dynamic factor modelNelson-Siegel modelyield curveThis paper proposes the Bayesian semiparametric dynamic Nelson-Siegel model for estimating the density of bond yields. Specifically, we model the distribution of
2.2 Bayesian dynamic model averaging For the second building block of our method, we propose a dynamic procedure for selecting and combining dynamic quantile regression (QR) models. The challenge of determining which regressors to include in the model can be addressed from two perspectives. One appr...
Bayes factor The ratio of the posterior odds to the prior odds of two competing hypotheses, also calculated as the ratio of the marginal likelihoods under the two hypotheses. It can be used, for example, to compare candidate models, where each model would correspond to a hypothesis. Credible...
We focus on factor analysis models (Spearman, 1904, Thurstone, 1934), which factorize the matrix of observations X into lower dimensional loading Ψ and score Θ matrices X=ΨΘ. The loading matrix captures correlations and scores the variability of the data. Additionally, we can use a smoothi...
Li and Ng (2000), Yu et al. (2010) and Bodnar et al. (2015) present exact solutions for their dynamic portfolio selection models assuming no unknown parameters. In the continuous setting, Brennan (1998) and Xia (2001) possess analytical solutions in the context of Bayesian learning based ...
Their finding showed that the (nonzero) noise covariance matrix is important to a state-space model. Liu et al. (2006) extended the state-space neural network (SSNN) by incorporating the extended Kalman filtering (EKF) algorithm. They compared their approach with two existing models, namely ...
Previous studies of strategic social interaction in game theory have predominantly used games with clearly-defined turns and limited choices. Yet, most real-world social behaviors involve dynamic, coevolving decisions by interacting agents, which poses c
[46], for a prediction exercise with dynamic factor models [47]. In Fig. 5, the predictive density and the real value of the natural gas price for observation 25 (2008) are depicted. Response variable on the abscissa is the natural gas price which was called dependent variable and is ...
We provide an overview of Bayesian model averaging (BMA), starting with a summary of the mathematics associated with classical BMA, including the calculation of posterior model probabilities and the choice of priors for both the models and the model parameters. We also consider prediction-based appr...
Bayesian network is a directed acyclic graph and reflects a series of probabilistic dependency relationships among different variables without consideringtime factorsto the variables. When time is considered as an additional factor of Bayesian network, it would become dynamic Bayesian network (DBN) which...