How is the ARIMA model a valid approach for forecasting Economic Variables? Ive noticed the use of univariate forecasting methods (i.e the ARIMA model) employed in forecasting inflation (see references below).

How is this a valid approach in terms of forecasting economic variables? does this approach not omit the large amount of economic theory which determines how these variables are determined (i.e via equilibrium and interaction of variables other than themselves?)



1.https://scholar.harvard.edu/files/stock/files/forecastinginflation.pdf see pages 6-72.http://repository.graduateinstitute.ch/record/294965/files/HEIDWP05-2017.pdfhttp://repository.graduateinstitute.ch/record/294965/files/HEIDWP05-2017.pdf 
3.http://www.unagaliciamoderna.com/eawp/coldata/upload/pakistans%20inflaction_arima%20model.pdf
So your question is basically asking why a purely statistical model would be better at forecasting than a model based on theoretical relationships. For example, think about a simple form of the ARIMA model i.e. an AR model where you're just using lags of the process to explain the process itself. Now think about what is contained in the lags of the process... If the true process (i.e. the DGP) is determined by theoretical relationships between other variables then these will automatically be included in the lag. So the ARIMA model is a statistical way of modeling the process to make sure you capture the statistical properties of the underlying relationship but does not necessarily provide an explanation for what is going on. In forecasting, especially when series are volatile and erratic, you often find that simple statistical models such as these do a better job since the underlying relationships may not be very stable or may be more complicated than any theory we can come up with. This is related to a recent article  which you may find interesting: http://www.sciencedirect.com/science/article/pii/S0169207017300997