Abstract
Conditional forecasts of US economic and energy sector activity are developed using information from a dynamic, data-rich environment. The forecasts are conditional on a path for carbon dioxide emissions outlined in the US Environmental Protection Agency’s Clean Power Plan (CPP) and are estimated based on a factor-augmented autoregressive framework. Results suggest that overall growth will be slower under the CPP than it would otherwise; however, economic growth and CO2 reductions can be achieved simultaneously. There are little differences between unconditional (business-as-usual) and conditional forecasts of the variables in the early part of the forecast period; the impacts of the CPP are small while the constraints on carbon dioxide are less stringent. The results serve as a data-driven complement to structural analyses of policy change in the energy sector.
Published Version
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