It is argued that whether or not there is a need for unit roots and cointegration based econometric methods is a methodological issue. An alternative is the econometrics of the London School of Economics (LSE) and Hendry approach based on the simpler classical methods of estimation. This is known as the general to specific method (GETS). Like all other methodological issues this is also difficult to resolve but we think that GETS is very useful.GETS, Cointegration, Box-Jenkin’s Equations, Hendry, Granger

Kumar, Saten

Rao, B. Bhaskara

Singh, Rup

English

Research Papers in Economics

Do we need time series econometrics? 
 
 
B. Bhaskara Rao 
 
raob@optusnet.com.au 
 
School of Economics 
University of the South Pacific, Suva, Fiji 
 
Rup Singh 
 
School of Economics 
University of the South Pacific, Suva, Fiji 
 
Saten Kumar
1 
 
School of Economics 
University of the South Pacific, Suva, Fiji 
Abstract 
 
It is argued that whether or not there is a need for unit roots and cointegration based 
econometric methods is a methodological issue. An alternative is the econometrics of the 
London School of Economics (LSE) and Hendry approach based on the simpler classical 
methods of estimation. This is known as the general to specific method (GETS). Like all 
other methodological issues this is also difficult to resolve but we think that GETS is very 
useful. 
 
Keywords: GETS, Cointegration, Box-Jenkin’s Equations, Hendry, Granger. 
 
JEL: B40, B41 and B49 
 
 
                                                 
1 Kumar is a graduate student. Rao is Professor of Economics and Singh is Lecturer in Economics. 1.  Introduction 
 
It is unusual to start a paper, with a 2,000 word limit, with quotations. However, 
to explain the essence of this paper it is necessary to note the views of two leading 
econometricians viz., Professor David Hendry and Nobel Laureate Clive Granger. 
Hendry is a well-known proponent of the econometric methodology of the 
London School of Economics (LSE) known as the general to specific approach 
(GETS). He said that “ I actually thought cointegration was so blindingly obvious 
that it was not even worth formalizing it.  ….[However,] I still think…. it 
[cointegration] is completely trivial but it is very, very interesting 
because….[when] the things that …. are [in] equilibria [imply]….. (a) they are the 
targets agents are trying to achieve and (b) when they get there they will stay 
there and when they are not there they will try to move there”; Hendry (2000, 
p.241). Granger, commenting on GETS,  said that “The LSE methodology is a 
mid-point between the classical econometrics strategy, with a heavy dependence 
on economic theory, and the theoretical pure time series techniques …. extend the 
Box-Jenkins approach, such as VAR. Economic theory is used to suggest an 
initial specification, but then the data are allowed to speak in the process of 
considering alternative specifications and in the eventual evaluation.”; Granger 
(1990, p.279), my italics.
2 
 
Given these observations why do economists heavily tilt towards the time series 
econometric methods and spurn established and simpler classical methods of 
estimation? Disregarding trivial merits like an opportunity to decorate papers with 
impressive mathematical symbols, applied economists do not seem to remember 
Smith’s (2000) three important stages in research, of which the very first one is 
purpose. For our paper a distinction between the purpose of testing economic 
theories and developing models for forecasting is necessary although in practice 
there is a mixture of both purposes.  
                                                 
2 The originals are a bit longer and the reader is requested to refer to the reference to get the full 
flavour.  
2. Testing Theories and Generating Forecasts 
 
The atheoretical Box-Jenkins equations are good examples of research where the 
main purpose or objective is to make forecasts with improved accuracy. On the 
other hand testing the quantity theory and purchasing power parity (PPP) etc., are 
examples of research with the main objective of testing the validity of theories. 
The Engle-Granger time series methods can be seen as an intermediate method 
extending the Box-Jenkin’s atheoretical pure time series methods by adding 
theoretical information to further improve the accuracy of forecasts. It is hard to 
admit that the cointegration methods are superior for testing economic theories 
than GETS. This is so because in spite of their different starting points, 
cointegration and GETS are indistinguishable from each other and seem to be 
observationally equivalent. Therefore, Hendry is justified in saying that 
“cointegration was so blindingly obvious that it was not even worth formalizing 
it.” Yet, in much of the applied work, the bulk of which is actually on testing 
theories and  not necessarily to generate more accurate forecasts, cointegration 
methods are widely used instead of GETS.  
 
