1 and β2.
2.5The Gauss-
Markov Theorem
Principles of Econometrics, 4th Edition Page 61Chapter 2: The Simple Linear Regression Model
4. The Gauss-Markov Theorem does not depend on the assumption of normality (assumption SR6).
5. In the simple linear regression model, if we want to use a linear and unbiased estimator, then we have to do no more searching. The estimators b1 and b2 are the ones to use. This explains why we are studying these estimators and why they are so widely used in research, not only in economics but in all social and physical sciences as well.
6. The Gauss-Markov theorem applies to the least squares estimators. It does not apply to the least squares estimates from a single sample.
2.5The Gauss-
Markov Theorem MAJOR POINTS ABOUT THE GAUSS-MARKOV THEOREM
Principles of Econometrics, 4th Edition Page 62Chapter 2: The Simple Linear Regression Model
2.6 The Probability Distributions of the
Least Squares Estimators
Principles of Econometrics, 4th Edition Page 63Chapter 2: The Simple Linear Regression Model
If we make the normality assumption (assumption SR6 about the error term) then the least squares estimators are normally distributed:
2.6The Probability Distributions of
the Least Squares
Estimators
2 2
1 1 2
σ~ β , i
i
xb N
N x x
2
2 2 2
σ~ β ,i
b Nx x
Eq. 2.17
Eq. 2.18
Principles of Econometrics, 4th Edition Page 64Chapter 2: The Simple Linear Regression Model
If assumptions SR1-SR5 hold, and if the sample
size N is sufficiently large, then the least squares
estimators have a distribution that approximates the
normal distributions shown in Eq. 2.17 and Eq. 2.18
2.6The Probability Distributions of
the Least Squares
Estimators
A CENTRAL LIMIT THEOREM
Principles of Econometrics, 4th Edition Page 65Chapter 2: The Simple Linear Regression Model
2.7 Estimating the Variance of the Error
Term
Principles of Econometrics, 4th Edition Page 66Chapter 2: The Simple Linear Regression Model
The variance of the random error ei is:
if the assumption E(ei) = 0 is correct.
Since the “expectation” is an average value we might consider estimating σ2 as the average of the squared errors:
where the error terms are
2.7Estimating the Variance of the
Error Term
2 2 2var( ) σ [ ( )] ( )i i i ie E e E e E e
22σ ie
N
1 2β βi i ie y x
Principles of Econometrics, 4th Edition Page 67Chapter 2: The Simple Linear Regression Model
The least squares residuals are obtained by replacing the unknown parameters by their least squares estimates:
There is a simple modification that produces an unbiased estimator, and that is:
so that:
1 2
22
ˆ ˆ
ˆσ
i i i i i
i
e y y y b b x
eN
2ˆ
22
Nei
2 2σ σE
Eq. 2.19
2.7Estimating the Variance of the
Error Term
Principles of Econometrics, 4th Edition Page 68Chapter 2: The Simple Linear Regression Model
Replace the unknown error variance σ2 in Eq. 2.14 – Eq. 2.16 by to obtain:
2.7.1Estimating the Variance and Covariance of
the Least Squares
Estimators
2
22
1 2ˆvar( ) σ i
i
xb
N x x
2
2 2
σvar( )i
bx x
21 2 2ˆcov( , ) σ
i
xb bx x
Eq. 2.20
Eq. 2.21
Eq. 2.22
2.7Estimating the Variance of the
Error Term
Principles of Econometrics, 4th Edition Page 69Chapter 2: The Simple Linear Regression Model
The square roots of the estimated variances are the “standard errors” of b1 and b2:
1 1se( ) var( )b bEq. 2.23
Eq. 2.24 2 2se( ) var( )b b
2.7Estimating the Variance of the
Error Term
2.7.1Estimating the Variance and Covariance of
the Least Squares
Estimators
Principles of Econometrics, 4th Edition Page 70Chapter 2: The Simple Linear Regression Model
2.7.2Calculations for
the Food Expenditure
Data
22 ˆ 304505.2σ 8013.29
2 38ie
N
Table 2.3 Least Squares Residuals2.7
Estimating the Variance of the
Error Term
Principles of Econometrics, 4th Edition Page 71Chapter 2: The Simple Linear Regression Model
The estimated variances and covariances for a regression are arrayed in a rectangular array, or matrix, with variances on the diagonal and covariances in the “off-diagonal” positions.
