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Forecasting industrial production in Germany: The predictive power of leading indicators

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  • Schlösser, Alexander

Abstract

We investigate the predictive power of several leading indicators in order to forecast industrial production in Germany. In addition, we compare their predictive performance with variables from two competing categories, namely macroeconomic and financial variables. The predictive power within and between these three categories is evaluated by applying Dynamic Model Averaging (DMA) which allows for timevarying coefficients and model change. We find that leading indicators have the largest predictive power. Macroeconomic variables, in contrast, are weak predictors as they are even not able to outperform a benchmark AR model, while financial variables are clearly inferior in terms of their predictive power compared to leading indicators. We show that the best set of predictors, within and between categories, changes over time and depends on the forecast horizon. Furthermore, allowing for time-varying model size is especially crucial after the Great Recession.

Suggested Citation

  • Schlösser, Alexander, 2020. "Forecasting industrial production in Germany: The predictive power of leading indicators," Ruhr Economic Papers 838, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    • Handle: RePEc:zbw:rwirep:838
    DOI: 10.4419/86788971
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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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