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timing of the structural breaks. In our case, since we have precise information about the structural breaks, we opted for using Perron methodology, given its simplicity.t" Figure 1 shows fitted trends with one-shot intercept and/or slope changes for at least two clearly identified regimes in the auction exchange rate series for the cases of Zambia, Uganda, and Nigeria. Three distinct regimes can be identified for Ghana. The results of the tests, and the models of structural breaks corresponding to the fitted trends, are shown in Table 1, together with the critical values employed. The table reveals the presence of considerable non-stationarity and regime- shifts for most of the auction data from the four countries even when structural breaks were taken into account. This is unsurprising in view of the stylised facts which emphasise rule changes, the non-normality of auction data and the importance of anticipations of policy changes.

Besides generating consistent estimates of the economic parameters implied by a model specification such as in section 2.1, the other main objective of our econometric methodology is to test some policy proposition **s** uggested by auction theory (see section 2.2). This requires that the equilibrium model should be esimated asmptotically efficiently. Cointegration readily guarantees consistent (in fact super-consistent) estimation for the eqailibrium parameters using a simple OLS regression (see Engle and Granger, 1987)." Unfortunately, the simple cointegration regression usually produces substantially inefficient asymptotic estimators (Phillips and Loretan, 1991). Given our interest in generating asymptotically efficient pointwise estmators with smaller margins of errors around the true equilibrium parameters, the direct cointegration estimation will not be adequate for the problem at hand. Phillips and Loretan propose a modified ECM that can be used to obtain asymptotically efficient estimation of longrun equaiibria in models with stochastic trends. Subscribing to the above empirical paradigm, we will model the dynamic behavior of the auction variables with a single-equation error correction model (ECM), with leads and lags in the differences of the regressors, and which includes structural break dummy variables. This type of model will be estimated by non-linear OLS, and is recommended by Phillips and Loretan (1991) for hypothesis testing. The form of the modified one-step ECM regression equation is as follows: IPedron (1989) computed critical values for a Dickey-Fuler and Augmented Dickey-Fuller tests that include two types of stwuctual **breaks:** one causing a shift in the intercept, and dt other a change in the slope. A key assumption of the Perron est is **dtat** these shocks are exogenous and are not a ralization of the **underlying data** generating mecham. Furtbermore, his test requires hat the fiming of the shocks be known. In our ae both of these two conditions apply (Aron and Elbadawi, 1994, Table 1).

19 The justification for cointgrtion give. the short span of high frequency (weekly) data may be somewhat problematic: ideally the requirment for non-stationarity tests to be valid is both long time saies data and high frequecy darN. However, the span of our data is comparable with that commonly analysed in stock market and foreign exchange settings (e.g. Froot and Obstfeld, 1991 and references cited therein).

20 Phillips and Lretan (1991) evaluatevarious empirical methods for esimating co-integting relationships, and show that single equation ECM models are efficient asymptotic estimators of long-run equilibrium relationships when formulated noninearly dtough the explicit inclusion of lagged equilibria, and incorpoatig leads and lags of differenced regressors.