1 1 2
1 2 2
var( ) cov( , )
cov( , ) var( )
b b b
b b b
2.7Estimating the Variance of the
Error Term
2.7.2Calculations for
the Food Expenditure
Data
Principles of Econometrics, 4th Edition Page 72Chapter 2: The Simple Linear Regression Model
For the food expenditure data the estimated covariance matrix is:
2.7.2Calculations for
the Food Expenditure
Data
C IncomeC 1884.442 -85.90316
Income -85.90316 4.381752
2.7Estimating the Variance of the
Error Term
Principles of Econometrics, 4th Edition Page 73Chapter 2: The Simple Linear Regression Model
The standard errors of b1 and b2 are measures of the sampling variability of the least squares estimates b1 and b2 in repeated samples. – The estimators are random variables. As such,
they have probability distributions, means, and variances.
– In particular, if assumption SR6 holds, and the random error terms ei are normally distributed, then:
2.7.3Interpreting the Standard Errors
222 2 2~ β , var( ) σ ib N b x x
2.7Estimating the Variance of the
Error Term
Principles of Econometrics, 4th Edition Page 74Chapter 2: The Simple Linear Regression Model
The estimator variance, var(b2), or its square root, which we might call the true standard deviation of b2, measures the sampling variation of the estimates b2
– The bigger is the more variation in the least squares estimates b2 we see from sample to sample. If is large then the estimates might change a great deal from sample to sample
– If is small relative to the parameter b2,we know that the least squares estimate will fall near b2 with high probability
2 2σ varb b
2b
2b
2b
2.7Estimating the Variance of the
Error Term
2.7.3Interpreting the Standard Errors
Principles of Econometrics, 4th Edition Page 75Chapter 2: The Simple Linear Regression Model
The question we address with the standard error is ‘‘How much variation about their means do the estimates exhibit from sample to sample?’’
2.7Estimating the Variance of the
Error Term
2.7.3Interpreting the Standard Errors
Principles of Econometrics, 4th Edition Page 76Chapter 2: The Simple Linear Regression Model
We estimate σ2, and then estimate using:
– The standard error of b2 is thus an estimate of what the standard deviation of many estimates b2 would be in a very large number of samples, and is an indicator of the width of the pdf of b2
shown in Figure 2.12
2b
2 2
2
2
se( ) var( )
σ
i
b b
x x
2.7Estimating the Variance of the
Error Term
2.7.3Interpreting the Standard Errors
Principles of Econometrics, 4th Edition Page 77Chapter 2: The Simple Linear Regression Model
Figure 2.12 The probability density function of the least squares estimator b2.2.7
Estimating the Variance of the
Error Term
2.7.3Interpreting the Standard Errors
Principles of Econometrics, 4th Edition Page 78Chapter 2: The Simple Linear Regression Model
2.8Estimating Nonlinear Relationships
Principles of Econometrics, 4th Edition Page 79Chapter 2: The Simple Linear Regression Model
Economic variables are not always related by straight-line relationships; in fact, many economic relationships are represented by curved lines, and are said to display curvilinear forms.
Fortunately, the simple linear regression model y = β1 + β2 + eis much more flexible than it looks at first glance, because the variables y and x can be transformations, involving logarithms, squares, cubes or reciprocals, of the basic economic variables, or they can be indicator variables that take only the values zero and one.
Including these possibilities means the simple linear regression model can be used to account for nonlinear relationships between variables
2.8Estimating Nonlinear
RelationshipsTHE WORLD IS NOT LINEAR
Principles of Econometrics, 4th Edition Page 80Chapter 2: The Simple Linear Regression Model
Consider the linear model of house prices:
where SQFT is the square footage.– It may be reasonable to assume that larger and
more expensive homes have a higher value for an additional square foot of living area than smaller, less expensive, homes
1 2β βPRICE SQFT e Eq. 2.25
2.8Estimating Nonlinear
Relationships
Principles of Econometrics, 4th Edition Page 81Chapter 2: The Simple Linear Regression Model
We can build this into our model in two ways:1. a quadratic equation in which the explanatory
variable is SQFT2
2. a loglinear equation in which the dependent variable is ln(PRICE)
– In each case we will find that the slope of the relationship between PRICE and SQFT is not constant, but changes from point to point.
