We evaluate stock return predictability using a fully flexible Bayesian framework, which explicitly allows for different degrees of time-variation in coefficients and in forecasting models. We believe that asset return predictability can evolve quickly or slowly, based upon market conditions,...

We evaluate stock return predictability using a fully flexible Bayesian framework, which explicitly allows for different degrees of time-variation in coefficients and in forecasting models. We believe that asset return predictability can evolve quickly or slowly, based upon market conditions,...

We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no arbitrage restrictions by regularizing appropriate groups of coefficients. The second...

This article considers a stable vector autoregressive (VAR) model and investigates return predictability in a Bayesian context. The VAR system comprises asset returns and the dividend-price ratio as proposed in Cochrane (2008), and allows pinning down the question of return predictability to the...

We study whether a large set of financial ratios provides valuable information about future excess stock returns. Confronted with a data-rich environment, we propose a novel ``divide and conquer" methodology that allows to efficiently retain all of the information available to investors. In...

This paper discusses how the forecast accuracy of a Bayesian vector autoregression (BVAR) is affected by introducing the zero lower bound on the federal funds rate. As a benchmark I adopt a common BVAR specification, including 18 variables, estimated shrinkage, and no nonlinearity. Then I...

We extend the literature on economic forecasting by constructing a mixed-frequency time-varying parameter vector autoregression with stochastic volatility (MF-TVP-SVVAR). The latter is able to cope with structural changes and can handle indicators sampled at different frequencies. We conduct a...

We develop a vector autoregressive model with time variation in the mean and the variance. The unobserved time-varying mean is assumed to follow a random walk and we also link it to long-term Consensus forecasts, similar in spirit to so called democratic priors. The changes in variance are...

Im Zentrum dieser Dissertation steht das Beschreiben und Erklären von Konjunkturdynamiken. Motiviert durch den außerordentlich starken wirtschaftlichen Einbruch in 2008/2009 betont die Arbeit dabei die Wichtigkeit der Nutzung von nichtlinearen Modellansätzen. Die Dissertation kann als Beitrag...

This paper provides a detailed assessment of the real-time forecast accuracy of a wide range of vector autoregressive models (VAR) that allow for both structural change and indicators sampled at different frequencies. We extend the literature by evaluating a mixed-frequency time-varying...