15,193 research outputs found
Bayesian Fused Lasso regression for dynamic binary networks
We propose a multinomial logistic regression model for link prediction in a
time series of directed binary networks. To account for the dynamic nature of
the data we employ a dynamic model for the model parameters that is strongly
connected with the fused lasso penalty. In addition to promoting sparseness,
this prior allows us to explore the presence of change points in the structure
of the network. We introduce fast computational algorithms for estimation and
prediction using both optimization and Bayesian approaches. The performance of
the model is illustrated using simulated data and data from a financial trading
network in the NYMEX natural gas futures market. Supplementary material
containing the trading network data set and code to implement the algorithms is
available online
Dynamic Bayesian Predictive Synthesis in Time Series Forecasting
We discuss model and forecast combination in time series forecasting. A
foundational Bayesian perspective based on agent opinion analysis theory
defines a new framework for density forecast combination, and encompasses
several existing forecast pooling methods. We develop a novel class of dynamic
latent factor models for time series forecast synthesis; simulation-based
computation enables implementation. These models can dynamically adapt to
time-varying biases, miscalibration and inter-dependencies among multiple
models or forecasters. A macroeconomic forecasting study highlights the dynamic
relationships among synthesized forecast densities, as well as the potential
for improved forecast accuracy at multiple horizons
Bayesian nonparametric sparse VAR models
High dimensional vector autoregressive (VAR) models require a large number of
parameters to be estimated and may suffer of inferential problems. We propose a
new Bayesian nonparametric (BNP) Lasso prior (BNP-Lasso) for high-dimensional
VAR models that can improve estimation efficiency and prediction accuracy. Our
hierarchical prior overcomes overparametrization and overfitting issues by
clustering the VAR coefficients into groups and by shrinking the coefficients
of each group toward a common location. Clustering and shrinking effects
induced by the BNP-Lasso prior are well suited for the extraction of causal
networks from time series, since they account for some stylized facts in
real-world networks, which are sparsity, communities structures and
heterogeneity in the edges intensity. In order to fully capture the richness of
the data and to achieve a better understanding of financial and macroeconomic
risk, it is therefore crucial that the model used to extract network accounts
for these stylized facts.Comment: Forthcoming in "Journal of Econometrics" ---- Revised Version of the
paper "Bayesian nonparametric Seemingly Unrelated Regression Models" ----
Supplementary Material available on reques
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