Working Paper: CEPR ID: DP7139
Authors: M. Hashem Pesaran; Andreas Pick; Allan G. Timmermann
Abstract: This paper conducts a broad-based comparison of iterated and direct multi-step forecasting approaches applied to both univariate and multivariate models. Theoretical results and Monte Carlo simulations suggest that iterated forecasts dominate direct forecasts when estimation error is a first-order concern, i.e. in small samples and for long forecast horizons. Conversely, direct forecasts may dominate in the presence of dynamic model misspecification. Empirical analysis of the set of 170 variables studied by Marcellino, Stock and Watson (2006) shows that multivariate information, introduced through a parsimonious factor-augmented vector autoregression approach, improves forecasting performance for many variables, particularly at short horizons.
Keywords: Factor-augmented VAR; Forecast horizon; Macroeconomic forecasting
JEL Codes: C53; E27
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| iterated forecasts (C53) | superior forecasting accuracy (C53) |
| estimation error is a primary concern (C51) | superior forecasting accuracy (C53) |
| small samples and long forecast horizons (C53) | superior forecasting accuracy (C53) |
| dynamic model misspecification (C32) | outperform iterated forecasts (C53) |
| multivariate information (C39) | enhanced forecasting performance (C53) |
| factor-augmented VAR approach (C22) | enhanced forecasting performance (C53) |
| short horizons (G14) | enhanced forecasting performance (C53) |