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

Xin Sheng (), Oguzhan Cepni (), Rangan Gupta and Minko Markovski ()
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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
Minko Markovski: Department of Economics, University of Reading, Reading, United Kingdom

No 202520, Working Papers from University of Pretoria, Department of Economics

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.

Keywords: Gross fixed capital formation; Extreme weather conditions; Mixed frequency; Machine learning; Forecasting (search for similar items in EconPapers)
JEL-codes: C22 C53 E22 Q54 (search for similar items in EconPapers)
Pages: 13 pages
Date: 2025-05
New Economics Papers: this item is included in nep-mac
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