scholarly journals Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic

2021 ◽  
Author(s):  
Frank Schorfheide ◽  
Dongho Song
Keyword(s):  
Real Time ◽  
Mixed Frequency

scholarly journals Real-Time Forecasting with a Mixed-Frequency VAR

2013 ◽  
Author(s):  
Frank Schorfheide ◽  
Dongho Song
Keyword(s):  
Real Time ◽  
Mixed Frequency

scholarly journals Real-Time Forecasting with a Mixed-Frequency VAR

2012 ◽  
Author(s):  
Frank Schorfheide ◽  
Dongho Song
Keyword(s):  
Real Time ◽  
Mixed Frequency

scholarly journals Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic

2020 ◽  
Author(s):  
Frank Schorfheide ◽  
Dongho Song
Keyword(s):  
Real Time ◽  
Mixed Frequency

scholarly journals Real-Time Forecasting and Scenario Analysis using a Large Mixed-Frequency Bayesian VAR

2015 ◽  
Author(s):  
Michael W. McCracken ◽  
Michael T. Owyang ◽  
Tatevik Sekhposyan
Keyword(s):  
Real Time ◽  
Scenario Analysis ◽  
Bayesian Var ◽  
Mixed Frequency

scholarly journals Real-Time Forecasting with a Large, Mixed Frequency, Bayesian VAR

2015 ◽  
Author(s):  
Michael W. McCracken ◽  
Michael Owyang ◽  
Tatevik Sekhposyan
Keyword(s):  
Real Time ◽  
Bayesian Var ◽  
Mixed Frequency

Real‐Time Fiscal Forecasting Using Mixed‐Frequency Data*

2019 ◽  
Vol 122 (1) ◽  
pp. 369-390
Author(s):  
Stylianos Asimakopoulos ◽  
Joan Paredes ◽  
Thomas Warmedinger
Keyword(s):  
Real Time ◽  
Frequency Data ◽  
Mixed Frequency ◽  
Mixed Frequency Data

scholarly journals Real-Time Forecasting With a Mixed-Frequency VAR

2015 ◽  
Vol 33 (3) ◽  
pp. 366-380 ◽  
Author(s):  
Frank Schorfheide ◽  
Dongho Song
Keyword(s):  
Real Time ◽  
Mixed Frequency

scholarly journals REAL-TIME PROBABILISTIC NOWCASTS OF UK QUARTERLY GDP GROWTH USING A MIXED-FREQUENCY BOTTOM-UP APPROACH

2020 ◽  
Vol 254 ◽  
pp. R1-R11
Author(s):  
Ana Beatriz Galvão ◽  
Marta Lopresto
Keyword(s):  
Real Time ◽  
Mixed Data ◽  
Gdp Growth ◽  
Data Sampling ◽  
Bottom Up ◽  
Mixed Frequency ◽  
Econometric Software ◽  
Probabilistic Forecasts ◽  
Mixed Data Sampling ◽  
Predictive Content

We propose a nowcasting system to obtain real-time predictive intervals for the first-release of UK quarterly GDP growth that can be implemented in a menu-driven econometric software. We design a bottom-up approach: forecasts for GDP components (from the output and the expenditure approaches) are inputs into the computation of probabilistic forecasts for GDP growth. For each GDP component considered, mixed-data-sampling regressions are applied to extract predictive content from monthly and quarterly indicators. We find that predictions from the nowcasting system are accurate, in particular when nowcasts are computed using monthly indicators 30 days before the GDP release. The system is also able to provide well-calibrated predictive intervals.

scholarly journals Real-Time Forecasting Using Mixed-Frequency Vars with Time-Varying Parameters

2020 ◽  
Author(s):  
Markus Heinrich ◽  
Magnus Reif
Keyword(s):  
Real Time ◽  
Time Varying ◽  
Varying Parameters ◽  
Mixed Frequency ◽  
Time Varying Parameters
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