The outbreak of COVID-19 has caused unprecedentedly negative impacts across all industries, with manufacturing among the most suffered. At the second quarter of 2020, the US Industrial Production Index falls by 14.4% year-on-year.
The outbreak of COVID-19 has caused unprecedentedly negative impacts across all industries, with manufacturing among the most suffered. At the second quarter of 2020, the US Industrial Production Index falls by 14.4% year-on-year. Shrinkage in industrial production is likely to create a negative demand shock for the input resources, which in turn drives down the prices of commodities, especially energy and metals.
At the same time, variations in commodity prices would also affect the supply, production costs, and hence production decision. For example, the tentative agreement in April 2020 between the Organization of the Petroleum Exporting Countries (OPEC), Russia and other countries to extend oil production cuts aims to support oil price after a slump in demand caused by coronavirus lockdowns.
Nonetheless, relationships among energy and metal prices could be even more complicated, not only due to their multi-directional linkages, but also due to the existence of time lagged effects, short run and long run equilibrium. In this article, I would try to apply Vector Error Correction Model (VECM) *to (1) *investigate the relationships between commodity prices and industrial production in the United States, and (2) forecast the movement of industrial production and commodity prices in near future. For all the codes below, please refer to my Github link here.
This exercise will focus on the period between January 1990 and July 2020.
From the World Bank’s commodity markets website, we can download the monthly price data for different commodities. Among all, below are selected as representatives of energy, industrial metals and precious metals.
Energy — Crude Oil (Brent), Coal (South African), Natural gas (US)
Industrial metals — Aluminum, Iron ore, Copper
Precious metals — Platinum, Silver
As a side note, in 2019, the two traditional energy sources, e.g. petroleum and natural gas, still account for 74.0% of total energy use in the US industrial sector, which is a record high since data available in 1950. In contrast, only 9.5% in total is contributed by renewable energy, a slight decline from the peak of 10.1% in 2011. What a sad situation!!!
For the United States’ industrial production index, data can be downloaded from the website of Federal Reserve Bank.
According to the definition, the Industrial Production Index is an economic indicator that measures real output for all facilities located in the United States manufacturing, mining, and electric, and gas utilities (excluding those in U.S. territories).
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
🔵 Intellipaat Data Science with Python course: https://intellipaat.com/python-for-data-science-training/In this Data Science With Python Training video, you...
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
Become a data analysis expert using the R programming language in this [data science](https://360digitmg.com/usa/data-science-using-python-and-r-programming-in-dallas "data science") certification training in Dallas, TX. You will master data...
Need a data set to practice with? Data Science Dojo has created an archive of 32 data sets for you to use to practice and improve your skills as a data scientist.