Empirical prediction intervals revisited

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How predictable is technological progress (2015, unknown): “In this case, one can use either the in-sample errors (the residuals, as in e.g. Taylor & Bunn (1999)), or the out-of-sample forecast errors (Williams & Goodman 1971, Lee & Scholtes 2014).”
Tales from tails: On the empirical distributions of forecasting errors and their implication to risk (2019, background): “The necessity of considering the asymmetry of forecasting error distributions was also stressed by Lee & Scholtes (2014) who concluded that prediction intervals that are based on empirical out-of-sample evaluations are more robust and manage to capture uncertainty more eﬀectively.”