Ch7 slides

‘Introductory Econometrics for Finance’ © Chris Brooks 2013 1
Chapter 7
Multivariate models

Simultaneous Equations Models
• All the models we have looked at thus far have been single equations models of
the form y = X + u
• All of the variables contained in the X matrix are assumed to be EXOGENOUS.
• y is an ENDOGENOUS variable.
An example from economics to illustrate - the demand and supply of a good:
(1)
(2)
(3)
where = quantity of the good demanded
= quantity of the good supplied
St = price of a substitute good
Tt = some variable embodying the state of technology
Q P S u
dt t t t
   
  
Q P T v
st t t t
   
  
Q Q
dt st

Qdt
Qst

• Assuming that the market always clears, and dropping the time subscripts
for simplicity
(4)
(5)
This is a simultaneous STRUCTURAL FORM of the model.
• The point is that price and quantity are determined simultaneously (price
affects quantity and quantity affects price).
• P and Q are endogenous variables, while S and T are exogenous.
• We can obtain REDUCED FORM equations corresponding to (4) and (5)
by solving equations (4) and (5) for P and for Q (separately).
Simultaneous Equations Models:
The Structural Form
Q P S u
   
  
Q P T v
   
  

• Solving for Q,
(6)
• Solving for P,
(7)
• Rearranging (6),
(8)
Obtaining the Reduced Form
  
  
P S u    
  
P T v
Q S u Q T v




  



 
      
     
P P T S v u
      
( ) ( ) ( )
     
      
P T S v u
P T S
v u










 
 

 

   

• Multiplying (7) through by ,
(9)
• (8) and (9) are the reduced form equations for P and Q.
Obtaining the Reduced Form (cont’d)
       
Q S u Q T v
      
       
Q Q T S u v
      
( ) ( ) ( )
       
      
Q T S u v
Q T S
u v










 
 

 

 
 
 

• But what would happen if we had estimated equations (4) and (5), i.e. the
structural form equations, separately using OLS?
• Both equations depend on P. One of the CLRM assumptions was that
E(Xu) = 0, where X is a matrix containing all the variables on the RHS of
the equation.
• It is clear from (8) that P is related to the errors in (4) and (5) - i.e. it is
stochastic.
• What would be the consequences for the OLS estimator, , if we ignore
the simultaneity?
Simultaneous Equations Bias



• Recall that and
• So that
• Taking expectations,
• If the X’s are non-stochastic, E(Xu) = 0, which would be the case in a
single equation system, so that , which is the condition for
unbiasedness.
• But .... if the equation is part of a system, then E(Xu)  0, in general.
Simultaneous Equations Bias (cont’d)
 ( ' ) '
  
X X X y
1
y X u
 

E E E X X X u
X X E X u
( ) ( ) (( ' ) ' )
( ' ) ( ' )
 

 
 


1
1
E( )
 

u
X
X
X
u
X
X
X
X
X
X
X
u
X
X
X
X
'
)
'
(
'
)
'
(
'
)
'
(
)
(
'
)
'
(
ˆ
1
1
1
1















• Conclusion: Application of OLS to structural equations which are part
of a simultaneous system will lead to biased coefficient estimates.
• Is the OLS estimator still consistent, even though it is biased?
• No - In fact the estimator is inconsistent as well.
• Hence it would not be possible to estimate equations (4) and (5)
validly using OLS.

So What Can We Do?
• Taking equations (8) and (9), we can rewrite them as
(10)
(11)
• We CAN estimate equations (10) & (11) using OLS since all the RHS
variables are exogenous.
• But ... we probably don’t care what the values of the  coefficients are;
what we wanted were the original parameters in the structural
equations - , , , , , .
Avoiding Simultaneous Equations Bias
P T S
   
   
10 11 12 1
Q T S
   
   
20 21 22 2

Can We Retrieve the Original Coefficients from the ’s?
Short answer: sometimes.
• As well as simultaneity, we sometimes encounter another problem:
identification.
• Consider the following demand and supply equations
Supply equation (12)
Demand equation (13)
We cannot tell which is which!
• Both equations are UNIDENTIFIED or NOT IDENTIFIED, or
UNDERIDENTIFIED.
• The problem is that we do not have enough information from the equations
to estimate 4 parameters. Notice that we would not have had this problem
with equations (4) and (5) since they have different exogenous variables.
Identification of Simultaneous Equations
Q P
 
 
Q P
 
 

• We could have three possible situations:
1. An equation is unidentified
· like (12) or (13)
· we cannot get the structural coefficients from the reduced form estimates
2. An equation is exactly identified
· e.g. (4) or (5)
· can get unique structural form coefficient estimates
3. An equation is over-identified
· Example given later
· More than one set of structural coefficients could be obtained from the
reduced form.
What Determines whether an Equation is Identified
or not?

