한국관련자료
Assessing Forecast Accuracy
- This paper assesses NIESR‘s macroeconomic forecasts, with a particular focus on GDP growth and CPI inflation, produced between 1992 and 2023 across horizons of up to ten years. We document forecast errors, dispersion and formal tests of bias, then evaluate NIESR‘s forecasts against a hierarchy of benchmarks - including a random walk, autoregressive models and a Bayesian VAR - using Diebold-Mariano tests.
- GDP growth forecasts show a significant positive (over-prediction) bias that strengthens with horizon; CPI inflation forecasts show no significant bias. NIESR outperforms the benchmark models on inflation at nearly all horizons, but for GDP growth its advantage is concentrated in the short-to-medium term and reversed beyond around five years. This coincides closely with the point at which over-prediction bias becomes significant, suggesting a correctable bias problem rather than a wholesale loss of forecasting content. Strikingly, this long-horizon weakness is not simply shock-driven: excluding major crisis years collapses the BVAR‘s advantage over NIESR forecasts but leaves the AR(p)‘s advantage intact.
- The results suggest that the benefits of structural modelling depend on both the variable being forecast and the forecast horizon. They also indicate that NIESR‘s long-horizon weakness may reflect systematic optimism rather than a loss of forecasting information, suggesting that horizon-specific bias correction could provide a promising, empirically testable avenue for improving long-horizon GDP forecast accuracy.
[Main points]
- Despite frequent criticism of economic forecasting, macroeconomic models continue to provide an important benchmark for both private sector decision-making and public policy formulation.
- Major shocks - including the Global Financial Crisis, the Covid-19 pandemic and the sharp rise in global energy prices in 2022 - significantly disrupted forecast performance, highlighting the difficulty of macroeconomic forecasting during periods of exceptional instability.
- The central challenge for macroeconomic forecasting is not simply improving baseline model specification but developing frameworks that are more robust to structural breaks, regime shifts and extreme shocks, while also addressing more mundane sources of persistent bias where they can be identified.
- Macroeconomic forecasts provide a consistent framework for analysing the economy and informing policy decisions. However, their usefulness ultimately depends on their predictive accuracy.
- GDP growth forecasts show a significant positive (over-prediction) bias that strengthens with horizon; CPI inflation forecasts show no significant bias. NIESR outperforms the benchmark models on inflation at nearly all horizons, but for GDP growth its advantage is concentrated in the short-to-medium term and reversed beyond around five years. This coincides closely with the point at which over-prediction bias becomes significant, suggesting a correctable bias problem rather than a wholesale loss of forecasting content. Strikingly, this long-horizon weakness is not simply shock-driven: excluding major crisis years collapses the BVAR‘s advantage over NIESR forecasts but leaves the AR(p)‘s advantage intact.
- The results suggest that the benefits of structural modelling depend on both the variable being forecast and the forecast horizon. They also indicate that NIESR‘s long-horizon weakness may reflect systematic optimism rather than a loss of forecasting information, suggesting that horizon-specific bias correction could provide a promising, empirically testable avenue for improving long-horizon GDP forecast accuracy.
[Main points]
- Despite frequent criticism of economic forecasting, macroeconomic models continue to provide an important benchmark for both private sector decision-making and public policy formulation.
- Major shocks - including the Global Financial Crisis, the Covid-19 pandemic and the sharp rise in global energy prices in 2022 - significantly disrupted forecast performance, highlighting the difficulty of macroeconomic forecasting during periods of exceptional instability.
- The central challenge for macroeconomic forecasting is not simply improving baseline model specification but developing frameworks that are more robust to structural breaks, regime shifts and extreme shocks, while also addressing more mundane sources of persistent bias where they can be identified.
- Macroeconomic forecasts provide a consistent framework for analysing the economy and informing policy decisions. However, their usefulness ultimately depends on their predictive accuracy.
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