2.8Estimating Nonlinear
Relationships
Principles of Econometrics, 4th Edition Page 82Chapter 2: The Simple Linear Regression Model
The quadratic function y = β1 + β2x2 is a parabola– The elasticity, or the percentage change in y
given a 1% change in x, is:
2.8.1Quadratic Functions
ybx
yxslope22
2.8Estimating Nonlinear
Relationships
Principles of Econometrics, 4th Edition Page 83Chapter 2: The Simple Linear Regression Model
Figure 2.13 A quadratic function2.8
Estimating Nonlinear
Relationships
2.8.1Quadratic Functions
Principles of Econometrics, 4th Edition Page 84Chapter 2: The Simple Linear Regression Model
A quadratic model for house prices includes the squared value of SQFT, giving:
– The slope is:
– If , then larger houses will have larger slope, and a larger estimated price per additional square foot
2.8.2Using a
Quadratic Model
21 2α αPRICE SQFT e
2
ˆˆ2α
d PRICESQFT
dSQFT
Eq. 2.26
Eq. 2.27
2α 0
2.8Estimating Nonlinear
Relationships
Principles of Econometrics, 4th Edition Page 85Chapter 2: The Simple Linear Regression Model
Figure 2.14 A fitted quadratic relationship2.8
Estimating Nonlinear
Relationships
2.8.2Using a
Quadratic Model
Principles of Econometrics, 4th Edition Page 86Chapter 2: The Simple Linear Regression Model
For 1080 houses sold in Baton Rouge, LA during mid-2005, the estimated quadratic equation is:
– The estimated slope is:
– The elasticity is:
255776 56 0 0154PRICE . . SQFT
2
ˆ
ˆ2α
SQFTslopePRICE
SQFTSQFTPRICE
2 0 0154slope . SQFT
2.8Estimating Nonlinear
Relationships
2.8.2Using a
Quadratic Model
Principles of Econometrics, 4th Edition Page 87Chapter 2: The Simple Linear Regression Model
To compute an estimate we must select values for SQFT and PRICE– A common approach is to choose a point on the
fitted relationship • That is, we choose a value for SQFT and
choose for price the corresponding fitted value
2.8Estimating Nonlinear
Relationships
2.8.2Using a
Quadratic Model
Principles of Econometrics, 4th Edition Page 88Chapter 2: The Simple Linear Regression Model
For houses of 2000, 4000 and 6000 square feet, the estimated elasticities are:
respectively
For a 2000-square-foot house, we estimate that a 1% increase in house size will increase price by 1.05%
1.05 using1.63 using1.82 using
7$117,461.7CEIPR 9$302,517.3CEIPR
2$610,943.4CEIPR
2.8Estimating Nonlinear
Relationships
2.8.2Using a
Quadratic Model
Principles of Econometrics, 4th Edition Page 89Chapter 2: The Simple Linear Regression Model
The log-linear equation ln(y) = a + bx has a logarithmic term on the left-hand side of the equation and an untransformed (linear) variable on the right-hand side – Both its slope and elasticity change at each
point and are the same sign as b• The slope is:
– The elasticity, the percentage change in y given a 1% increase in x, at a point on this curve is:
2.8.3A Log-Linear
Function
bydxdy
bxyxslope
2.8Estimating Nonlinear
Relationships
Principles of Econometrics, 4th Edition Page 90Chapter 2: The Simple Linear Regression Model
Using the slope expression, we can solve for a semi-elasticity, which tells us the percentage change in y given a 1-unit increase in x:
100η 100
dy dxb
dx Eq. 2.28
2.8Estimating Nonlinear
Relationships
2.8.3A Log-Linear
Function
Principles of Econometrics, 4th Edition Page 91Chapter 2: The Simple Linear Regression Model
Consider again the model for the price of a house as a function of the square footage, but now written in semi-log form:
– This logarithmic transformation can regularize data that is skewed with a long tail to the right
2.8.4Using a Log-Linear Model
1 2ln γ γPRICE SQFT e Eq. 2.29
