Predictive Regressions with Time-Varying Coefficients

By Thomas Dangl, Michael Halling. 2012

550 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/29d5e62f5d04be5d806131a002f7f8d365b8f6eb

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Citations in the corpus

Sequential Learning, Predictability, and Optimal Portfolio Returns: Sequential Learning, Predictability, and Optimal Portfolio Returns (2014, unknown): “performance”
Sparse Signals in the Cross‐Section of Returns (2018, unknown): “For example, DeMiguel et al. (2009a) explains why norm-constrained estimators will perform better out of sample in the presence of estimation error. Both Freyberger et al. (2017) and Feng et al. (2017) use the LASSO to identify characteristics related to the cross-section of expected returns at the ”
Testing predictive regression models with nonstationary regressors (2014, unknown): “…Stambaugh (1986, 1999), Elliott and Stock (1994), Cavanagh et al. (1995), Viceira (1997), Amihud and Hurvich (2004), Torous et al. (2004), Lewellen (2004), Paye and Timmermann (2006), Campbell and Yogo (2006), Polk et al. (2006), Dangl and Halling (2012), Rossi (2007), and the references therein.”
Large time-varying parameter VARs (2013, methodology): “…(also known as discount factors), which have long been used with state space models (see, e.g., Raftery et al. (2010), and the discussion and citations therein), do not require the use of MCMC methods and have been found to have desirable properties inmany contexts (e.g. Dangl and Halling (2012)).”
Time-Varying Fund Manager Skill: Time-Varying Fund Manager Skill (2012, unknown): “After all, expected stock returns vary with the state of the business cycle (e.g., Ferson and Harvey (1991) and Dangl and Halling (2011)).”
Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy (2010, unknown): “First, Fama and French (1989) and Cochrane (1999, 2007) contend that heightened risk aversion during economic downturns requires a higher risk premium, thereby generating equity premium predictability.”
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.”
Do Return Prediction Models Add Economic Value (2012, unknown): “(10), holds the optimal portfolio for one period and then reoptimizes the portfolio weights the following period based on any new information.”
Linear Hypothesis Testing in Dense High-Dimensional Linear Models (2016, background): “Some studies have found that the risk premia of stock market returns have diﬀerent predictability, depending on whether the macroeconomy is in recession or expansion; see Rapach et al. (2010), Henkel et al. (2011) and Dangl and Halling (2012).”
Measuring Dynamic Connectedness with Large Bayesian VAR Models (2018, unknown): “4 West (2010), and Dangl and Halling (2012) and consider the idea of variance discounting.”
Why Does Return Predictability Concentrate in Bad Times (2017, background): “2Rapach, Strauss, and Zhou (2010), Henkel, Martin, and Nardari (2011), Dangl and Halling (2012), and Piatti and Trojani (2015) find that macro variables, such as the pricedividend ratio, have better predictive power in recessions.”
Forecasting the European carbon market (2013, background): “Empirical papers such as Dangl and Halling (2012) and Koop and Korobilis (2011) find DMA to be a useful approach when forecasting with financial or macroeconomic time series.”
Predictability of stock returns and asset allocation under structural breaks (2011, unknown): “This break model is quite different from the drifting coefficients (random walk) models studied by Dangl and Halling (2008) and Johannes, Korteweg, and Polson (2009).”
Stock return predictability from a mixed model perspective (2020, unknown): “1 , N M 1 , N M 1 1 ( ) , N t A V G t k r r k N       (14)”
Time-varying sparsity in dynamic regression models (2014, unknown): “• This is an explanation of why “static” regression models, where predictor effects are assumed constant over time often produce poor out-of-sample forecasts or predictions when fitted to different time periods (see Fisher and Statman (2006), Paye and Timmermann (2006) and Dangl and Halling (2012)).”