A computationally efficient method for vector autoregression with mixed frequency data
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Publication:726603
DOI10.1016/j.jeconom.2016.04.016zbMath1431.62415OpenAlexW2345730366MaRDI QIDQ726603
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.2016.04.016
Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)
Related Items (2)
Quasi-maximum likelihood estimation of GARCH models in the presence of missing values ⋮ The rescaled VAR model with an application to mixed-frequency macroeconomic forecasting
Cites Work
- MIDAS Regressions: Further Results and New Directions
- Predicting volatility: getting the most out of return data sampled at different frequencies
- Maximum Likelihood Fitting of ARMA Models to Time Series with Missing Observations
- Exact likelihood of vector autoregressive-moving average process with missing or aggregated data
- On Gibbs sampling for state space models
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