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15.10.2003, 16:32

What Granger Overlooked, and Mises Did Not / Artikel mises.org

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What Granger Overlooked, and Mises Did Not
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By
Frank Shostak

[Posted
October 15, 2003]

<img alt src="http://www.mises.org/images3/dogticker.gif" align="right" border="0" width="295" height="167">Two
economists who have developed statistical techniques to track economic trends
and to measure investment risk—Clive Granger and Robert Engle—were this
year awarded the Nobel Prize in economics.

In
this article I will be focusing on the contribution of Clive Granger to
economics. According to the Nobel committee Granger's important contribution
to economic science is his discovery of a phenomenon called cointegration.
This discovery, so it is held, enables economists to accurately validate
relationships among various economic data.

So
what is it all about?

Making
sense of economic data

Often
we observe that two pieces of data, which are not supposed to have any
relationship, appear to have very high visual correlation. For instance, we
may discover a strong correlation between the intensity of dog barking and
movements in stock prices. One is then tempted to take advantage of this
discovery in order to make money in the stock market.

In
reality, however, both the barking of the dog and movements of stock indexes
have nothing to do with each other. What makes the apparent good correlation
is that they are both influenced by an upward long-term trend. Also,
fluctuations of these data don't seem to converge around the trend but just
seem to move in an upward direction. These types of data statisticians label
nonstationary.<a id="_ftnref1" title href="http://www.mises.org/fullstory.asp?control=1352#_ftn1" name="_ftnref1">[1]</a>

In
contrast, data that converges around a fixed value is labelled stationary.
Data that is stationary implies an unchanged structure, something that is
stable and hence one can make sense of it, whereas nonstationary data is
associated with irregular fluctuations, which of course makes it very
difficult to make any sense of. Thus, if something drifts aimlessly it is not
possible to say much about its future course.

Now,
if one tries to make sense from data that is irregular obviously one will not
get very far. This, however, creates a major problem for economists since it
is held that most of the data that economists and financial analysts are
employing are not stationary. Consequently, incorporating these types of data
into economic analyses leads to misleading results.

For
instance, an economist wants to establish the importance of changes in
production on people's consumption. The common procedure for this is to apply
statistical methods on consumption and production data in order to establish
their interrelationship. In short, by means of a statistical technique, also
known as regression analysis, one establishes how consumption and production
are quantitatively connected to each other. Let us assume that an economist
has found that the relationship between consumption and production is
summarised by the following mathematical expression:

Consumption
= 10 +0.5*Production

Armed
with this finding the economist can now tell us the direction of consumption
if there is a change in production. Thus, if production is 100 then
consumption will be 60 (because 10+0.5*100=60). Economists label the numbers
10 and 0.5 parameters. Observe that the knowledge regarding the size of these
parameters, i.e., whether they are 10 and 0.5 or something else is obtained by
means of the regression technique.

Note
that 10 and 0.5, which have been generated by regression method, are estimates
of true parameters in the real world, or so it is held. It is also held that
on average these estimates are very close to the true parameters. In short, it
is held that any conclusions derived from the equation regarding the
relationship between consumption and production are a reflection of reality.

Granger,
however, contests this. He argues that no meaningful conclusions can be drawn
from the above equation if the data employed in establishing this equation are
nonstationary. In plain English, it is counter-productive to establish
meaningful conclusions from data that drifts aimlessly. The parameters that
one will get from such data will be erroneous and hence the outcome of the
analysis will be meaningless. So how does one overcome the problem?

Now,
if one could establish a common factor that influences both consumption and
production then these two time-series are said to be connected, or
cointegrated. Granger and others have shown by means of mathematical and
statistical methods that the existence of the common factor makes the
interrelationship between non-stationary time series stationary. Thus
consumption and production can be observed separately as a non-stationary time
series.

Therefore
if one tries to establish economic relationships between them one will get
misleading answers. However, if one were to suggest that both consumption and
production have a common factor then one could infer that over time both
consumption and production must move together. This common or cointegrating
factor could be that people's well-being requires consumption and production.
Moreover, without production there cannot be consumption and without
consumption no production is possible.

Another
example is an identical good which is trading in different locations. The
day-to-day fluctuations in prices may appear to be random in various locations
and therefore most likely will not correspond to each other.

However,
the existence of arbitrage and the law of supply and demand will make sure
that over time prices in various locations will move close to each other. Instead
of trying to find out what the cointegrating factor is, Granger and others
have produced a mechanized framework, which enables economists to establish
whether data complies with cointegration, i.e., whether the relationship
between the data makes sense so to speak. Once it is established that the data
is cointegrated it can then be incorporated by means of a certain mathematical
procedure to establish the correct parameters. <a id="_ftnref2" title href="http://www.mises.org/fullstory.asp?control=1352#_ftn2" name="_ftnref2">[2]</a>

Various
statistical results that are produced by means of Granger's framework
therefore are regarded as valid since they have been applied on cointegrated
data. Granger's discovery raises serious doubts about conclusions regarding
economic interrelationships which are reached by means of the old techniques.
It also provides a criticism of the popular usage of correlations without
attempting to make sense of the relationships.

