Bagged artificial neural networks in forecasting inflation: An extensive comparison with current modelling frameworks
Karol Szafranek ()
No 262, NBP Working Papers from Narodowy Bank Polski, Economic Research Department
Accurate inflation forecasts lie at the heart of effective monetary policy. By utilizing a thick modelling approach, this paper investigates the out-of-sample quality of the short-term Polish headline inflation forecasts generated by a combination of thousands of bagged single hidden-layer feed-forward artificial neural networks in the period of systematically falling and persistently low inflation. Results indicate that the forecasts from this model outperform a battery of popular approaches, especially at longer horizons. During the excessive disinflation it has more accurately accounted for the slowly evolving local mean of inflation and remained only mildly biased. Moreover, combining several linear and nonlinear approaches with diverse underlying model assumptions delivers further statistically significant gains in the predictive accuracy and statistically outperforms a panel of examined benchmarks at multiple horizons. The robustness analysis shows that resigning from data preprocessing and bootstrap aggregating severely compromises the forecasting ability of the model.
Keywords: inflation forecasting; artificial neural networks; principal components; bootstrap aggregating; forecast combination (search for similar items in EconPapers)
JEL-codes: C22 C38 C45 C53 C55 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cba, nep-cmp, nep-ets, nep-for and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:nbp:nbpmis:262
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