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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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Our Projects 07.2022 ‐ 12.2026 Evaluation of the InvKG and the federal STARK programme On behalf of the Federal Ministry of Economics and Climate Protection, the IWH and the RWI…

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Energy Markets and Global Economic Conditions

Christiane Baumeister, Dimitris Korobilis, Thomas K. Lee
Review of Economics and Statistics, No. 4, 2022
Abstract
We evaluate alternative indicators of global economic activity and other market funda-mentals in terms of their usefulness for forecasting real oil prices and global petroleum consumption. World industrial production is one of the most useful indicators. However, by combining measures from several different sources we can do even better. Our analysis results in a new index of global economic conditions and measures for assessing future energy demand and oil price pressures. We illustrate their usefulness for quantifying the main factors behind the severe contraction of the global economy and the price risks faced by shale oil producers in early 2020.
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Energy Markets and Global Economic Conditions

Christiane Baumeister, Dimitris Korobilis, Thomas K. Lee
NBER Working Paper, No. 27001, 2020
published in: Review of Economics and Statistics
Abstract
This paper evaluates alternative indicators of global economic activity and other market fundamentals in terms of their usefulness for forecasting real oil prices and global petroleum consumption. We find that world industrial production is one of the most useful indicators that has been proposed in the literature. However, by combining measures from a number of different sources we can do even better. Our analysis results in a new index of global economic conditions and new measures for assessing future tightness of energy demand and expected oil price pressures.
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Nowcasting East German GDP Growth: a MIDAS Approach

João Carlos Claudio, Katja Heinisch, Oliver Holtemöller
Empirical Economics, No. 1, 2020
Abstract
Economic forecasts are an important element of rational economic policy both on the federal and on the local or regional level. Solid budgetary plans for government expenditures and revenues rely on efficient macroeconomic projections. However, official data on quarterly regional GDP in Germany are not available, and hence, regional GDP forecasts do not play an important role in public budget planning. We provide a new quarterly time series for East German GDP and develop a forecasting approach for East German GDP that takes data availability in real time and regional economic indicators into account. Overall, we find that mixed-data sampling model forecasts for East German GDP in combination with model averaging outperform regional forecast models that only rely on aggregate national information.
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Nowcasting East German GDP Growth: a MIDAS Approach

João Carlos Claudio, Katja Heinisch, Oliver Holtemöller
IWH Discussion Papers, No. 24, 2019
published in: Empirical Economics
Abstract
Economic forecasts are an important element of rational economic policy both on the federal and on the local or regional level. Solid budgetary plans for government expenditures and revenues rely on efficient macroeconomic projections. However, official data on quarterly regional GDP in Germany are not available, and hence, regional GDP forecasts do not play an important role in public budget planning. We provide a new quarterly time series for East German GDP and develop a forecasting approach for East German GDP that takes data availability in real time and regional economic indicators into account. Overall, we find that mixed-data sampling model forecasts for East German GDP in combination with model averaging outperform regional forecast models that only rely on aggregate national information.
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Should Forecasters Use Real‐time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence

Katja Heinisch, Rolf Scheufele
German Economic Review, No. 4, 2019
Abstract
In this paper, we investigate whether differences exist among forecasts using real‐time or latest‐available data to predict gross domestic product (GDP). We employ mixed‐frequency models and real‐time data to reassess the role of surveys and financial data relative to industrial production and orders in Germany. Although we find evidence that forecast characteristics based on real‐time and final data releases differ, we also observe minimal impacts on the relative forecasting performance of indicator models. However, when obtaining the optimal combination of soft and hard data, the use of final release data may understate the role of survey information.
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Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment

Katja Heinisch, Rolf Scheufele
Empirical Economics, No. 2, 2018
Abstract
In this paper, we investigate whether there are benefits in disaggregating GDP into its components when nowcasting GDP. To answer this question, we conduct a realistic out-of-sample experiment that deals with the most prominent problems in short-term forecasting: mixed frequencies, ragged-edge data, asynchronous data releases and a large set of potential information. We compare a direct leading indicator-based GDP forecast with two bottom-up procedures—that is, forecasting GDP components from the production side or from the demand side. Generally, we find that the direct forecast performs relatively well. Among the disaggregated procedures, the production side seems to be better suited than the demand side to form a disaggregated GDP nowcast.
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Should Forecasters Use Real-time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence

Katja Heinisch, Rolf Scheufele
IWH Discussion Papers, No. 5, 2017
published in: German Economic Review
Abstract
In this paper we investigate whether differences exist among forecasts using real-time or latest-available data to predict gross domestic product (GDP). We employ mixed-frequency models and real-time data to reassess the role of survey data relative to industrial production and orders in Germany. Although we find evidence that forecast characteristics based on real-time and final data releases differ, we also observe minimal impacts on the relative forecasting performance of indicator models. However, when obtaining the optimal combination of soft and hard data, the use of final release data may understate the role of survey information.
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EFN Report Winter 2014/15: Economic Outlook for the Euro Area in 2015 and 2016

EFN Reports, No. 1, 2015
Abstract
At the beginning of the year 2015 crude oil is, with little more than 50 dollars per barrel (Brent), about 50% cheaper than in the summer 2014. The oil price hike is a stimulus for the whole world economy, even though the gains of energy users are matched by losses of the suppliers: oil producing countries frequently run high current account surpluses vis-à-vis the rest of the world. It is therefore plausible to assume that, reacting to the redistribution of incomes, the oil user countries will increase their spending by more than what will be lost in spending by producers. Investment activity in the euro area has recently been particularly weak. Remarkably, investment in Greece, Portugal, and Spain, i.e. in countries that had to accept international support during the euro crisis, has strengthened. The setback took place in the so-called core countries, including Germany. Consumption growth was also subdued. Lower oil prices, by increasing real incomes of consumers and lowering production costs for firms, should increase private consumption and, in particular, investment. Moreover, the depreciation of the euro will favour exports. Overall, according to our forecasts, the euro area GDP will grow by 1.3% in 2015 and by 1.6% in 2016. GDP growth is about 0.3 percentage points higher due to the impact of lower oil prices in 2015. However, these forecasts are subject to substantial uncertainty, and we do not see any significant reduction of the unemployment rate during the forecasting horizon. Our inflation forecast for 2015 is 0.3%, with the possibility of a mild deflation not excluded. In 2016 inflation will increase up to 1.1%, still clearly below the 2% ECB’s target. This calls for further monetary expansion, though its effects could be limited in the absence of a complementary fiscal stimulus. The expansionary cyclical policies should be combined with more structural reforms to enhance the competitiveness of the euro area and its growth potential, which in our forecasts remains quite limited. In fact, we expect potential output to grow by only 0.4% both in 2015 and 2016.
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