Transcript Chapter 9

Chapter 9
Simultaneous Equations Models
(聯立方程式模型)
What is in this Chapter?
• How do we detect this problem?
• What are the consequences?
• What are the solutions?
What is in this Chapter?
• In Chapter 4 we mentioned that one of the
assumptions in the basic regression model
is that the explanatory variables are
uncorrelated with the error term
• In this chapter we relax that assumption
and consider the case where several
variables are jointly determined
– Predetermined vs. jointly determined
– Exogenous vs. Endogenous
What is in this Chapter?
• This chapter first discusses the conditions
under which equations are estimable in
the case of jointly determined variables
(the "identification problem") and methods
of estimation
• One major method is that of "instrumental
variables"
• Finally, this chapter also discusses
causality
9.1 Introduction
• In the usual regression model y is the
dependent or determined variable and x1,
x2, x3... Are the independent or
determining variables
• The crucial assumption we make is that
the x's are independent of the error term u
• Sometimes, this assumption is violated: for
example, in demand and supply models
9.1 Introduction
• Suppose that we write the demand function as:
• where q is the quantity demanded, p the price,
and u the disturbance term which denotes
random shifts in the demand function
• In Figure 9.1 we see that a shift in the demand
function produces a change in both price and
quantity if the supply curve has an upward
slope
9.1 Introduction
• If the supply curve is horizontal (i.e.,
completely price inelastic), a shift in the
demand curve produces a change in price
only
• If the supply curve is vertical (infinite price
elasticity), a shift in the demand curve
produces a change in quantity only
9.1 Introduction
• Thus in equation (9.1) the error term u is
correlated with p when the supply curve is
upward sloping or perfectly horizontal
• Hence an estimation of the equation by
ordinary least squares produces
inconsistent estimates of the parameters
9.2 Endogenous and Exogenous
Variables
• In simultaneous equations models
variables are classified as endogenous
and exogenous
• The traditional definition of these terms is
that endogenous variables are variables
that are determined by the economic
model and exogenous variables are those
determined from outside
9.2 Endogenous and Exogenous
Variables
• Endogenous variables are also called jointly
determined and exogenous variables are called
predetermined. (It is customary to include past
values of endogenous variables in the
predetermined group.)
• Since the exogenous variables are
predetermined, they are independent of the error
terms in the model
• They thus satisfy the assumptions that the x's
satisfy in the usual regression model of y on x's
9.2 Endogenous and Exogenous
Variables
• Consider now the demand and supply mode
q = a1 + b1p + c1 y + u1 demand function
q = a2 + b2p + c2R + u2 supply function (9.2)
• q is the quantity, p the price, y the income, R the
rainfall, and u1 and u2 are the error terms
• Here p and q are the endogenous variables and
y and R are the exogenous variables
9.2 Endogenous and Exogenous
Variables
• Since the exogenous variables are independent
of the error terms u1 and u2 and satisfy the usual
requirements for ordinary least squares
estimation, we can estimate regressions of p
and q on y and R by ordinary least squares,
although we cannot estimate equations (9.2)by
ordinary least squares
• We will show presently that from these
regressions of p and q on y and R we can
recover the parameters in the original demand
and supply equations (9.2)
9.2 Endogenous and Exogenous
Variables
• This method is called indirect least
squares—it is indirect because we do not
apply least squares to equations (9.2)
• The indirect least squares method does
not always work, so we will first discuss
the conditions under which it works and
how the method can be simplified. To
discuss this issue, we first have to clarify
the concept of identification
9.3 The Identification Problem:
Identification Through Reduced Form
• We have argued that the error terms u1 and u2
are correlated with p in equations (9.2),and
hence if we estimate the equation by ordinary
least squares, the parameter estimates are
inconsistent
• Roughly speaking, the concept of identification is
related to consistent estimation of the
parameters
• Thus if we can somehow obtain consistent
estimates of the parameters in the demand
function, we say that the demand function is
identified
9.3 The Identification Problem:
Identification Through Reduced Form
• Similarly, if we can somehow get
consistent estimates of the parameters in
the supply function, we say that the supply
function is identified
• Getting consistent estimates is just a
necessary condition for identification, not a
sufficient condition, as we show in the next
section
9.3 The Identification Problem:
Identification Through Reduced Form
• If we solve the two equations in(9.2) for q and p in
terms of y and R, we get
• These equations are called the reduced-form
equations.
