Forecasting U.S. State-Level Carbon Dioxide Emissions

James Burnett and Xueting Zhao ()

The Review of Regional Studies, 2014, vol. 44, issue 3, 223-240

Abstract: This study explores the use of spatial models in forecasting U.S. state-level carbon dioxide emissions. We compare forecasts against empirical reality using panel data models with and without spatial effects. Understanding how to predict emissions is important for designing climate change mitigation policies. To determine if spatial econometric models can help us predict emissions, it is important to test these models to see if they are a valid strategy to describe the underlying data, in the context of forecasting. We find that a non-spatial OLS estimator performs best in all out-of-sample forecasts; however, the OLS model is not statistically distinguishable from a spatial panel data model with random effects.

Keywords: spatial panel data econometrics; forecasting; carbon dioxide emissions (search for similar items in EconPapers)
JEL-codes: C33 C53 Q50 (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:rre:publsh:v44:y:2014:i:3:p:223-240

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