Comparative Analysis and Robustness Tests
This chapter shows how alternative specifications perform when they are used to estimate growth effects of variables with time series and panel datasets. Of the three methods compared, Rao and Singh's approach seems to work the best. This validates the earlier observations that models with stronger theoretical basis perform better. To gain further confidence in these findings, this chapter applies Sala-i-Martin's (1997) and Levine and Renelt's (1992) extreme bounds procedures to test the robustness of variables and the adopted specifications. This is an important but neglected issue in modeling economic growth and in many other empirical studies. The robustness test results imply that all the tested variables are important determinants of long-run growth, estimated with correct signs, plausible magnitudes, and with high confidence. Globalization and financial sector variables show small but statistically significant and robust relationships with long-run growth. Government spending and investment rate are growth enhancing while high rates of inflation are negatively associated with long-run growth. Analyses in this chapter imply that because of the consistent results obtained from alternative tests, Rao and Singh's (2007) extension seems to be robust and useful for empirical application. This result, therefore, provides greater confidence in the application and use of this new extension in empirical studies, especially for but not restricted to, country-specific studies.