Working Paper: CEPR ID: DP5295
Authors: Massimiliano Marcellino
Abstract: Pooling forecasts obtained from different procedures typically reduces the mean square forecast error and more generally improves the quality of the forecast. In this paper we evaluate whether pooling interpolated or backdated time series obtained from different procedures can also improve the quality of the generated data. Both simulation results and empirical analyses with macroeconomic time series indicate that pooling plays a positive and important role also in this context.
Keywords: Factor Model; Interpolation; Kalman Filter; Pooling; Spline
JEL Codes: C32; C43; C82
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Pooling (C83) | Reduced Mean Square Forecast Error (C53) |
| Pooling (C83) | Improved Econometric Outcomes (C51) |
| Pooling methods (C54) | Enhanced Data Quality (L15) |
| Pooling versus Single Methods (C15) | Reduced Mean Square Error (C20) |
| Pooling (C83) | Reduced Bias in Econometric Analyses (C51) |
| Best Method (DGM) (C69) | Pooling as Close Second Best (C52) |