*2.4. Estimation Techniques*

The estimation techniques employed in this study are the Mean Group (MG) proposed by Pesaran and Smith (1995), and the Pooled Mean Group (PMG) proposed by Pesaran et al. (1999). The latter estimator assumes homogeneous long-term coefficients across countries but allows for variations in the short-term coefficients, speed of adjustment and error variances. However, the MG estimator allows the long-term and short-term coefficients, the speed of adjustment and the error variances to differ across countries. After estimation, the study conducts the Hausman test of homogeneity of long-term coefficients to determine the appropriate model between MG and PMG estimators. The MG and PMG estimators are chosen for this study for three reasons: (i) the MG and PMG estimators can be applied irrespective of the order of integration of the variables in the model because they are based on Autoregressive Distributed Lag (ARDL) models. In this study, some of the variables in the model are integrated in order zero [I (0)], while some variables are integrated in order one [I (1)]. (ii) The MG and PMG estimators provide both short-term and long-term estimation results. By distinguishing between short-term and long-term impacts, these estimators provide viable options for policy making. (iii) While the PMG estimator accounts for heterogeneity in short-term coefficients, the MG estimator accounts for heterogeneity in both short-term and long-term coefficients.

To complement the MG and PMG estimators, this study also employs an Instrumental Variable (IV) approach based on Two Stage Least Squares (TSLS), which is capable of controlling for possible endogeneity. The instruments used are the legal origin of the countries as well as the initial values of financial development, inflation rate, governmen<sup>t</sup> expenditure, trade openness and human capital (see Asongu 2014; La Porta et al. 1997; Levine et al. 2000; Rousseau and Wachtel 2002). To ensure that the IV approach satisfies the order condition for identification, we perform the tests for endogeneity and over-identifying restrictions. The former shows whether the dependent variable is endogenous in the original model, whereas the latter indicates whether the instruments are uncorrelated with the error process.

Additionally, this study also utilizes the Seemingly Unrelated Regression (SUR) estimator proposed by Zellner (1962) on the disaggregated data to examine the influence of the real exchange rate or its volatility on the finance-growth nexus for individual country. SUR estimator enables us to account for possible cross-sectional dependence. Pesaran (2006) revealed that parameters estimates could be substantially biased, and their sizes could be distorted if cross-sectional dependence is ignored. The SUR estimator is a generalization of a linear regression model, which comprises many regression equations with each having its own dependent and independent variables. Each of the regression equations is a valid linear regression that could be estimated separately, but the regression equations are assumed to be correlated with respect to the error terms (see Bittencourt 2011; Ehigiamusoe et al. 2018). After estimating the equations, the study examines the statistical significance of the Lagrange Multiplier (LM) test in order to determine the presence of cross-sectional dependence among the countries in the panel and the suitability of the SUR estimator.