2.8Estimating Nonlinear
Relationships
Principles of Econometrics, 4th Edition Page 92Chapter 2: The Simple Linear Regression Model
Figure 2.16 (a) Histogram of PRICE (b) Histogram of ln(PRICE)2.8
Estimating Nonlinear
Relationships
2.8.4Using a Log-Linear Model
Principles of Econometrics, 4th Edition Page 93Chapter 2: The Simple Linear Regression Model
Using the Baton Rouge data, the fitted log-linear model is:
– To obtain predicted price take the anti-logarithm, which is the exponential function:
ln 10.8386 0.0004113PRICE SQFT
exp ln exp 10.8386 0.0004113PRICE PRICE SQFT
2.8Estimating Nonlinear
Relationships
2.8.4Using a Log-Linear Model
Principles of Econometrics, 4th Edition Page 94Chapter 2: The Simple Linear Regression Model
Figure 2.17 The fitted log-linear model2.8
Estimating Nonlinear
Relationships
2.8.4Using a Log-Linear Model
Principles of Econometrics, 4th Edition Page 95Chapter 2: The Simple Linear Regression Model
The slope of the log-linear model is:
For a house with a predicted PRICE of $100,000, the estimated increase in PRICE for an additional square foot of house area is $41.13, and for a house with a predicted PRICE of $500,000, the estimated increase in PRICE for an additional square foot of house area is $205.63
2γ 0.0004113
d PRICEPRICE PRICE
dSQFT
2.8Estimating Nonlinear
Relationships
2.8.4Using a Log-Linear Model
Principles of Econometrics, 4th Edition Page 96Chapter 2: The Simple Linear Regression Model
The estimated elasticity is:
– For a house with 2000-square-feet, the estimated elasticity is 0.823:• A 1% increase in house size is estimated to
increase selling price by 0.823%– For a house with 4000 square feet, the
estimated elasticity is 1.645: • A 1% increase in house size is estimated to
increase selling price by 1.645%
2ˆ γ 0.0004113SQFT SQFT
2.8Estimating Nonlinear
Relationships
2.8.4Using a Log-Linear Model
Principles of Econometrics, 4th Edition Page 97Chapter 2: The Simple Linear Regression Model
Using the ‘‘semi-elasticity’’ defined in Eq. 2.28 we can say that, for a one-square-foot increase in size, we estimate a price increase of 0.04%– Or, perhaps more usefully, we estimate that a
100-square-foot increase will increase price by approximately 4%.
2.8Estimating Nonlinear
Relationships
2.8.4Using a Log-Linear Model
Principles of Econometrics, 4th Edition Page 98Chapter 2: The Simple Linear Regression Model
We should do our best to choose a functional form that is:– consistent with economic theory– that fits the data well– that is such that the assumptions of the
regression model are satisfied
2.8.5Choosing a
Functional Form
2.8Estimating Nonlinear
Relationships
Principles of Econometrics, 4th Edition Page 99Chapter 2: The Simple Linear Regression Model
In real-world problems it is sometimes difficult to achieve all these goals– Furthermore, we will never truly know the
correct functional relationship, no matter how many years we study econometrics
– The truth is out there, but we will never know it – In applications of econometrics we must simply
do the best we can to choose a satisfactory functional form
2.8Estimating Nonlinear
Relationships
2.8.5Choosing a
Functional Form
Principles of Econometrics, 4th Edition Page 100Chapter 2: The Simple Linear Regression Model
2.9Regression with Indicator Variables
Principles of Econometrics, 4th Edition Page 101Chapter 2: The Simple Linear Regression Model
An indicator variable is a binary variable that takes the values zero or one; it is used to represent a nonquantitative characteristic, such as gender, race, or location
– How do we model this?
