Whether or not there is a need for the unit roots and cointegration based time series 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 it is difficult to resolve which approach is better. However, we think that GETS is conceptually simpler and very useful in applied work.GETS, Cointegration, Box-Jenkins’s Equations, Hendry, Granger

Kumar, Saten

Rao, B. Bhaskara

Singh, Rup

English

Research Papers in Economics

Do we need time series econometrics? 
(A shorter version is forthcoming in the Applied Economics Letters) 
 
B. Bhaskara Rao 
raob123@bigpond.com 
School of Economics and Finance 
University of Western Sydney 
Sydney, Australia 
 
Rup Singh 
singh_r@uspac.fj 
School of Economics 
University of the South Pacific 
 Suva, Fiji 
 
Saten Kumar 
SKumar@aic.ac.nz 
Auckland International College 
Auckland, New Zealand 
 
Abstract 
 
Whether or not there is a need for the unit roots and cointegration based time series 
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 it is difficult to resolve which approach is better. However, 
we think that GETS is conceptually simpler and very useful in applied work. 
 
Keywords: GETS, Cointegration, Box-Jenkins’s Equations, Hendry, Granger. 
 
JEL: B40, B41 and B49 
 
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1.  Introduction 
 
It is unusual to start a short paper 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  Professor 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 ….. 
[which] 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.
1 
 
Given these observations why do economists heavily tilt towards the time series 
econometric methods and spurn the 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.
2 For our arguments in this paper a distinction between the purpose of testing 
economic theories and developing models for forecasting is necessary although in 
practice a mixture of both purposes prevail.  
                                                    
1 The originals are a longer and the reader is requested to refer to the reference to get the full flavour. 
2 The other two are: summarising observed facts (data analysis) and interpretation. 
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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. Box-Jenkins 
equations are attractive because forecasting accuracy can be achieved by regressing a 
variable on its own lagged values. On the other hand testing the quantity theory or the 
purchasing power parity (PPP) theory or the convergence principle of the Solow 
model of growth 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 of extending the Box-Jenkins’s atheoretical pure time series 
methods by adding the theoretical information to further improve the accuracy of 
forecasts.
3 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 accurate forecasts, cointegration methods are widely used.   
 
As an example consider this. It can be said that from the medium to long run 
perspectives, central banks are more interested in understanding by how much the 
nominal money supply should be increased to maintain stability of economic activity 
and in particular stability of inflation. They are seldom interested in knowing what 
should be the increase in money supply every week or month. If  they need accuracy 
in inflation forecasts, they may perhaps use the Box-Jenkins equations and these may 
do a good job as those based on the cointegration methodology. Therefore, for policy 
                                                    
3 Although this is the purpose we are not aware of any evidence that equations based on the cointegration 
methodology, known as VECM (vector error correction models), can forecast better than the simple Box-
Jenkins equations. 
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are equally important. 
 
3. GETS and Cointegration 
 
A drawback of economic theories is that they are essentially equilibrium relationships 
between variables often in their levels. Theory seldom gives much information about 
the dynamic adjustments of the variables during the transition period between two 
equilibrium states and how long is the transition process in the real calendar time. 
However, the data  used to test the 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 some 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 negative feedback effects. A variables 
changes in the current period due to two main reasons. Firstly, the variable may not 
have fully adjusted in the previous period to its equilibrium value, which in turn is 
determined by the theory underling the variable of interest. Therefore, the variables 
changes in the current period at least partly to close the gap between its actual and 
equilibrium values. As an example consider the simple theory of the demand for real 
narrow money (m ). Theory says that it depends positively on real income and 
negatively on the nominal rate of interest. Although theory does not say what 
equation form should be used to specify this relationship, it is a common practice to 
use the following: 
 
 
* ln( ) ln( )                                            (1) m y i a b g = + -  
 
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the rate of interest paid on time deposits. The superscript asterisk denotes that the 
variable is the equilibrium value. Therefore, the aforesaid first adjustment can be 
formulated as: 
 
[ ] 1 1 1 ln ln ( ln )                 (2) t t t t m m y i l a b g - - - D = - - + -  
 
Note that if the actual value of money in the previous period exceeds its equilibrium 
value in the previous period, then it should decrease (in the current period) to move 
closer to its equilibrium value. That is if the term in the square brackets is positive, 
the change on the left hand side should be negative. That is why the sign of 
l (adjustment coefficient) is negative and this is known as the negative feedback 
adjustment or simply error correction mechanism (ECM). 
 
Secondly, real demand for money in the current period may also change due to 
changes in the explanatory variables i.e., y and i in the current and past periods. 
Therefore, (2) may be specified in a more general form as follows: 
 
[ ] 1 1 1
1 2 3
0 0
ln ln ( ln )                
               + ln + + ln     (3)
t t t t
n n n
i t i i t i i t i
i i i i
m m y i
y i m
l a b g
j v y
- - -
- - -
= = =
D = - - + -
D D + D ￿ ￿ ￿
 
 
Note that the changes in the lagged dependent variable are added to (3) to give a very 
general dynamic specification to the adjustment process. This is known as the general 
unrestricted (dynamic) model (GUM). 
 
Equation (3) can be estimated with the classical methods. The instrumental variables 
method can be also used to minimize any endogenous variable bias. The insignificant 
lagged changes in the variables can be deleted. PcGETS and now PcGIVE of Hendry 
and Krolzig (2001, 2005) have routines to quickly determine the significant lagged 
changes in the variables. Deleting the insignificant changes in the variables gives a 
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(equilibrium) theory by analysing the estimated parameters of the expression in ECM. 
It can also be used to make forecasts, but testing theories is its main strength. 
 
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 to this approach, the coefficients of the cointegrating equation and 
the dynamic adjustment are estimated in GETS in one step by estimating (3) in one 
step. 
 
The above similarities between these two methods have been ignored by many 
critiques of GETS, who in spite of repeated clarifications by Hendry, argued that the 
level variables in the ECM are non-stationary and therefore classical methods of 
estimation 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 the 
underlying economic theory is correct, the combination of variables in the ECM must 
be stationary. Conceptually this is similar to the example of a drunken farmer and his 
dog to explain the concept of cointegration. In our example of the demand for money,  
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 
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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 (Fiji) we have 
extensively used GETS as well as the standard time series and cointegration 
techniques.
4 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 seems to be second to none. 
                                                    
4 At the time of writing this paper the first author was professor of economics at the University of the South 
Pacific (USP) and he has extensively used GETS in his time series courses. The third author was  a 
graduate student at the USP. 
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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. 
 
 
 
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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;,

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Research Papers in Economics