Bayesian Forecasting And Dynamic Models

2016

706 citations recorded by the source corpus.

Citations in the corpus

• Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images (2015, methodology): “In a broader context our model is also related to work on learning non-linear (latent) dynamic models for observations in a Markov decision process through, e.g., hidden Markov models and Kalman filters[34, 35, 36].”
• The Dynamics of State Policy Liberalism, 1936–2014 (2016, methodology): “In this application we use a local-level DLM, which models ↵jt using a “random walk” prior centered on ↵j,t 1: ↵jt ⇠ N(↵j,t 1, 2↵).”
• Measuring Dynamic Connectedness with Large Bayesian VAR Models (2018, unknown): “It can be shown that the time t prior of the covariance matrix is of the form Σt|Dt−1 ∼ iW ( St|t−1, nt|t−1 ) (2) and the resulting posterior is Σt|Dt ∼ iW (St, nt) (3) where iW denotes the inverse Wishart distribution, and exact expressions for the scale St and degrees of freedom parameters nt are ”
• Large Order-Invariant Bayesian VARs with Stochastic Volatility (2021, methodology): “Multivariate SV models based on this approach are considered in Uhlig (1997), West and Harrison (2006), Prado and West (2010), Bognanni (2018) and Arias, Rubio-Ramirez, and Shin (2022).”
• bridgesampling: An R Package for Estimating Normalizing Constants (2017, unknown): “Here we reanalyze a data set that contains the changes in monthly international exchange rates for pounds sterling from January 1975 to December 1986 (West and Harrison 1997, pp. 612-615).”
• Bayesian forecasting in economics and finance: A modern review (2022, background): “Whilst excellent textbook treatments of Bayesian forecasting are given in Geweke (2005) and West and Harrison (2006), and with Geweke and Whiteman (2006) reviewing specific aspects of Bayesian forecasting in a slew of practical settings, the field has advanced by leaps and bounds in the last twenty…”
• On the Long-Run Volatility of Stocks (2018, methodology): “Assuming the use of standard priors, posterior sampling for this model relies on well-studied dynamic linear model (West and Harrison 1997; Prado and West 2010) and stochastic volatility techniques (Jacquier, Polson, and Rossi 2002; Chib, Nardari, and Shephard 2002).”
• Stock Price Booms and Expected Capital Gains (2017, unknown): “The subjective price beliefs (25),(26) and (31) generate perfect foresight equilibrium 46See theorem 3.1 in West and Harrison (1997).”
• Bayesian inference for palaeoclimate with time uncertainty and stochastic volatility (2015, methodology): “We structure our model similarly to that defined by Berliner (1996), though similar concepts are found in state space models (e.g. Cressie and Wikle (2011)) and dynamic linear models (West and Harrison, 1999).”
• Of quantiles and expectiles: consistent scoring functions, Choquet representations and forecast rankings (2015, unknown): “…Yt ∼N .0, σ̂2t / where σ̂2t = t−1∑ s=1 ν t−s−1Y 2s / t−1∑ s=1 ν t−s−1: .47/ This simplistic model is closely related to some more complicated methodologies that are frequently employed in time series forecasting, including dynamic linear models and exponential downscaling (West and Harrison, 1999).”
• Build, Compute, Critique, Repeat: Data Analysis with Latent Variable Models (2014, background): “See West and Harrison (1997) for a general probabilistic perspective on continuous time-series.”
• Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction (2016, background): “Unlike the AR and DLM models above, state-of-the-art MF methods scale linearly in n , and hence can handle large datasets.”
• Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm (2005, unknown): “(1) in which all the dipole–dipole couplings and chemical shielding parameters are varied.”
• A general method for addressing forecasting uncertainty in inventory models (2019, background): “A broad discussion of Bayesian forecasting is provided by West and Harrison (1997), who present predictive distributions for dynamic linear models, autoregressive models, and non-linear models, among others.”