As an example consider this. It can be said that from medium to long run 
perspectives, central banks are more interested in understanding by how much 
nominal money supply should beincreased to maintain stability of prices and 
economic activity. They are seldom interested in knowing what should be the 
increase in money supply every month. If  they need accuracy in inflation 
forecasts, they may perhaps use the Box-Jenkins equations and these may do an 
equally good job as those based on cointegration methodology. Therefore, for 
policy formulation valid theories are more important than accurate forecasts.  
3. GETS and Cointegration 
 
A drawback of economic theories is that they are essentially equilibrium 
relationships between variables in their levels of and/or growth rates. Theory 
seldom gives much information about dynamic adjustments and how long is the 
transition process in the real calendar time. However, the data from the real world 
which is used to test theories are hardly generated by an equilibrium world. 
Therefore, there is a methodological problem with using data generated from a 
disequilibrium world to test equilibrium theories. And this is the starting point for 
the development of GETS.  
 
Economists and econometricians at the LSE took a pragmatic view, mostly under 
the influence of  Popper’s methodology, that dynamics is an empirical issue to be 
determined by data and theoretical insights. They discarded the then popular 
partial adjustment based dynamics as inadequate and extended  the Phillips 
(Phillips curve fame) error correction model, with its positive feedbacks, to 
augment with additional adjustments due to the current and past changes in the 
variables of the model. This basic equation is known as the general dynamic 
equation (GDE). A parsimonious version of this GDE is derived by deleting 
insignificant variables with the variable deletion tests. PcGets of Hendry and 
Krolzig (2001) does an excellent job of searching for parsimonious dynamic 
equations.  
 
As an example, this approach can be explained as follows with the money demand 
equation. Theory implies that demand for real money (m) depends on real income 
(y) and the nominal rate of interest (R). A semi-log form for narrow money is as 
follows. 
 
lnln                                             (1) myR abg =+-  Changes in the current period demand for money are due to 2 reasons. First, the 
money market may have been in disequilibrium in the past period and therefore, 
there will be a change in the demand for money in the current period. Second 
money demand may also change because the explanatory variables may change in 
the current period and/or their past changes may have some delayed effects. 
Therefore, the following general dynamic specification is reasonable to explain 
current period changes in the demand for money. 
 
 
111
123
001
ln[ln(ln)]
                +lnln                   (2)
tttt
nnn
tjtktl
jkl
mmyR
yRm
labg ---
---
===
D=--+-
D+D+D ￿￿￿
 
 
The expression in the square bracket is the measure of past period departure from 
the equilibrium and it is known as he lagged error correction (ECM) term. ECM  
has been borrowed from GETS by the cointegration method. l  in (1) is the speed 
of adjustment and it should be negative for negative feedback adjustment to take 
place. It will be positive and may be insignificant if the underlying economic 
theory is inadequate. Theories can be tested with GETS by estimating the   
coefficients and testing for the significance of the lagged explanatory variables in 
the ECM. A parsimonious version of (2) with a fewer lagged changes of the 
variables will improve the standard errors of the estimated coefficients and 
forecasting accuracy by increasing the degrees of freedom. Equation (2) can be 
estimated with the classical methods and also the instrumental variables method 
can be used to minimize any endogenous variable bias. 
 