• How do we tell if an equation is identified or not?
• There are two conditions we could look at:
- The order condition - is a necessary but not sufficient condition for an
equation to be identified.
- The rank condition - is a necessary and sufficient condition for
identification. We specify the structural equations in a matrix form and
consider the rank of a coefficient matrix.
What Determines whether an Equation is Identified
or not? (cont’d)

Statement of the Order Condition (from Ramanathan 1995, pp.666)
• Let G denote the number of structural equations. An equation is just
identified if the number of variables excluded from an equation is G-1.
• If more than G-1 are absent, it is over-identified. If less than G-1 are
absent, it is not identified.
Example
• In the following system of equations, the Y’s are endogenous, while the X’s
are exogenous. Determine whether each equation is over-, under-, or just-
identified.
(14)-(16)
Y Y Y X X u
Y Y X u
Y Y u
1 0 1 2 3 3 4 1 5 2 1
2 0 1 3 2 1 2
3 0 1 2 3
     
   
  
    
  
 

Solution
G = 3;
If # excluded variables = 2, the eqn is just identified
If # excluded variables > 2, the eqn is over-identified
If # excluded variables < 2, the eqn is not identified
Equation 14: Not identified
Equation 15: Just identified
Equation 16: Over-identified

• How do we tell whether variables really need to be treated as endogenous
or not?
• Consider again equations (14)-(16). Equation (14) contains Y2 and Y3 - but
do we really need equations for them?
• We can formally test this using a Hausman test, which is calculated as
follows:
1. Obtain the reduced form equations corresponding to (14)-(16). The
reduced forms turn out to be:
(17)-(19)
Estimate the reduced form equations (17)-(19) using OLS, and obtain the
fitted values,
Tests for Exogeneity
Y X X v
Y X v
Y X v
1 10 11 1 12 2 1
2 20 21 1 2
3 30 31 1 3
   
  
  
  
 
 
 ,  , 
Y Y Y
1 2 3

2. Run the regression corresponding to equation (14).
3. Run the regression (14) again, but now also including the fitted values
as additional regressors:
(20)
4. Use an F-test to test the joint restriction that 2 = 0, and 3 = 0. If the
null hypothesis is rejected, Y2 and Y3 should be treated as endogenous.
Tests for Exogeneity (cont’d)
 , 
Y Y
2 3
1
1
3
3
1
2
2
2
5
1
4
3
3
2
1
0
1
ˆ
ˆ u
Y
Y
X
X
Y
Y
Y 






 







• Consider the following system of equations:
(21-23)
• Assume that the error terms are not correlated with each other. Can we estimate
the equations individually using OLS?
• Equation 21: Contains no endogenous variables, so X1 and X2 are not correlated
with u1. So we can use OLS on (21).
• Equation 22: Contains endogenous Y1 together with exogenous X1 and X2. We
can use OLS on (22) if all the RHS variables in (22) are uncorrelated with that
equation’s error term. In fact, Y1 is not correlated with u2 because there is no Y2
term in equation (21). So we can use OLS on (22).
Recursive Systems
Y X X u
Y Y X X u
Y Y Y X X u
1 10 11 1 12 2 1
2 20 21 1 21 1 22 2 2
3 30 31 1 32 2 31 1 32 2 3
   
    
     
  
   
    

• Equation 23: Contains both Y1 and Y2; we require these to be
uncorrelated with u3. By similar arguments to the above, equations
(21) and (22) do not contain Y3, so we can use OLS on (23).
• This is known as a RECURSIVE or TRIANGULAR system. We do
not have a simultaneity problem here.
• But in practice not many systems of equations will be recursive...
Recursive Systems (cont’d)

