When reading about any predictive technique the first thing to focus on before trying to understand the predictive mechanism is to understand the predictive acc

When reading about any predictive technique the first thing to focus on before trying to understand the predictive mechanism is to understand the predictive accuracy of the model. A paper like this should have extensive discussions of overfitting, underfitting and overall predictive accuracy. The entirety of this paper's attempt to address this is: "Last, to avoid arbitrary fitting and to obtain a robust measure of forecast skill, we apply a fourfold cross-validation scheme for each model: the model is fit to three-fourths of the data to predict the remaining one-fourth out-of-sample, and the procedure is repeated for each one-fourth segment of the time series." They are dealing with time series data, which naturally has temporally correlated observations so a cross-validation like this will give a biased error estimate that will favor the more complex model (it encourages overfitting). I haven't delved into the details, but compared to the Ricker model their model is likely more complex so I have very little confidence in their claims of accuracy. Of course, the choice of the Ricker model (developed in the 1950's) is really a strawman in any case. There are much better competitive techniques they could have chosen to benchmark their work.