So, what is wrong with this approach to test theories and probably also use it for 
forecasting? Although the time series based cointegration approach is very 
similar, it has shown that if the variables are non-stationary in their levels their 
means and variances violate the classical assumptions that they are constant. 
Therefore, the estimated standard errors with the classical methods are spurious and unreliable. It is necessary to transform such non-stationary variables into 
stationary variables by differencing and at the same time estimate the models 
without ignoring the theoretical information on their levels. Time series methods 
have been developed for this purpose. In the first stage these methods estimate 
efficiently the coefficients of the variables in the ECM term in (2) and this is the 
cointegrating equation. The short run dynamic adjustment equation is estimated in 
the second stage, in the same way as in GETS. In contrast, coefficients of the 
cointegrating equation and the dynamic adjustment are estimated in GETS in one 
step. 
 
The above similarities between these two methods have been ignored by many 
critiques of GETS who in spite of repeated statements by Hendry argue that the 
level variables in the ECM are non-stationary and therefore classical methods are 
inappropriate. For a long time the fact that GETS can  also be made consistent 
with cointegration has been ignored. Hendry repeatedly stated that if economic 
theory is correct, the combination of variables in the ECM should be stationary. 
Conceptually this is similar to the drunken farmer and his dog  example to explain 
the concept of cointegration. In our example of the money demand equation, 
output and the rate of interest are the drunken farmer and demand for money is 
the dog. If theory is correct they should move closely. Therefore, the order of the 
variables on  both sides of (2) is balanced and consistent with cointegration and 
the time series methods. Ericsson and McKinnon (2002) have developed a test, 
perhaps belatedly, to test for cointegration in GETS. However, by then 
cointegration and time series econometrics have become enormously popular and 
GETS did not receive its due recognition. 
 
In much of our applied work at the University of the South Pacific we have 
extensively used GETS as well as the standard time series and cointegration 
techniques. Nowhere had we found that GETS performed worse than the time 
series methods.  
 4. Conclusions 
 
This paper briefly stated the story and methodology behind GETS and the 
cointegration techniques. We took a methodological view that both these 
techniques are observationally equivalent but GETS based on the classical 
methods is simpler to use and well suited for the purpose of testing theories. 
However, like in all such methodological controversies, it is difficult to assert 
without any reservations that only one particular methodology is the best. 
Whether the entirely atheoretical Box-Jenkins equations, or GETS or the 
cointegration based time series techniques  or their variants give the best forecasts 
is something not yet well explored. This is an area worth examining further with 
real world data. But we can be fairly confident and claim that if the main purpose 
of a researcher is testing theories for policy formulation, the simpler GETS seem 
to be second to none. References 
 
 
Ericsson, N. R. and MacKinnon, J. G., (2002) “Distributions of error 
correction tests for cointegration”, Econometrics Journal, 5: pp. 285-318.  
Granger, C.W. J., (1990) “Introduction”, in Granger, C. J. (ed.) Modeling 
Economic Series: Readings in Econometric Methodology, Oxford: Clarendon 
Press, pp.277-278. 
 
Hendry, D. F., (2000) “Econometrics techniques: General discussion” in 
Backhouse, R. E. and Salanti, A. (eds.)  Macroeconomics and the Real World, 
Oxford: Oxford University Press, pp.239-242. 
 
Hendry, D. F. and Krolzig, H. M.,  (2001)  Automatic Econometric Model 
Selection, Oxford: Timberlake Consultants Press.  
 
-------and ------- (2005) ``The properties of automatic GETS modeling", the 
Economic Journal, 115: pp.C32-C61. 
 
Smith, R. P., (2000) ``Unit Roots and all that: the impact of time-series methods 
on macroeconomics", in Backhouse, R. E. and Salanti, A. (eds.) Macroeconomics 
and the Real World, Oxford: Oxford University Press, pp. 199-218. 
 
 
 

Automatic Econometric Model Selection,

Distributions of error correction tests for cointegration”,

Econometrics techniques: General discussion”

Introduction”,

The properties of automatic GETS modeling&quot;,

Unit Roots and all that: the impact of time-series methods on macroeconomics&quot;,

Do we need time series econometrics?

Do we need time series econometrics?

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