• Equation (9.2) are called the structural equations
because they describe the structure of the economic
system.
9.3 The Identification Problem:
Identification Through Reduced Form
• We can write equations (9.3) as
where v1 and v2 are error terms and
9.3 The Identification Problem:
Identification Through Reduced Form
• The π’s are called reduced-form parameters.
• The estimation of the equations (9.4) by ordinary
least squares gives us consistent estimates of
the reduced form parameters.
• From these we have to obtain consistent
estimates of the parameters in
9.3 The Identification Problem:
Identification Through Reduced Form
• Since aˆ1,aˆ2 ,bˆ1,bˆ2 ,cˆ1,cˆ2 are all single-valued
function of the ˆ ,they are consistent estimates
of the corresponding structural parameters.
• As mentioned earlier, this method is known as
the indirect least squares method.
9.3 The Identification Problem:
Identification Through Reduced Form
• It may not be always possible to get estimates of
the structural coefficients from the estimates of
the reduced-form coefficients, and sometimes
we get multiple estimates and we have the
problem of choosing between them.
• For example, suppose that the demand and
supply model is written as
9.3 The Identification Problem:
Identification Through Reduced Form
• Then the reduced from is
9.3 The Identification Problem:
Identification Through Reduced Form
or
• In this casebˆ2  ˆ2 / ˆ4 and aˆ2  ˆ1 / bˆ2ˆ3 .
• But these is no way of getting estimates of a1, b1,
and c1.
• Thus the supply function is identified but the
demand function is not.
9.3 The Identification Problem:
Identification Through Reduced Form
• On the other hand, suppose that we have the
model
• Now we can check that the demand function is
identified but the supply function is not.
9.3 The Identification Problem:
Identification Through Reduced Form
• Finally, suppose that we have the system
9.3 The Identification Problem:
Identification Through Reduced Form
or
• Now we get two estimates of b2.
• One is bˆ2  ˆ2 / ˆ5 and the other is bˆ2  ˆ3 / ˆ6 , and
these need not be equal.
• For each of these we get an estimate of a2, which
is aˆ2  ˆ1 / bˆˆ4 .
9.3 The Identification Problem:
Identification Through Reduced Form
• On the other hand, we get no estimate for the
parameters a1 , b1, c1, and d1 of the demand
function.
• Here we say that the supply function is
overidentified and the demand function is
underidentified.
• When we get unique estimates for the structural
parameters of an equation fro, the reduced-form
parameters, we say that the equation is exactly
identified.
9.3 The Identification Problem:
Identification Through Reduced Form
• When we get multiple estimates, we say that the
equation is overidentified, and when we get no
estimates, we say that the equation is
underidentified (or not identified).
• There is a simple counting rule available in the
linear systems that we have been considering.
• This counting rule is also known as the order
condition for identification.
9.3 The Identification Problem:
Identification Through Reduced Form
• This rule is as follows: Let g be the number of
endogenous variables in the system and k the
total number of variables (endogenous and
exogenous) missing from the equation under
consideration.
• Then
9.3 The Identification Problem:
Identification Through Reduced Form
• This condition is only necessary but not
sufficient.
• Let us apply this rule to the equation
systems we are considering.
• In equations (9.2), g, the number of
endogenous variable, is 2 and there is
only one variable missing from each
equation (i.e., k=1).
• Both equations are identified exactly.
9.3 The Identification Problem:
Identification Through Reduced Form
• In equations (9.5), again g=2.
– There is no variable missing from the first equation
(i.e., k=0); hence it is underidentified.
– There is one variable missing in the second equation
(i.e., k=1); hence it is exactly identifies.
• In equation (9.6)
– there is no variable missing in the first equation;
hence it is not identified.
