Mixed frequency machine learning forecasting of the growth of real gross fixed capital formation in the United States: the role of extreme weather conditions

We forecast the 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 sample period of 1974:Q1 to 2022:Q1. Our results show that incorporating monthly data on severe climatic conditions, especially the information contained in relatively high (above-the-mean) extreme weather values, significantly outperforms not only the benchmark autoregressive model, but also the econometric framework that includes the macro-financial factors when forecasting the growth rate of quarterly real gross fixed capital formation.