Forecast Combination and Interpretability Using Random Subspace
Boris Kozyrev
IWH Discussion Papers,
No. 21,
2024
Abstract
This paper investigates forecast aggregation via the random subspace regressions method (RSM) and explores the potential link between RSM and the Shapley value decomposition (SVD) using the US GDP growth rates. This technique combination enables handling high-dimensional data and reveals the relative importance of each individual forecast. First, it is possible to enhance forecasting performance in certain practical instances by randomly selecting smaller subsets of individual forecasts and obtaining a new set of predictions based on a regression-based weighting scheme. The optimal value of selected individual forecasts is also empirically studied. Then, a connection between RSM and SVD is proposed, enabling the examination of each individual forecast’s contribution to the final prediction, even when there is a large number of forecasts. This approach is model-agnostic (can be applied to any set of predictions) and facilitates understanding of how the aggregated prediction is obtained based on individual forecasts, which is crucial for decision-makers.
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Full sample
Full sample Descriptive Financial Labour Productivity decompositions Trade Markup Joint distributions Back
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Productivity decompositions
Productivity decompositions Back
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Productivity decompositions
Productivity decompositions Back
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20E sample
20E sample Descriptive Financial Labour Productivity decompositions Trade Joint distributions Back
See page
Productivity decompositions
Productivity decompositions Back
See page
Full sample
Full sample Descriptive Financial Labour Productivity decompositions Trade Markup Joint distributions Back
See page
Productivity decompositions
Productivity decompositions Back
See page
20E sample
20E sample Descriptive Financial Labour Productivity decompositions Trade Joint distributions Back
See page
Non-base Compensation and the Gender Pay Gap
Boris Hirsch, Philipp Lentge
LABOUR: Review of Labour Economics and Industrial Relations,
No. 3,
2022
Abstract
This paper investigates whether non-base compensation contributes to the gender pay gap (GPG). Using administrative data from Germany, we find in wage decompositions that lower bonus payments to women explain about 10 per cent of the gap at the mean and at different quantiles of the unconditional wage distribution whereas the lower prevalence of shift premia and overtime pay among women is unimportant. Among managers, the contribution of bonuses to the mean gap more than doubles and is steadily rising as one moves up the wage distribution. Our findings suggest that gender differences in bonuses are an important contributor to the GPG, particularly in top jobs.
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