• Cannot use OLS on structural equations, but we can validly apply it to
the reduced form equations.
• If the system is just identified, ILS involves estimating the reduced
form equations using OLS, and then using them to substitute back to
obtain the structural parameters.
• However, ILS is not used much because
1. Solving back to get the structural parameters can be tedious.
2. Most simultaneous equations systems are over-identified.
Indirect Least Squares (ILS)

• In fact, we can use this technique for just-identified and over-identified
systems.
• Two stage least squares (2SLS or TSLS) is done in two stages:
Stage 1:
• Obtain and estimate the reduced form equations using OLS. Save the
fitted values for the dependent variables.
Stage 2:
• Estimate the structural equations, but replace any RHS endogenous
variables with their stage 1 fitted values.
Estimation of Systems
Using Two-Stage Least Squares

Example: Say equations (14)-(16) are required.
Stage 1:
• Estimate the reduced form equations (17)-(19) individually by OLS and
obtain the fitted values, .
Stage 2:
• Replace the RHS endogenous variables with their stage 1 estimated values:
(24)-(26)
• Now and will not be correlated with u1, will not be correlated with u2 ,
and will not be correlated with u3 .
Using Two-Stage Least Squares (cont’d)
 ,  , 
Y Y Y
1 2 3
Y Y Y X X u
Y Y X u
Y Y u
1 0 1 2 3 3 4 1 5 2 1
2 0 1 3 2 1 2
3 0 1 2 3
     
   
  
    
  
 
 



Y2

Y3

Y3

Y2

• It is still of concern in the context of simultaneous systems whether the
CLRM assumptions are supported by the data.
• If the disturbances in the structural equations are autocorrelated, the
2SLS estimator is not even consistent.
• The standard error estimates also need to be modified compared with
their OLS counterparts, but once this has been done, we can use the
usual t- and F-tests to test hypotheses about the structural form
coefficients.
Using Two-Stage Least Squares (cont’d)

• Recall that the reason we cannot use OLS directly on the structural
equations is that the endogenous variables are correlated with the errors.
• One solution to this would be not to use Y2 or Y3 , but rather to use some
other variables instead.
• We want these other variables to be (highly) correlated with Y2 and Y3, but
not correlated with the errors - they are called INSTRUMENTS.
• Say we found suitable instruments for Y2 and Y3, z2 and z3 respectively. We
do not use the instruments directly, but run regressions of the form
(27) & (28)
Instrumental Variables
Y z
Y z
2 1 2 2 1
3 3 4 3 2
  
  
  
  

• Obtain the fitted values from (27) & (28), and , and replace Y2 and
Y3 with these in the structural equation.
• We do not use the instruments directly in the structural equation.
• It is typical to use more than one instrument per endogenous variable.
• If the instruments are the variables in the reduced form equations, then
IV is equivalent to 2SLS.
Instrumental Variables (cont’d)

Y2

Y3

What Happens if We Use IV / 2SLS Unnecessarily?
• The coefficient estimates will still be consistent, but will be inefficient
compared to those that just used OLS directly.
The Problem With IV
• What are the instruments?
Solution: 2SLS is easier.
Other Estimation Techniques
1. 3SLS - allows for non-zero covariances between the error terms.
2. LIML - estimating reduced form equations by maximum likelihood
3. FIML - estimating all the equations simultaneously using maximum
likelihood
Instrumental Variables (cont’d)

• George and Longstaff (1993)
• Introduction
- Is trading activity related to the size of the bid / ask spread?
- How do spreads vary across options?
• How Might the Option Price / Trading Volume and the Bid / Ask Spread
be Related?
Consider 3 possibilities:
1. Market makers equalise spreads across options.
2. The spread might be a constant proportion of the option value.
3. Market makers might equalise marginal costs across options irrespective
of trading volume.
An Example of the Use of 2SLS: Modelling
the Bid-Ask Spread and Volume for Options

• The S&P 100 Index has been traded on the CBOE since 1983 on a
continuous open-outcry auction basis.
• Transactions take place at the highest bid or the lowest ask.
• Market making is highly competitive.
Market Making Costs

• For every contract (100 options) traded, a CBOE fee of 9c and an
Options Clearing Corporation (OCC) fee of 10c is levied on the firm
that clears the trade.
• Trading is not continuous.
• Average time between trades in 1989 was approximately 5 minutes.
What Are the Costs Associated with Market Making?

