Besides using these two types of measurements i.e. commodity concentration and geographic concentration as the explanatory variables, this study adopts another explanatory variables namely the share of primary commodity exports (PCC) as its third explanatory variable. Meanwhile, the instability index is used as the dependent variable where it is defined as annual percentage rate of change in the value of exports.
links of london sale It is measured according to major (IMajor), non-manufactured (INManu), manufactured (IManu) and total (ITotal). The commodity concentration index is disaggregated into major (CMajor), non-manufacuted (CNManu), manufactured (CManu) and total (CTotal). This is done to identify the importance of a particular type of commodity concentration that is capable of explaining instability quite significantly. The geographic concentration is also measured at three levels; 5 percent, 1 percent and 0.3 percent as delineated above.
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It is well documented that most economic time series data are nonstationary and the error terms are time-variant. Thus any empirical work based on time series data need to conduct a priori tests to evaluate the stationarity of the series, the order of integration, and the cointegration of two or more variables under consideration. Through our observation on the list of variables that we have, we anticipate that these variables are not integrated of the same order.
Links of London friendship Since the instability (I) index is defined as annual percentage rate of change in the value of exports, the series are normally stationary while other series in the model are non-stationary and contain unit root. This paper employs a cointegrating technique namely the Autoregressive Distributed Lag Model (ARDL) as introduced by Pesaran et al. (2001) which does not require pre-unit root testing. This procedure can be applied irrespective of whether the underlying regressors are I(1), I(0) or fractionally integrated and the long-run coefficient estimates are found to be consistent and asymptotically normally distributed. The ARDL has another advantage of yielding the optimal lag-length for each variable in which it estimates (p + l)^sup k^number of regressions, where p is the maximum lag to be used and k is the number of variables in the equation.
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