A comprehensive evaluation of macroeconomic forecasting methods

TY - JOUR

T1 - A comprehensive evaluation of macroeconomic forecasting methods

AU - Carriero, Andrea

AU - Galvão, Ana Beatriz

AU - Kapetanios, George

PY - 2019

Y1 - 2019

N2 - We employ datasets for seven developed economies and consider four classes of multivariate forecasting models in order to extend and enhance the empirical evidence in the macroeconomic forecasting literature. The evaluation considers forecasting horizons of between one quarter and two years ahead. We find that the structural model, a medium-sized DSGE model, provides accurate long-horizon US and UK inflation forecasts. We strike a balance between being comprehensive and producing clear messages by applying meta-analysis regressions to 2,976 relative accuracy comparisons that vary with the forecasting horizon, country, model class and specification, number of predictors, and evaluation period. For point and density forecasting of GDP growth and inflation, we find that models with large numbers of predictors do not outperform models with 13–14 hand-picked predictors. Factor-augmented models and equal-weighted combinations of single-predictor mixed-data sampling regressions are a better choice for dealing with large numbers of predictors than Bayesian VARs.

AB - We employ datasets for seven developed economies and consider four classes of multivariate forecasting models in order to extend and enhance the empirical evidence in the macroeconomic forecasting literature. The evaluation considers forecasting horizons of between one quarter and two years ahead. We find that the structural model, a medium-sized DSGE model, provides accurate long-horizon US and UK inflation forecasts. We strike a balance between being comprehensive and producing clear messages by applying meta-analysis regressions to 2,976 relative accuracy comparisons that vary with the forecasting horizon, country, model class and specification, number of predictors, and evaluation period. For point and density forecasting of GDP growth and inflation, we find that models with large numbers of predictors do not outperform models with 13–14 hand-picked predictors. Factor-augmented models and equal-weighted combinations of single-predictor mixed-data sampling regressions are a better choice for dealing with large numbers of predictors than Bayesian VARs.

KW - BVAR models

KW - Density forecasts

KW - DSGE models

KW - Factor models

KW - Meta-analysis

KW - MIDAS models

UR - http://www.scopus.com/inward/record.url?scp=85067920910&partnerID=8YFLogxK

U2 - 10.1016/j.ijforecast.2019.02.007

DO - 10.1016/j.ijforecast.2019.02.007

M3 - Article

AN - SCOPUS:85067920910

SN - 0169-2070

VL - 35

SP - 1226

EP - 1239

JO - INTERNATIONAL JOURNAL OF FORECASTING

JF - INTERNATIONAL JOURNAL OF FORECASTING

IS - 4

ER -