• The CBOE limits the tick size:
$1/8 for options worth $3 or more
$1/16 for options worth less than $3
• The spread is likely to depend on trading volume
... but also trading volume is likely to depend on the spread.
• So there will be a simultaneous relationship.
The Influence of Tick-Size Rules on Spreads

• All trading days during 1989 are used for observations.
• The average bid & ask prices are calculated for each option during the
time 2:00pm – 2:15pm Central Standard time.
• The following are then dropped from the sample for that day:
1. Any options that do not have bid / ask quotes reported during the ¼ hour.
2. Any options with fewer than 10 trades during the day.
• The option price is defined as the average of the bid & the ask.
• We get a total of 2456 observations. This is a pooled regression.
The Data

• For the calls:
(1)
(2)
• And symmetrically for the puts:
(3)
(4)
where PRi & CRi are the squared deltas of the options
The Models
CBA CDUM C CL T CR e
i i i i i i i
      
     
0 1 2 3 4 5
CL CBA T T M v
i i i i i i
     
    
0 1 2 3
2
4
2
PBA PDUM P PL T PR u
i i i i i i i
      
     
0 1 2 3 4 5
PL PBA T T M w
i i i i i i
     
    
0 1 2 3
2
4
2

• CDUMi and PDUMi are dummy variables
= 0 if Ci or Pi < $3
= 1 if Ci or Pi  $3
• T2 allows for a nonlinear relationship between time to maturity and the
spread.
• M2 is used since ATM options have a higher trading volume.
• Aside: are the equations identified?
• Equations (1) & (2) and then separately (3) & (4) are estimated using
2SLS.
The Models (cont’d)

Results 1
Call Bid-Ask Spread and Trading Volume Regression
i
i
i
i
i
i
i e
CR
T
CL
C
CDUM
CBA 





 5
4
3
2
1
0 




 (6.55)
i
i
i
i
i
i v
M
T
T
CBA
CL 




 2
4
2
3
2
1
0 



 (6.56)
0 1 2 3 4 5 Adj. R2
0.08362
(16.80)
0.06114
(8.63)
0.01679
(15.49)
0.00902
(14.01)
-0.00228
(-12.31)
-0.15378
(-12.52)
0.688
0 1 2 3 4 Adj. R2
-3.8542
(-10.50)
46.592
(30.49)
-0.12412
(-6.01)
0.00406
(14.43)
0.00866
(4.76)
0.618
Note: t-ratios in parentheses. Source: George and Longstaff (1993). Reprinted with the permission of
the School of Business Administration, University of Washington.

Results 2
Put Bid-Ask Spread and Trading Volume Regression
i
i
i
i
i
i
i u
PR
T
PL
P
PDUM
PBA 





 5
4
3
2
1
0 




 (6.57)
i
i
i
i
i
i w
M
T
T
PBA
PL 




 2
4
2
3
2
1
0 



 (6.58)
0 1 2 3 4 5 Adj. R2
0.05707
(15.19)
0.03258
(5.35)
0.01726
(15.90)
0.00839
(12.56)
-0.00120
(-7.13)
-0.08662
(-7.15)
0.675
0 1 2 3 4 Adj. R2
-2.8932
(-8.42)
46.460
(34.06)
-0.15151
(-7.74)
0.00339
(12.90)
0.01347
(10.86)
0.517
Note: t-ratios in parentheses. Source: George and Longstaff (1993). Reprinted with the permission of
the School of Business Administration, University of Washington.

Adjusted R2  60%
1 and 1 measure the tick size constraint on the spread
2 and 2 measure the effect of the option price on the spread
3 and 3 measure the effect of trading activity on the spread
4 and 4 measure the effect of time to maturity on the spread
5 and 5 measure the effect of risk on the spread
1 and 1 measure the effect of the spread size on trading activity etc.
Comments:

• The paper argues that calls and puts might be viewed as substitutes
since they are all written on the same underlying.
• So call trading activity might depend on the put spread and put trading
activity might depend on the call spread.
• The results for the other variables are little changed.
Calls and Puts as Substitutes