– In the second equation there are two variables
missing; thus k>g-1 and the equation is overidentified.
9.3 The Identification Problem:
Identification Through Reduced Form
• Illustrative Example
– In Table 9.1 data are presented for
demand and supply of pork in the United
States for 1922-1941
9.3 The Identification Problem:
Identification Through Reduced Form
•
•
•
•
Pt, retail price of pork (cents per pound)
Qt, consumption of pork (pounds per capita)
Yt, disposable personal income (dollars per capital)
Zt, “predetermined elements in pork production.”
9.3 The Identification Problem:
Identification Through Reduced Form
• The coefficient of Y in the second equation is very close
to zero and the variable Y can be dropped from this
equation.
• This would imply that b2=0, or supply is not responsive to
price.
• In any case, solving from the reduced from to the
structural from, we get the estimates of the structural
equation as
9.3 The Identification Problem:
Identification Through Reduced Form
• The least squares estimates of the demand
function are:
– Normalized with respect to Q
– Normalized with respect to P
9.3 The Identification Problem:
Identification Through Reduced Form
• The structural demand function can also be written
in the two forms:
– Normalized with respect to Q
– Normalized with respect to P
• The estimates of the parameters in the demand
function are almost the same with the direct least
squares method as with the indirect least squares
method when the demand function is normalized
with respect to P.
9.3 The Identification Problem:
Identification Through Reduced Form
• Which is the correct normalization?
• We argued in Section 9.1 that if quantity
supplied is not responsive to price, the demand
function should be normalized with respect to P.
• We saw that fact the coefficient of Y in the
reduced-form equation for Q was close to zero
implied that b2=0 or quantity supplied is not
responsive to price.
9.3 The Identification Problem:
Identification Through Reduced Form
• This is also confirmed by the structural estimate
of b2, which show a wrong sign for b2 as well but
a coefficient close to zero.
• Dropping P from the supply function and using
OLS, we get the supply function as
9.5 Methods of Estimation: The
Instrumental Variable Method
• In previous sections we discussed the
indirect least squares method
– However, this method is very cumbersome if
there are many equations and hence it is not
often used
– Identification problem
• Here we discuss some methods that are
more generally applicable
– The Instrumental Variable Method
9.5 Methods of Estimation: The
Instrumental Variable Method
• Broadly speaking, an instrumental variable
is a variable that is uncorrelated with the
error term but correlated with the
explanatory variables in the equation
• For instance, suppose that we have the
equation
y = ßx + u
9.5 Methods of Estimation: The
Instrumental Variable Method
• where x is correlated with u
• Then we cannot estimate this equation by
ordinary least squares
• The estimate of ß is inconsistent because of the
correlation between x and u
• If we can find a variable z that is uncorrelated
with u, we can get a consistent estimator for ß
• We replace the condition cov (z, u) = 0 by its
sample counterpart
9.5 Methods of Estimation: The
Instrumental Variable Method
1
z ( y   x)  0

n
• This gives
• But
 zu / zx can be written as (1/ n) zu /(1/ n) zx
9.5 Methods of Estimation: The
Instrumental Variable Method
• The probability limit of this expression is
since cov (z, x) ≠0.
• Hence plim ˆ   ,thus proving that ˆ is a
consistent estimator for β.
• Note that we require z to be correlated with x so
that cov (z, x) ≠0.
9.5 Methods of Estimation: The
Instrumental Variable Method
• Now consider the simultaneous equations model
where y1, y2 are endogenous variables, z1, z2, z3
are exogenous variables, and u1, u2 are error
term.
• Since z1 and z2 are independent of u1,
– cov (z1, u1) =0 , cov (z2, u1) =0
• However, y2 is not independent of u1
– cov (y2, u1) ≠0.
9.5 Methods of Estimation: The
Instrumental Variable Method
• Since we have three coefficients to estimate, we
have to find a variable that is independent of u1.
• Fortunately, in this case we have z3 and
cov(z3,u1)=0.
• z3 is the instrumental variable for y2.
