Predictability of stock returns and asset allocation under structural breaks

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Predictive Regressions with Time-Varying Coefficients (2012, unknown): “Pettenuzzo and Timmermann (2005) address the issue of model instability (i.e., structural breaks in predictive relationships) and document that it can have a larger impact on optimal asset allocation than other sources of risk such as uncertainty in parameter estimation.”
Predicting Global Stock Returns (2008, unknown): “The overall instability of the regression coefficients over time is in line with the results of the formal test procedures of Pettenuzzo and Timmermann (2005) and Paye and Timmermann (2006).”
Regime Changes and Financial Markets (2012, background): “This is consistent with the ﬁnding in Pettenuzzo and Timmermann (2010) that uncertainty about future regimes can have a large effect on in-vestors’ optimal long-run asset allocation decisions which can even change from being upward-sloping in the investment horizon in the absence of ‘breaks’ to…”
Complete subset regressions (2013, methodology): “Models with timevarying coefficients have been considered for stockmarket data by Griffin and Kalli (2012) and Dangl and Halling (2012), while Pettenuzzo and Timmermann (2011) consider forecast combination in the presence of model instability.”
On the Long-Run Volatility of Stocks (2018, background): “These facts are also true in Pettenuzzo and Timmermann (2011) where expected returns are modeled via predictive regression with structural breaks.”