• Bid - Ask spread variations between options can be explained by reference
to the level of trading activity, deltas, time to maturity etc. There is a 2 way
relationship between volume and the spread.
• The authors argue that in the second part of the paper, they did indeed find
evidence of substitutability between calls & puts.
Comments
- No diagnostics.
- Why do the CL and PL equations not contain the CR and PR variables?
- The authors could have tested for endogeneity of CBA and CL.
- Why are the squared terms in maturity and moneyness only in the
liquidity regressions?
- Wrong sign on the squared deltas.
Conclusions

• A natural generalisation of autoregressive models popularised by Sims
• A VAR is in a sense a systems regression model i.e. there is more than one
dependent variable.
• Simplest case is a bivariate VAR
where uit is an iid disturbance term with E(uit)=0, i=1,2; E(u1t u2t)=0.
• The analysis could be extended to a VAR(g) model, or so that there are g
variables and g equations.
Vector Autoregressive Models
y y y y y u
y y y y y u
t t k t k t k t k t
t t k t k t k t k t
1 10 11 1 1 1 1 11 2 1 1 2 1
2 20 21 2 1 2 2 21 1 1 2 1 2
       
       
   
   
    
    
... ...
... ...

• One important feature of VARs is the compactness with which we can
write the notation. For example, consider the case from above where k=1.
• We can write this as
or
or even more compactly as
yt = 0 + 1 yt-1 + ut
g1 g1 gg g1 g1
Vector Autoregressive Models:
Notation and Concepts
y y y u
y y y u
t t t t
t t t t
1 10 11 1 1 11 2 1 1
2 20 21 2 1 21 1 1 2
   
   
 
 
  
  
y
y
y
y
u
u
t
t
t
t
t
t
1
2
10
20
11 11
21 21
1 1
2 1
1
2





 





 











 










 
 

• This model can be extended to the case where there are k lags of each
variable in each equation:
yt = 0 + 1 yt-1 + 2 yt-2 +...+ k yt-k + ut
g1 g1 gg g1 gg g1 gg g1 g1
• We can also extend this to the case where the model includes first
difference terms and cointegrating relationships (a VECM).
Vector Autoregressive Models:
Notation and Concepts (cont’d)

• Advantages of VAR Modelling
- Do not need to specify which variables are endogenous or exogenous - all are
endogenous
- Allows the value of a variable to depend on more than just its own lags or
combinations of white noise terms, so more general than ARMA modelling
- Provided that there are no contemporaneous terms on the right hand side of the
equations, can simply use OLS separately on each equation
- Forecasts are often better than “traditional structural” models.
• Problems with VAR’s
- VAR’s are a-theoretical (as are ARMA models)
- How do you decide the appropriate lag length?
- So many parameters! If we have g equations for g variables and we have k lags of each
of the variables in each equation, we have to estimate (g+kg2) parameters. e.g. g=3, k=3,
parameters = 30
- Do we need to ensure all components of the VAR are stationary?
- How do we interpret the coefficients?
Vector Autoregressive Models Compared with
Structural Equations Models

Choosing the Optimal Lag Length for a VAR
 2 possible approaches: cross-equation restrictions and information criteria
Cross-Equation Restrictions
 In the spirit of (unrestricted) VAR modelling, each equation should have
the same lag length
 Suppose that a bivariate VAR(8) estimated using quarterly data has 8 lags
of the two variables in each equation, and we want to examine a restriction
that the coefficients on lags 5 through 8 are jointly zero. This can be done
using a likelihood ratio test
 Denote the variance-covariance matrix of residuals (given by /T), as .
The likelihood ratio test for this joint hypothesis is given by
̂
u
u 
ˆ
ˆ
 
u
r
T
LR 


 ˆ
log
ˆ
log

Choosing the Optimal Lag Length for a VAR
(cont’d)
where is the variance-covariance matrix of the residuals for the restricted
model (with 4 lags), is the variance-covariance matrix of residuals for the
unrestricted VAR (with 8 lags), and T is the sample size.
• The test statistic is asymptotically distributed as a 2 with degrees of freedom
equal to the total number of restrictions. In the VAR case above, we are
restricting 4 lags of two variables in each of the two equations = a total of 4 *
2 * 2 = 16 restrictions.
• In the general case where we have a VAR with p equations, and we want to
impose the restriction that the last q lags have zero coefficients, there would
be p2q restrictions altogether
• Disadvantages: Conducting the LR test is cumbersome and requires a
normality assumption for the disturbances.
r
̂
u
̂

