If all the econometric analysis techniques, which are applied across the world in the various contexts of economic analysis, are analyzed, then it can be seen that all of the latest complex econometric methodologies have been derived out of regression technique. It is one of the oldest econometric techniques the world of economics has seen. Considering the linear or non-linear associations between independent and dependent variables, this methodology is robust enough to explain the variances of the dependent variable, based on the combination(s) of independent variables.
However, if the literature on modern economic analysis across several domains of economics can be seen, then it can be experienced that the modern researchers are turning out to be reluctant in applying traditional regression technique in their analyses. If being looked at this closely, then it can be found out that there are several reasons for this scenario. Changing business environment is in the demand of more complex analysis of the economic data. For example, the world of capital and financial markets are moving towards algorithmic trading, and for this purpose, they are in the need of high frequency data analysis. This kind of analysis is being carried out on the time series data, which are collected either hourly or daily basis. Now, applying regression analysis on this kind of data may result in spurious outcomes due to the heteroscedasticity and multicolinearity issues. Moreover, in some cases, like environmental and energy economics, it may be possible that positive and negative feedback links being present from dependent to independent variables, and thus both the variables in the analysis are associated by a spiral nature. These are some of the issues, which the traditional regression analysis cannot answer. Therefore the significance of regression analysis is being lost among the community of researchers. For more information about various aspects of regression analysis, kindly browse through the pages of http://www.statworkz.com.