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Price-series correlations are convenient measures of market integration since they rely only
on price data, which are more readily available than the cost data required to evaluate
intermarket price differentials. Many researchers, however, have argued against the use of the
correlation coefficients to measure market integration as it is fraught with problems. Goletti
and Babu (1994), for instance, have noted that correlation coefficients mask the presence of
other synchronous factors, such as general price inflation, seasonality, population growth, and
procurement policy. This shortcoming has led to a proliferation of other methods for testing
market integration. In this study, therefore, we considered a second measure of integration,
the bivariate correlation of price differences. The use of price differences is based on the
observation that market integration could be interpreted as interdependence of price changes.
Using price differences removes the nonstationarity and common time trends present in price
levels solves the problem of spurious correlation. A third approach for testing market
integration is the cointegration approach. This approach examines whether two markets are
integrated in the long term by assessing whether their prices fluctuate within a fixed band. It
is assumed that prices move together, subject to various individual shocks that may cause
temporary divergences. If, in the long run, they exhibit a linear constant relation, then they
are said to be cointegrated. In general, a pair of price series X t and Y t is said to be
cointegrated if the series are individually of the same order of economic integration and there
exists a linear combination of the series such that the measure
ε = Y −α
− βX
(1)
1 t
t,
is stationary or integrated of order zero (Engle and Granger 1991). Such integration is
abbreviated as I(0). A two-step, residual-based test due to Engle and Granger (1987) is often
used to assess whether pairs of markets are integrated. In the first step, price series are tested
for unit roots using the augmented Dickey-Fuller (ADF) test to establish the order of
economic integration. The order of economic integration is the number of times the series
needs to be differenced before it is transformed into a stationary series. Once the order of
economic integration is established, the next step is to test for cointegration of the price
series. To test for cointegration, “cointegrating regressions” are estimated by ordinary least
squares (OLS), that is prices in market i, P i , t, are regressed on prices in market j, P j , t , thus:
P =
i, t α + 0 δ 1 P + (2)
j, t et
The residuals from this regression are then tested for the presence of unit roots. In theory, if
market i is cointegrated with market j, then market j should be cointegrated with market i. In
practice, however, test results may differ. For this reason cointegration tests are typically
repeated for all the markets, interchanging the left-hand and right-hand price variables
(Ngugi, Mataya, and Ng’ong’ola 1997).
To test for Granger causality, Goletti and Babu (1994) suggest the following error correction
model:
Δ
Pi
t
,
=α i + o α i 1 P + i, t − 1 α i 2 P t
j 1
k
, − + ∑ =
=
m i
k 1
δ i k ΔPi
t −k
h
,
+ ∑ =
=
n i
λ i h ΔP j t −h
h 0
,
(3)
where: Δ is the difference operator; m i and n i are the number of lags; and the α’s, δ’s and λ’s
are parameters to be estimated.