Information Criteria for VAR Lag Length Selection
• Multivariate versions of the information criteria are required. These can
be defined as:
where all notation is as above and k is the total number of regressors in all
equations, which will be equal to g2k + g for g equations, each with k lags
of the g variables, plus a constant term in each equation. The values of the
information criteria are constructed for 0, 1, … lags (up to some pre-
specified maximum ).
k
ln(ln(T))
2
ˆ
ln
ln(T)
ˆ
ln
/
2
ˆ
ln
T
k
MHQIC
T
k
MSBIC
T
k
MAIC














Does the VAR Include Contemporaneous Terms?
• So far, we have assumed the VAR is of the form
• But what if the equations had a contemporaneous feedback term?
• We can write this as
• This VAR is in primitive form.
y y y u
y y y u
t t t t
t t t t
1 10 11 1 1 11 2 1 1
2 20 21 2 1 21 1 1 2
   
   
 
 
  
  
y y y y u
y y y y u
t t t t t
t t t t t
1 10 11 1 1 11 2 1 12 2 1
2 20 21 2 1 21 1 1 22 1 2
    
    
 
 
   
   
y
y
y
y
y
y
u
u
t
t
t
t
t
t
t
t
1
2
10
20
11 11
21 21
1 1
2 1
12
22
2
1
1
2
0
0





 





 











 











 










 
 



Primitive versus Standard Form VARs
• We can take the contemporaneous terms over to the LHS and write
or
A yt = 0 + 1 yt-1 + ut
• We can then pre-multiply both sides by A-1 to give
yt = A-10 + A-11 yt-1 + A-1ut
or
yt = A0 + A1 yt-1 + et
• This is known as a standard form VAR, which we can estimate using OLS.
1
1
22
12 1
2
10
20
11 11
21 21
1 1
2 1
1
2













 





 











 









 

 
 
y
y
y
y
u
u
t
t
t
t
t
t

Block Significance and Causality Tests
• It is likely that, when a VAR includes many lags of variables, it will be
difficult to see which sets of variables have significant effects on each
dependent variable and which do not. For illustration, consider the following
bivariate VAR(3):
• This VAR could be written out to express the individual equations as
• We might be interested in testing the following hypotheses, and their
implied restrictions on the parameter matrices:



















































































t
t
t
t
t
t
t
t
t
t
u
u
y
y
y
y
y
y
y
y
2
1
3
2
3
1
22
21
12
11
2
2
2
1
22
21
12
11
1
2
1
1
22
21
12
11
20
10
2
1














t
t
t
t
t
t
t
t
t
t
t
t
t
t
t
t
u
y
y
y
y
y
y
y
u
y
y
y
y
y
y
y
2
3
2
22
3
1
21
2
2
22
2
1
21
1
2
22
1
1
21
20
2
1
3
2
12
3
1
11
2
2
12
2
1
11
1
2
12
1
1
11
10
1











































Block Significance and Causality Tests (cont’d)
• Each of these four joint hypotheses can be tested within the F-test
framework, since each set of restrictions contains only parameters drawn
from one equation.
• These tests could also be referred to as Granger causality tests.
• Granger causality tests seek to answer questions such as “Do changes in y1
cause changes in y2?” If y1 causes y2, lags of y1 should be significant in the
equation for y2. If this is the case, we say that y1 “Granger-causes” y2.
• If y2 causes y1, lags of y2 should be significant in the equation for y1.
• If both sets of lags are significant, there is “bi-directional causality”
Hypothesis Implied Restriction
1. Lags of y1t do not explain current y2t 21 = 0 and 21 = 0 and 21 = 0

Impulse Responses
• VAR models are often difficult to interpret: one solution is to construct
the impulse responses and variance decompositions.
• Impulse responses trace out the responsiveness of the dependent variables
in the VAR to shocks to the error term. A unit shock is applied to each
variable and its effects are noted.
• Consider for example a simple bivariate VAR(1):
• A change in u1t will immediately change y1. It will change change y2 and
also y1 during the next period.
• We can examine how long and to what degree a shock to a given equation
has on all of the variables in the system.
y y y u
y y y u
t t t t
t t t t
1 10 11 1 1 11 2 1 1
2 20 21 2 1 21 1 1 2
   
   
 
 
  
  

Variance Decompositions
• Variance decompositions offer a slightly different method of examining
VAR dynamics. They give the proportion of the movements in the
dependent variables that are due to their “own” shocks, versus shocks to the
other variables.
• This is done by determining how much of the s-step ahead forecast error
variance for each variable is explained innovations to each explanatory
variable (s = 1,2,…).
• The variance decomposition gives information about the relative importance
of each shock to the variables in the VAR.