• Thus, writing the sample counterparts of these
three covariances, we have three equations
9.5 Methods of Estimation: The
Instrumental Variable Method
• The difference between the normal equation for the
ordinary least squares method and the instrumental
variable method is only in the last equation.
9.5 Methods of Estimation: The
Instrumental Variable Method
• Consider the second equation of our model
• Now we have to find an instrumental variable for
y1 but we have a choice of z1 and z2
• This is because this equation is overidentified
(by the order condition)
• Note that the order condition (counting rule) is
related to the question of whether or not we
have enough exogenous variables elsewhere in
the system to use as instruments for the
endogenous variables in the equation with
unknown coefficients
9.5 Methods of Estimation: The
Instrumental Variable Method
• If the equation is underidentified we do not have
enough instrumental variables
• If it is exactly identified, we have just enough
instrumental variables
• If it is overidentified, we have more than enough
instrumental variables
– In this case we have to use weighted averages of the
instrumental variables available
– We compute these weighted averages so that we get
the most efficient (minimum asymptotic variance)
estimator
9.5 Methods of Estimation: The
Instrumental Variable Method
• It has been shown (proving this is beyond the
scope of this book) that the efficient instrumental
variables are constructed by regressing the
endogenous variables on all the exogenous
variables in the system (i.e., estimating the
reduced-form equations).
• In the case of the model given by equations (9.8),
we first estimate the reduced-form equations by
regressing y1 and y2 on z1, z2, z3.
• We obtain the predicted values yˆ1 and yˆ2 and
use these as instrumental variables.
9.5 Methods of Estimation: The
Instrumental Variable Method
• For the estimation of the first equation we
use yˆ 2 , and for the estimation of the second
equation we use yˆ1 .
• We can write yˆ1 and yˆ 2 as linear function of z1,
z 2, z3.
• Let us write
where the a’s are obtained from the estimation of
the reduced-form equations by OLS.
9.5 Methods of Estimation: The
Instrumental Variable Method
• In the estimation of the first equation in (9.8) we use
z1, z2, and z3 as instruments.
• This is the same as using z1, z2, z3 as instruments
because
,
• But the first two terms are zero by virtue of the first two
equations in (9.8’).
• Thus  yˆ2 u 1 0   z3 u 1 0 . Hence using yˆ 2 as an
instrumental variable is the same as using z3 as an
instrumental variable.
• This is the case with exactly indentified equations where
there is no choice in the instruments.
9.5 Methods of Estimation: The
Instrumental Variable Method
• The case with the second equation in (9.8) is different.
• Earlier, we said that we had a choice between z1 and z2
as instruments for y1.
• The use of yˆ1 gives the optimum weighting.
• The normal equations now are

since
z3u 2 0 .
• Thus the optimal weights for z1 and z2 are a11 and a12.
9.5 Methods of Estimation: The
Instrumental Variable Method
Illustrative Example
• Table 9.2 provides data on some characteristics
of the wine industry in Australia for 1955-1956 to
1974-1975.
• The demand-supply model for the wine industry
9.5 Methods of Estimation: The
Instrumental Variable Method
9.5 Methods of Estimation: The
Instrumental Variable Method
where Qt= real capital consumption of wine
ptw = price of wine relative to CPI
ptb = price of beer relative to CPI
Yt= real per capital disposable income
At= real per capital advertising expenditure
St= index of storage costs
• Qt and Pt w are the endogenous variables
• The other variable are exogenous.
9.5 Methods of Estimation: The
Instrumental Variable Method
• For the estimation of the demand function we have only
one instrumental variable St.
• But for the estimation of the supply function we have
available three instrumental variables: Pt b , Yt , and At .
• The OLS estimation of the demand function gave the
following results (all variables are in logs and figures in
parentheses are t-ratios):
• All the coefficients except that of Y have the wrong signs.
• The coefficient of Pw not only has the wrong sign but is
also significant.
9.5 Methods of Estimation: The
Instrumental Variable Method
• Treating Pw as endogenous and using S as an
instrument, we get following results:
• The coefficient of Pw still has a wrong sign but it is at
least not significant.
