A MIDAS approach to modeling first and second moment dynamics

DOI10.1016/j.jeconom.2016.04.009zbMath1391.62293OpenAlexW3125253874MaRDI QIDQ726588

Davide Pettenuzzo, Rossen Valkanov, Allan G. Timmermann

Publication date: 12 July 2016

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jeconom.2016.04.009

zbMATH Keywords

Bayesian estimationout-of-sample forecastsGibbs sampling methodsindustrial productioninflation forecastsmixed-data-sampling (MIDAS) modelsstochastic votality

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Statistical methods; economic indices and measures (91B82)

Related Items (7)

Incorporating overnight and intraday returns into multivariate GARCH volatility models ⋮ Fat tails in leading indicators ⋮ Uncertainty through the lenses of a mixed-frequency Bayesian panel Markov-switching model ⋮ Unnamed Item ⋮ Bayesian MIDAS penalized regressions: estimation, selection, and prediction ⋮ Macroeconomics and the reality of mixed frequency data ⋮ Nowcasting with large Bayesian vector autoregressions

Uses Software

• Gibbsit

Cites Work

• Unnamed Item
• MIDAS Regressions: Further Results and New Directions
• Macroeconomics and the reality of mixed frequency data
• Optimal prediction pools
• Bayesian fan charts for U.K. Inflation: Forecasting and sources of uncertainty in an evolving monetary system
• Markov chain Monte Carlo methods for stochastic volatility models.
• Real-Time Density Forecasts From Bayesian Vector Autoregressions With Stochastic Volatility
• Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules
• An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator
• Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models
• Identifying Long-Run Risks: A Bayesian Mixed-Frequency Approach
• Bayes Factors
• Strictly Proper Scoring Rules, Prediction, and Estimation

This page was built for publication: A MIDAS approach to modeling first and second moment dynamics