Impulse Responses and Variance Decompositions:
The Ordering of the Variables
• But for calculating impulse responses and variance decompositions, the
ordering of the variables is important.
• The main reason for this is that above, we assumed that the VAR error terms
were statistically independent of one another.
• This is generally not true, however. The error terms will typically be correlated
to some degree.
• Therefore, the notion of examining the effect of the innovations separately has
little meaning, since they have a common component.
• What is done is to “orthogonalise” the innovations.
• In the bivariate VAR, this problem would be approached by attributing all of
the effect of the common component to the first of the two variables in the
VAR.
• In the general case where there are more variables, the situation is more
complex but the interpretation is the same.

An Example of the use of VAR Models:
The Interaction between Property Returns and the
Macroeconomy.
• Brooks and Tsolacos (1999) employ a VAR methodology for investigating the
interaction between the UK property market and various macroeconomic variables.
• Monthly data are used for the period December 1985 to January 1998.
• It is assumed that stock returns are related to macroeconomic and business
conditions.
• The variables included in the VAR are
– FTSE Property Total Return Index (with general stock market effects removed)
– The rate of unemployment
– Nominal interest rates
– The spread between long and short term interest rates
– Unanticipated inflation
– The dividend yield.
The property index and unemployment are I(1) and hence are differenced.

Marginal Significance Levels associated with Joint
F-tests that all 14 Lags have not Explanatory Power
for that particular Equation in the VAR
• Multivariate AIC selected 14 lags of each variable in the VAR
Lags of Variable
Dependent variable SIR DIVY SPREAD UNEM UNINFL PROPRES
SIR 0.0000 0.0091 0.0242 0.0327 0.2126 0.0000
DIVY 0.5025 0.0000 0.6212 0.4217 0.5654 0.4033
SPREAD 0.2779 0.1328 0.0000 0.4372 0.6563 0.0007
UNEM 0.3410 0.3026 0.1151 0.0000 0.0758 0.2765
UNINFL 0.3057 0.5146 0.3420 0.4793 0.0004 0.3885
PROPRES 0.5537 0.1614 0.5537 0.8922 0.7222 0.0000

Variance Decompositions for the
Property Sector Index Residuals
• Ordering for Variance Decompositions and Impulse Responses:
– Order I: PROPRES, DIVY, UNINFL, UNEM, SPREAD, SIR
– Order II: SIR, SPREAD, UNEM, UNINFL, DIVY, PROPRES.
Explained by innovations in
SIR DIVY SPREAD UNEM UNINFL PROPRES
Months ahead I II I II I II I II I II I II
1 0.0 0.8 0.0 38.2 0.0 9.1 0.0 0.7 0.0 0.2 100.0 51.0
2 0.2 0.8 0.2 35.1 0.2 12.3 0.4 1.4 1.6 2.9 97.5 47.5
3 3.8 2.5 0.4 29.4 0.2 17.8 1.0 1.5 2.3 3.0 92.3 45.8
4 3.7 2.1 5.3 22.3 1.4 18.5 1.6 1.1 4.8 4.4 83.3 51.5
12 2.8 3.1 15.5 8.7 15.3 19.5 3.3 5.1 17.0 13.5 46.1 50.0
24 8.2 6.3 6.8 3.9 38.0 36.2 5.5 14.7 18.1 16.9 23.4 22.0

Impulse Responses and Standard Error Bands for
Innovations in Dividend Yield and
the Treasury Bill Yield
Innovations in Dividend Yields
-0.06
-0.04
-0.02
0
0.02
0.04
0.06
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Steps Ahead
Innovations in the T-Bill Yield
-0.08
-0.06
-0.04
-0.02
0
0.02
0.04
0.06
0.08
0.1
0.12
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Steps Ahead

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