2.9Regression with
Indicator Variables
OaksGolden in is house 0
Townity in Univers is house 1UTOWN
1 2β βPRICE UTOWN e
Principles of Econometrics, 4th Edition Page 102Chapter 2: The Simple Linear Regression Model
Figure 2.18 Distributions of house prices2.9
Regression with Indicator Variables
Principles of Econometrics, 4th Edition Page 103Chapter 2: The Simple Linear Regression Model
1 2
1
β β if 1β if 0
UTOWNE PRICE
UTOWN
1 2
215.7325 61.5091277.2416 if 1215.7325 if 0
PRICE b b UTOWNUTOWN
UTOWNUTOWN
2.9Regression with
Indicator Variables
When an indicator variable is used in a regression, it is important to write out the regression function for the different values of the indicator variable
– The estimated regression is:
1 2
1
β β if 1β if 0
UTOWNE PRICE
UTOWN
1 2
215.7325 61.5091277.2416 if 1215.7325 if 0
PRICE b b UTOWNUTOWN
UTOWNUTOWN
Principles of Econometrics, 4th Edition Page 104Chapter 2: The Simple Linear Regression Model
The least squares estimators b1 and b2 in this indicator variable regression can be shown to be:
– In the simple regression model, an indicator variable on the right-hand side gives us a way to estimate the differences between population means
OaksGolden Town University2
OaksGolden 1
PRICEPRICEb
PRICEb
2.9Regression with
Indicator Variables
Principles of Econometrics, 4th Edition Page 105Chapter 2: The Simple Linear Regression Model
Key Words
Principles of Econometrics, 4th Edition Page 106Chapter 2: The Simple Linear Regression Model
assumptions asymptotic B.L.U.E. biased estimator degrees of freedom dependent variable deviation from the
mean form econometric model economic model elasticity Gauss-Markov
Theorem heteroskedastic
homoskedastic independent
variable least squares
estimates least squares
estimators least squares
principle least squares
residuals linear estimator prediction random error term
regression model regression
parameters repeated sampling sampling precision sampling properties scatter diagram simple linear
regression function specification error unbiased estimator
Keywords
Principles of Econometrics, 4th Edition Page 107Chapter 2: The Simple Linear Regression Model
Appendices
Principles of Econometrics, 4th Edition Page 108Chapter 2: The Simple Linear Regression Model
2A Derivation of the Least Squares Estimates2B Deviation from the Mean Form of b2
2C b2 is a Linear Estimator2D Derivation of Theoretical Expression for b2
2E Deriving the Variance of b2
2F Proof of the Gauss-Markov Theorem
Principles of Econometrics, 4th Edition Page 109Chapter 2: The Simple Linear Regression Model
2ADerivation of the
Least Squares Estimates
Eq. 2A.1
Eq. 2A.2
21 2 1 2
1
1 21
22 1
2
(β ,β ) ( β β )
2 β 2 2 ββ
2 β 2 2 ββ
N
i ii
i i
i i i i
S y x
S N y x
S x x y x
Principles of Econometrics, 4th Edition Page 110Chapter 2: The Simple Linear Regression Model
Figure 2A.1 The sum of squares function and the minimizing values b1 and b2
2ADerivation of the
Least Squares Estimates
Principles of Econometrics, 4th Edition Page 111Chapter 2: The Simple Linear Regression Model
Eq. 2A.3
Eq. 2A.4
02
02
22
1
21
bxbxyx
bxNby
iiii
ii
Set the derivatives equal to zero to get two equations:
1 2
21 2
i i
i i i i
Nb x b y
x b x b x y
Simplify these to:
2ADerivation of the
Least Squares Estimates
Principles of Econometrics, 4th Edition Page 112Chapter 2: The Simple Linear Regression Model
Eq. 2A.5
Solving the two equations simultaneously, we get for b2:
222
ii
iiii
xxN
yxyxNb
2ADerivation of the
Least Squares Estimates
Principles of Econometrics, 4th Edition Page 113Chapter 2: The Simple Linear Regression Model
2BDeviation from the Mean Form
of b2
Eq. 2B.1
We can rewrite the equation 2A.5 by noting the following:
22
222
22
222
2
12
2
xNx
xNxNx
xNxN
Nxx
