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Mixed Frequency Machine Learning Forecasting of the Growth of Real Gross Fixed Capital Formation in the United States: The Role of Extreme Weather Conditions

Author

Listed:
  • Xin Sheng

    (Lord Ashcroft International Business School, Anglia Ruskin University, Chelmsford, United Kingdom)

  • Oguzhan Cepni

    (Ostim Technical University, Ankara, Turkiye; University of Edinburgh Business School, Centre for Business, Climate Change, and Sustainability; Department of Economics, Copenhagen Business School, Denmark)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

  • Minko Markovski

    (Department of Economics, University of Reading, Reading, United Kingdom)

Abstract

We forecast quarterly growth rate of real gross fixed capital formation of the United States using the information content of a monthly metric of extreme weather conditions, while controlling for a set of principal components derived from a large data set of economic and financial indicators. In this regard, we utilize a Mixed Frequency Machine Learning framework over the period of 1974:Q1 to 2022:Q1. Our results show that incorporating monthly data on severe climatic conditions significantly, especially information contained in relatively higher (above the mean) extreme weather values, outperforms not only the benchmark autoregressive model, but also the econometric framework that includes the macro-finance factors when forecasting the growth rate of quarterly real gross fixed capital formation.

Suggested Citation

  • Xin Sheng & Oguzhan Cepni & Rangan Gupta & Minko Markovski, 2025. "Mixed Frequency Machine Learning Forecasting of the Growth of Real Gross Fixed Capital Formation in the United States: The Role of Extreme Weather Conditions," Working Papers 202520, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202520
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    More about this item

    Keywords

    Gross fixed capital formation; Extreme weather conditions; Mixed frequency; Machine learning; Forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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