Cobb, Marcus P A (2017): Aggregate Density Forecasting from Disaggregate Components Using Large VARs.
When it comes to point forecasting there is a considerable amount of literature that deals with ways of using disaggregate information to improve aggregate accuracy. This includes examining whether producing aggregate forecasts as the sum of the component’s forecasts is better than alternative direct methods. On the contrary, the scope for producing density forecasts based on disaggregate components remains relatively unexplored. This research extends the bottom-up approach to density forecasting by using the methodology of large Bayesian VARs to estimate the multivariate process and produce the aggregate forecasts. Different specifications including both fixed and time-varying parameter VARs and allowing for stochastic volatility are considered. The empirical application with GDP and CPI data for Germany, France and UK shows that VARs can produce well calibrated aggregate forecasts that are similar or more accurate than the aggregate univariate benchmarks.
|Item Type:||MPRA Paper|
|Original Title:||Aggregate Density Forecasting from Disaggregate Components Using Large VARs|
|Keywords:||Density Forecasting; Bottom-up forecasting; Hierarchical forecasting; Bayesian VAR; Forecast calibration|
|Subjects:||C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods
E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications
|Depositing User:||Marcus Cobb|
|Date Deposited:||15 Feb 2017 17:12|
|Last Modified:||15 Feb 2017 17:13|
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