xNxxxxx
i
i
ii
iii
Also note that:
Nx
x
xxx
xNxxx
ii
ii
ii
22
2
222
Eq. 2B.2
Principles of Econometrics, 4th Edition Page 114Chapter 2: The Simple Linear Regression Model
Eq. 2B.3
Finally, we have:
Nyx
yx
yxNyxyyxx
iiii
iiii
2BDeviation from the Mean Form
of b2
Principles of Econometrics, 4th Edition Page 115Chapter 2: The Simple Linear Regression Model
We can rewrite b2 in deviation from the mean form as:
22 xx
yyxxb
i
ii
2BDeviation from the Mean Form
of b2
Principles of Econometrics, 4th Edition Page 116Chapter 2: The Simple Linear Regression Model
First note that we will always have:
0 xxi
2BDeviation from the Mean Form
of b2
Principles of Econometrics, 4th Edition Page 117Chapter 2: The Simple Linear Regression Model
Now rewrite our formula for b2 and use this fact:
ii
ii
i
i
ii
i
iii
i
ii
yw
yxx
xx
xxyxx
xxxxyyxx
xxyyxx
b
2
2
2
22
2Cb2 is a Linear
Estimator
Principles of Econometrics, 4th Edition Page 118Chapter 2: The Simple Linear Regression Model
2DDerivation of Theoretical
Expression for b2
To obtain Eq. 2.12, replace yi in Eq. 2.11 by yi = β1+ β2xi +ei and simplify:
2
1 2
1 2
2
β β
β β
β
i i
i i i
i i i i i
i i
b w y
w x e
w w x w e
w e
Principles of Econometrics, 4th Edition Page 119Chapter 2: The Simple Linear Regression Model
For this, we used the facts that:
2
2
2 2
1
0
1
β β
0
ii
i
i
i
i i
i i
i
x xw
x x
x xx x
w x
w x
x x
2DDerivation of Theoretical
Expression for b2
Principles of Econometrics, 4th Edition Page 120Chapter 2: The Simple Linear Regression Model
We can show that by using:
ii
iii
iii
xxx
xxxxxx
xxxxxx
2
so that:
1ii xw
1
2
ii
ii
i
iiii
xxxxxx
xxxxx
xw
2DDerivation of Theoretical
Expression for b2
Principles of Econometrics, 4th Edition Page 121Chapter 2: The Simple Linear Regression Model
2EDeriving the
Variance of b2
First note that:
2 2β i ib w e and that:
2222 )()var( bEbEb
Principles of Econometrics, 4th Edition Page 122Chapter 2: The Simple Linear Regression Model
2
2 2 2
2
2 2
2 2i
2 2
2
2
var( ) β β
2
E e 2
σ
σ
i i
i i
i i i j i ji j
i i j i ji j
i
i
b E w e
E w e
E w e w w e e
w w w E e e
w
x x
2EDeriving the
Variance of b2
Principles of Econometrics, 4th Edition Page 123Chapter 2: The Simple Linear Regression Model
2 22 2
222
2 2 22 2
2 2
σ var( ) ( ) 0
cov , 0
1
var( ) var( ) var( ) 2cov( , )
i i i i i
i j i i j j i j
iii
ii i
e E e E e E e E e
e e E e E e e E e E e e
x xx xw
x xx x x x
aX by a X b Y X Y
2EDeriving the
Variance of b2
Principles of Econometrics, 4th Edition Page 124Chapter 2: The Simple Linear Regression Model
2 2
2
2
2 2
2
2
var( ) var
var cov( , )
var( )
σ
σ
i i
i i i j i ji j
i i
i
i
b w e
w e w w e e
w e
w
x x
[since is a constant]2β
[generalizing the variance rule]
[using cov(ei,ej) = 0]
[using var(ei) = ]2
2EDeriving the
Variance of b2
Principles of Econometrics, 4th Edition Page 125Chapter 2: The Simple Linear Regression Model
2FProof of the
Gauss-Markov Theorem
To begin, let be any other linear estimator of β1. Also, suppose ki = wi + ci. Then:
ii ykb*2
*2 1 2
1 2
1 1 2 2
1 2 2
β β
β β
β β β β
β β β
i i i i i i i i i
i i i i i i i i
i i i i i i i i i
i i i i i i
b k y w c y w c x e
w c w c x w c e
w c w x c x w c e
c c x w c e
Eq. 2F.1
Principles of Econometrics, 4th Edition Page 126Chapter 2: The Simple Linear Regression Model
Now:
*2 1 2 2
1 2 2
*2 2
β β β
β β β
0 and 0
β
i i i i i i
i i i
i i i
i i i i i
E b c c x w c E e
c c x
c c x
b k y w c e
Eq. 2F.2
Eq. 2F.3
Eq. 2F.4
2FProof of the
Gauss-Markov Theorem
Principles of Econometrics, 4th Edition Page 127Chapter 2: The Simple Linear Regression Model
2 2 2
1 0i ii i i i i
i i i
c x x xc w c x cx x x x x x
For now, observe that :
Now we can write:
*2 2
2
22
2 2 2 2
2 22
2
var( ) var β
var
σ
σ σ
var σ
var
i i i
i i i
i i
i i
i
b w c e
w c e
