A new Bayesian approach to quantile autoregressive time series model estimation and forecasting

DOI10.1111/j.1467-9892.2012.00800.xzbMath1281.62184OpenAlexW1494830222WikidataQ61719205 ScholiaQ61719205MaRDI QIDQ5397942

Neville Davies, Yuzhi Cai, Julian Stander

Publication date: 25 February 2014

Published in: Journal of Time Series Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-9892.2012.00800.x

zbMATH Keywords

combining forecastsMCMCquantile forecastingpredictive density functionsquantile modelling

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Non-Markovian processes: estimation (62M09) Bayesian inference (62F15) Monte Carlo methods (65C05)

Related Items (3)

Bayesian inference for quantile autoregressive model with explanatory variables ⋮ Dynamic quantile linear models: a Bayesian approach ⋮ Estimation of Non-Crossing Quantile Regression Curves

Cites Work

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• Bayesian Inference and Prediction for Mean and Variance Shifts in Autoregressive Time Series
• Quantile self-exciting threshold autoregressive time series models
• BAYESIAN ANALYSIS OF AUTOREGRESSIVE TIME SERIES VIA THE GIBBS SAMPLER
• Convergence Theory of a Numerical Method for Solving the Chapman--Kolmogorov Equation
• Forecaster Diversity and the Benefits of Combining Forecasts
• Forecasting for quantile self-exciting threshold autoregressive time series models
• Monte Carlo sampling methods using Markov chains and their applications
• A Bayesian Approach to Understanding Time Series Data
• Unit Root Quantile Autoregression Inference
• Quantile Autoregression
• Bayesian quantile regression

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