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AI InsightFactor models perform better than the combination of bi-variate forecasts which indicates that pooling information improves over pooling individual forecasts.
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Özet
In this paper, industrial production growth and core inflation are forecasted using a large number of domestic and international indicators. Two methods are employed, factor models and forecast combination, to deal with the curse of dimensionality problem stemming from the availability of ever growing data sets. A comprehensive analysis is carried out to understand the sensitivity of the forecast performance of factor models to various modelling choices. In this respect, effects of factor extraction method, number of factors, data aggregation level and forecast equation type on the forecasting performance are analyzed. Moreover, the effect of using certain data blocks such as interest rates on the forecasting performance is evaluated as well. Out-of-sample forecasting exercise is conducted for two consecutive periods to assess the stability of the forecasting performance. Factor models perform better than the combination of bi-variate forecasts which indicates that pooling information improves over pooling individual forecasts.