w c
w c
b c
b
2FProof of the
Gauss-Markov Theorem
Principles of Econometrics, 4th Edition Page 128Chapter 2: The Simple Linear Regression Model
Monte Carlo simulation experiments use random number generators to replicate the random way that data are obtained– In Monte Carlo simulations we specify a data
generation process and create samples of artificial data• Then we ‘‘try out’’ estimation methods on the data
we have created– We create many samples of size N and examine the
repeated sampling properties of the estimators• In this way, we can study how statistical
procedures behave under ideal, as well as not so ideal, conditions
2GMonte Carlo Simulation
Principles of Econometrics, 4th Edition Page 129Chapter 2: The Simple Linear Regression Model
The data generation process for the simple linear regression model is given by:
– Each value of the dependent variable yi is obtained, or generated, by adding a random error ei to the regression function E(yi |xi )
– To simulate values of yi we create values for the systematic portion of the regression relationship E(yi |xi ) and add to it the random error ei
NiexbbexyEy
ii
iiii
,...,1
|
21
2GMonte Carlo Simulation
Principles of Econometrics, 4th Edition Page 130Chapter 2: The Simple Linear Regression Model
To create the variables for the regression function, we must:1. Select sample size N2. Choose xi values3. Choose β1 and β2
2G.1The Regression
Function
2GMonte Carlo Simulation
Principles of Econometrics, 4th Edition Page 131Chapter 2: The Simple Linear Regression Model
To be consistent with assumptions SR2–SR4 the random errors should have mean zero, a constant variance and be uncorrelated with one another–We can generate random numbers– Of course the computer-generated numbers
cannot be truly random, because they are generated by a computer code
– The random numbers created by computer software are ‘‘pseudorandom,’’ in that they behave like random numbers
2G.2The Random
Error
2GMonte Carlo Simulation
Principles of Econometrics, 4th Edition Page 132Chapter 2: The Simple Linear Regression Model
2G.3Theoretically True Values
Figure 2G.1 The true probability density functions of the data2G
Monte Carlo Simulation
Principles of Econometrics, 4th Edition Page 133Chapter 2: The Simple Linear Regression Model
Figure 2G.2 The true probability density functions of the estimator b2
2GMonte Carlo Simulation
2G.3Theoretically True Values
Principles of Econometrics, 4th Edition Page 134Chapter 2: The Simple Linear Regression Model
2G.4Creating a
Sample of Data
ˆ 75.7679 11.9683
se 25.7928 1.6313
ˆ σ 51.5857
i iy x
b1 b2
b1 665.2699 -39.9162
b2 -39.9162 2.6611
Variance-Covariance Matrix
2GMonte Carlo Simulation
Principles of Econometrics, 4th Edition Page 135Chapter 2: The Simple Linear Regression Model
What do we hope to achieve with a Monte Carlo experiment?–We would like to verify that under SR1–SR5 the
least squares estimators are unbiased–We would like to verify that under SR1–SR5 the
least squares estimators have sampling variances given by Eq. 2.14 and Eq. 2.16
–We would like to verify that the estimator of the error variance Eq. 2.19 is unbiased
– Because we have assumed the random errors are normal, SR6, we expect the least squares estimates to have a normal distribution.
2G.5Monte Carlo Objectives
2GMonte Carlo Simulation
Principles of Econometrics, 4th Edition Page 136Chapter 2: The Simple Linear Regression Model
2G.6Monte Carlo
Results
Table 2G.1 Summary of 1,000 Monte Carlo Samples2G
Monte Carlo Simulation
Principles of Econometrics, 4th Edition Page 137Chapter 2: The Simple Linear Regression Model
Figure 2G.3 The sampling distribution of b2 in 1000 Monte Carlo samples2G
Monte Carlo Simulation
2G.6Monte Carlo
Results