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Enhancing survey‐based investment forecasts

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  • Ciaran Driver
  • Nigel Meade

Abstract

We investigate the accuracy of capital investment predictors from a national business survey of South African manufacturing. Based on data available to correspondents at the time of survey completion, we propose variables that might inform the confidence that can be attached to their predictions. Having calibrated the survey predictors' directional accuracy, we model the probability of a correct directional prediction using logistic regression with the proposed variables. For point forecasting, we compare the accuracy of rescaled survey forecasts with time series benchmarks and some survey/time series hybrid models. In addition, using the same set of variables, we model the magnitude of survey prediction errors. Directional forecast tests showed that three out of four survey predictors have value but are biased and inefficient. For shorter horizons we found that survey forecasts, enhanced by time series data, significantly improved point forecasting accuracy. For longer horizons the survey predictors were at least as accurate as alternatives. The usefulness of the more accurate of the predictors examined is enhanced by auxiliary information, namely the probability of directional accuracy and the estimated error magnitude.

Suggested Citation

  • Ciaran Driver & Nigel Meade, 2019. "Enhancing survey‐based investment forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(3), pages 236-255, April.
    • Handle: RePEc:wly:jforec:v:38:y:2019:i:3:p:236-255
    DOI: 10.1002/for.2567
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    Cited by:

    1. Ciaran Driver, 2019. "Trade liberalization and South African manufacturing: Looking back with data," WIDER Working Paper Series wp-2019-30, World Institute for Development Economic Research (UNU-WIDER).
    2. Claire Giordano & Marco Marinucci & Andrea Silvestrini, 2022. "Assessing the usefulness of survey‐based data in forecasting firms' capital formation: Evidence from Italy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 491-513, April.
    3. Kladivko, Kamil & Österholm, Pär, 2020. "Can Households Predict where the Macroeconomy is Headed?," Working Papers 2020:11, Örebro University, School of Business.
    4. Blagov, Boris & Müller, Henrik & Jentsch, Carsten & Schmidt, Torsten, 2021. "The investment narrative: Improving private investment forecasts with media data," Ruhr Economic Papers 921, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    5. Claveria, Oscar & Monte, Enric & Torra, Salvador, 2020. "Economic forecasting with evolved confidence indicators," Economic Modelling, Elsevier, vol. 93(C), pages 576-585.
    6. Oscar Claveria & Enric Monte & Salvador Torra, 2021. "“Nowcasting and forecasting GDP growth with machine-learning sentiment indicators”," AQR Working Papers 202101, University of Barcelona, Regional Quantitative Analysis Group, revised Feb 2021.

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