Model selection when there are multiple breaks

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Unpredictability in economic analysis, econometric modeling and forecasting (2014, background): “However, insample dominance does not ensure the avoidance of forecast failure, and Clements and Hendry (1998) establish the robustness after location shifts of double-differenced forecasting devices, such as ∆x̂T+1|T = ∆xT , which can outperform despite their non-congruence and non-encompassing.”
Testing for Structural Stability of Factor Augmented Forecasting Models (2014, background): “Mild factor loading instability, particularly if sufficiently independent across the different constituent variables, does not affect the estimation of the number of factors, nor subsequent estimation of the factors themselves (see e.g. Stock and Watson (2009)).”
Detecting Location Shifts during Model Selection by Step-Indicator Saturation (2015, methodology): “The non-null retention frequency when selecting indicators is called its potency, akin to a similar test’s power for rejecting a false null hypothesis: see [10] for simulations of IIS under the alternative.”
MODEL DISCOVERY AND TRYGVE HAAVELMO’S LEGACY (2014, background): “This implements Haavelmo’s program relatively costlessly.”