• In any case the conclusion we arrive at is that the
quantity demanded is not responsive to prices and
advertising expenditures but is responsive to income.
• The income elasticity of demand for wine is about
4.0 (significantly greater than unity).
9.6 Methods of Estimation: The
Two-Stage Least Squares Method
• The 2SLS method differs the IV method
described in Section 9.5 in that the yˆ ‘s are used
as regressors rather than as instruments, but the
two methods give identical estimates.
• Consider the equation to be estimated:
y1  b1 y2  c1 z1  u1
(9.9)
• The other exogenous variables in the system
are z2, z3, and z4.
9.6 Methods of Estimation: The
Two-Stage Least Squares Method
• Let yˆ 2 be the predicted value of y2 from, a
regression on y2 on z1, z2, z3, and z4 (the
reduces-form equation).
• Then y2  yˆ 2  v2
where v2, the residual, is
uncorrelated with each of the regressors, z1, z2,
z3, and z4 and hence with yˆ 2 as well. (This is the
property of least squares regression that we
discussed in Chapter 4.)
9.6 Methods of Estimation: The
Two-Stage Least Squares Method
• The normal equations for the efficient IV method
are
• Substituting y2  yˆ 2  v2 we get
9.6 Methods of Estimation: The
Two-Stage Least Squares Method
• But these are the normal equations if we
replace y2 by yˆ 2 in (9.9) and estimate the
equation by OLS.
• This method of replacing the endogenous
variables on the right-hand side by their
predicted values from the reduced form and
estimating the equation by OLS is called the
two-stage least squares (2SLS) method.
9.6 Methods of Estimation: The
Two-Stage Least Squares Method
• The name arises from the fact that OLS is used
in two stages:
Stage 1. Estimate the reduced-form equations by
OLS and obtain the predicted yˆ ‘s.
Stage 2.Replace the right-hand side endogenous
variables by yˆ ‘s and estimate the
equation by OLS.
9.6 Methods of Estimation: The
Two-Stage Least Squares Method
• Note that the estimates do not change even if
we replace y1 by yˆ1 in equation (9.9).
• Take the normal equations (9.12).
• y1  yˆ1  v1
• Now substitute y1  yˆ1  v1 in equations (9.12).
• We get
9.6 Methods of Estimation: The
Two-Stage Least Squares Method
• The last terms of these two equations are zero
and the equations that remain are the normal
equations from the OLS estimation of the
equation
yˆ1  b1 yˆ 2  c1z1  w
• Thus in stage 2 of the 2SLS method we can
replace all the endogenous variables in the
equation by their predicted values from the
reduced forms and then estimate the equation
by OLS.
9.10 Granger Causality
• Granger starts from the premise that the future
cannot cause the present or the past.
• If event A occurs after event B, we know that A
cannot cause B.
• At the same time, if A occurs before B, it does
not necessarily imply that A causes B.
• For instance, the weatherman's prediction
occurs before the rain. This does not mean that
the weatherman causes the rain.
9.10 Granger Causality
• In practice, we observe A and B as time series
and we would like to know whether A precedes B,
or B precedes A, or they are contemporaneous
• For instance, do movements in prices precede
movements in interest rates, or is it the opposite,
or are the movements contemporaneous?
• This is the purpose of Granger causality
• It is not causality as it is usually understood
9.10 Granger Causality
• Granger devised some tests for causality (in the limited
sense discussed above) which proceed as follows.
• Consider two time series, {yt} and {xt}.
• The series xt fails to Granger cause yt if in a regression
of yt on lagged y’s and lagged x’s, the coefficients of the
latter are zero.
• That is, consider
• Then if βi=0 (i=1,2,....,k), xt fails to cause yt.
• The lag length k is, to some extent, arbitrary.
9.10 Granger Causality
• Learner suggests using the simple word
"precedence" instead of the complicated words
Granger causality since all we are testing is
whether a certain variable precedes another and
we are not testing causality as it is usually
understood
• However, it is too late to complain about the term
since it has already been well established in the
econometrics literature. Hence it is important to
understand what it means