How does multicollinearity affect a toy model under laboratory circumstances and what can be done about it — Background Multicollinearity is a topic that every first-year student comes across, even before multiple regression appears on the curriculum. However, as a small finger exercise, it is interesting to engage in some toy models, to understand exactly how regression models are affected, and more interestingly, in which case they are not…

Domain knowledge remains a deciding factor in machine learning applications — Background Time series forecasting is usually a complex task because the structure of already univariate data often contains many unobserved factors. Standard models such as ARIMA, or filters, e.g. Kalman Filter are complex models that often need tweaking which requires a rigorous understanding of the underlying theory.

Goals and contents ARIMA timeseries models are often taught in econometrics courses as part of the regular business science curriculum and are thus put to use by sometimes inexperienced data scientists. The intention of this case study is to understand the data generating process behind simple MA(1) models and illustrate weakness of the…