This paper describes how to resolve a recurrent research problem in finance research, that is, how to identify and then take steps to correct structural breakpoints in time series data sets. A review of finance literature suggests that the familiar method of identifying breaks is by using news reports of events, which is not accurate in a formal sense, and will likely introduce estimation errors in research. There exist formal models, which are used to accurately identify breaks especially in long-time series to pre-test exchange rate data series as a pre-analysis step to accurately locate breaks that will help control estimation errors introduced from breakpoint impacts. The findings from testing four-country data series, using 651 months data of each country, suggested that the method described in this study identified breakpoints accurately, which was also verified using graphs. Therefore, it is suggested that this process is helpful for researchers to formally identify structural breakpoints as it greatly improves the robustness of estimation of exchange rate behaviour (apart from other financial variables).