As economic systems are rarely in equilibrium, we will re-specify equation 3 as a short-run or disequilibrium relationship involving lagged values of Y and of the variables on the right hand side (RHS) of the equation as we did for the financial development equation. We will assume the relationship takes the form of equation 4 with first order lags for all variables. The variables in equation 3 are expressed in natural logs and denoted in lower-case letters.

Yt = bo + ,t-y, +bIlrt, + b2lrttl + c1lre, + c21re,, + dlit+ d2it-l + e1sct + e2sct-l + f1tt + f2tt-1 + g1 pt + g2 P1 1 + h1fdt + h2 fd 1l + i1mt+ i2mtl + The variables and their data sources are described in Appendix 1.

# **(c) Empirical Testing and Analysis**

# **(i) Unit Root Tests**<sup>**2**</sup>

More often than not, time series data are non-stationary. In this section, we carry out unit root tests using the Augmented Dickey-Fuller (ADF) method (Dickey and Fuller, 1979) to test for non-stationarity for the key variables in the economic growth and financial deepening equations, using Microfit 4.0 (Pesaran and Pesaran, 1997). The existence of non-stationarity in a data series would mean that, if hit by a shock, a variable would suffer permanent change, since non-stationary variables have a non-constant mean, variance and covariance. On the other hand, faced by a shock, a stationary data series will over time return to its unconditional mean; that is, the effects of the shock will dissipate, leaving only temporary effects on the time series.

T he ADF tests show that only the real deposit rate variable (RD) is stationary at 1(0) while the rest of the variables attain stationarity at I(1) except the per capita income variable (Y) that attains stationarity at 1(2). These ADF results are further verified using correlograms of autocorrelation coefficients. Stationarity is achieved when the 2 We have also carried out other pre-tests such as scatter plots involving dependent variables and the land registration variables in which a positive association was found. Variables were also plotted against time to detect patterns, trends and outliers. In addition to looking out for outliers as a check on data quality, we were particularly interested in detecting shifts or structural changes in the time series data as a basis to select the most appropriate method for unit root tests for stationarity. Specifically, in situations of a permanent shift which could be due to policy changes or external shocks, the normal unit root tests such as the Augmented Dicker-Fuller (ADF) and Phillips-Perron tests, would have low testing power. Such breaks can make an otherwise stationary time series data appear non-stationary. If such breaks in data are known, the unit root tests can be adjusted by including dummy variables to ensure there are as many deterministic regressors as there are deterministic components in the data generating process. If the breaks in data are unknown, other methods can be used for unit root testing such as the Perron method (Perron, 1989). A review of the graphs indicate that there are no breaks in data that would reduce the power of the normal unit root test methods. Hence, the ADF test can be used to carry out the unit root tests and was indeed selected for the test. The scatter plots against time and between dependent and land registration variables can be provided by the author upon request.

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