Granger's
framework can be seen as a preventative in minimizing the use of meaningless
correlations. For instance, the Granger framework will indicate that movements
in the stock market and the intensity of the dog barking cannot be
cointegrated and therefore the use of these relationships to make money in the
stock market could prove to be a very expensive exercise.

In
this respect it could be regarded as bringing back the validity of fundamental
analysis. This must be contrasted with the popular way of thinking that
fundamental analysis is of little help since the data is of a random, i.e.,
nonstationary, nature. So it seems that the Granger's framework is a great
tool in furthering our understanding of the economic universe.

But
is it?

Are
there constants in economics?

The
major issue that Granger hasn't addressed is not whether the old techniques
have been generating valid parameter estimates, but whether such parameters
exist at all. In the natural sciences, the employment of mathematics enables
scientists to formulate the essential nature of objects. Consequently, within
given conditions, the same response will be obtained time and again. The same
approach, however, is not valid in economics. For economics is supposed to
deal with human beings and not objects. According to Mises,

<blockquote dir="ltr" style="MARGIN-RIGHT: 0px">
The
experience with which the sciences of human action have to deal is always an
experience of complex phenomena. No laboratory experiments can be performed
with regard to human action.<a id="_ftnref3" title href="http://www.mises.org/fullstory.asp?control=1352#_ftn3" name="_ftnref3">[3]</a>

[/i]
In
short, people have the freedom of choice to change their minds and pursue
actions that are contrary to what was observed in the past. As a result of the
unique nature of human beings, analyses in economics can only be qualitative.
In other words, there are no parameters in the human universe. Thus Mises
wrote,"There are, in the field of economics, no constant relations, and
consequently no measurement is possible."<a id="_ftnref4" title href="http://www.mises.org/fullstory.asp?control=1352#_ftn4" name="_ftnref4">[4]</a>

The
popular view that human economic activity can be captured by mathematical
formulae expressed through fixed parameters implies that human beings are
operating like machines. For instance, contrary to the mathematical way of
thinking, individual outlays on goods are not"caused" by income as
such. In his own context, every individual decides how much of a given income
will be used for consumption and how much for savings.

While
it is true that people respond to changes in their incomes, the response is
not automatic, and it cannot be captured by a mathematical formula. For
instance, an increase in an individual's income doesn't automatically imply
that his consumption expenditure will follow suit. In short, every individual
assesses the increase in income against the goals he wants to achieve. Thus,
he might decide that it is more beneficial for him to raise his savings rather
than raise his consumption.

At
best, mathematical formulations can be seen as a technique to provide a
snapshot at a given point in time of various economic data. In this sense it
can be seen as a particular way to present historical data. These
types of presentations, however, can tell us nothing about the driving causes
of human economic activity. What's more, the employment of established
historical relations to assess the impact of changes in government policies
will produce misleading results notwithstanding Granger's framework.

After
all, to assume that a change in government policy will leave the structure of
the equations intact would mean that individuals in the economy ceased to be
alive and were, in fact, frozen.

In
this regard Mises wrote,"As a method of economic analysis econometrics
is a childish play with figures that does not contribute anything to the
elucidation of the problems of economic reality."<a id="_ftnref5" title href="http://www.mises.org/fullstory.asp?control=1352#_ftn5" name="_ftnref5">[5]</a> 

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Frank Shostak
is an adjunct scholar of the Mises Institute and a frequent contributor to
Mises.org. Send him  MAIL and
see his outstanding Mises.org Daily
Articles Archive. He
would like to thank Andrew Pease and Dean Dusanic for comments.

<a id="_ftn1" title href="http://www.mises.org/fullstory.asp?control=1352#_ftnref1" name="_ftn1">[1]</a>Granger,
C.W.J. and Newbold, P. 1974."Spurious Regressions in Econometrics",
Journal of Econometrics, Vol. 2, pp 111-20.

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<div id="ftn2">
<a id="_ftn2" title href="http://www.mises.org/fullstory.asp?control=1352#_ftnref2" name="_ftn2">[2]</a>Granger,
C.W.J. and Weiss, A.A. 1983. Time series analysis of error-correction models,
in S.Karlin, T. Amemiya and L.A. Goodman, Studies in Econometrics, Time
series and Multivariate Statistics, in Honor of T.W. Anderson, Academic
Press, San Diego, pp 255-78.

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<div id="ftn3">
<a id="_ftn3" title href="http://www.mises.org/fullstory.asp?control=1352#_ftnref3" name="_ftn3">[3]</a>Ludwig
von Mises. 1963. Human Action. P. 31.

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<a id="_ftn4" title href="http://www.mises.org/fullstory.asp?control=1352#_ftnref4" name="_ftn4">[4]</a>Ibid. P.
55.

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<div id="ftn5">
<a id="_ftn5" title href="http://www.mises.org/fullstory.asp?control=1352#_ftnref5" name="_ftn5">[5]</a>Ludwig
von Mises. 1962. The Ultimate Foundation of Economic Science. P.
63.


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