This paper provides comparisons of a variety of time-series methods for short-run forecasts of the main greenhouse gas, carbon dioxide, for the United States, using a recently released state-level data set from 1960-2001. We test the out-of-sample performance of univariate and multivariate forecasting models by aggregating state-level forecasts versus forecasting the aggregate directly. We find evidence that forecasting the disaggregate series and accounting for spatial effects drastically improves forecasting performance under root mean squared forecast error loss. Based on the in-sample observations we attempt to explain the emergence of voluntary efforts by states to reduce greenhouse gas emissions. We find evidence that states with decr...
November 10, 2004, Comments Welcome 1 Understanding the distribution of per capita carbon dioxide em...
The key role that atmospheric carbon dioxide plays in climate warming makes it particularly relevant...
In this paper, we present four ARIMA (Autoregressive Integrated Moving Average) models for forecasti...
This paper provides comparisons of a variety of time series methods for short run forecasts of the m...
This paper provides comparisons of a variety of time-series methods for short-run forecasts of the m...
This paper tests the out of sample predictive ability of reduced form models found in the literature...
We compare the most common reduced-form models used for emissions forecasting, point out shortcoming...
Due to criticisms of potential identification issues within spatial panel data models, this study co...
In recent years, the international community has been increasing its efforts to reduce the human foo...
We characterize the evolution of U.S. carbon dioxide (CO2) emissions using an index num-ber decompos...
As the regulation of carbon dioxide emissions play an increasingly important role in fighting agains...
Past statistical modeling of carbon dioxide (CO2) emissions has primarily followed the reduced-form ...
Climate change has been realized as a major concern worldwide and people along with the government a...
One of the major criticisms of past environmental Kuznets curve (EKC) studies is that the spatiotemp...
A model of carbon dioxide emissions of the USA is presented. The model consists of population, incom...
November 10, 2004, Comments Welcome 1 Understanding the distribution of per capita carbon dioxide em...
The key role that atmospheric carbon dioxide plays in climate warming makes it particularly relevant...
In this paper, we present four ARIMA (Autoregressive Integrated Moving Average) models for forecasti...
This paper provides comparisons of a variety of time series methods for short run forecasts of the m...
This paper provides comparisons of a variety of time-series methods for short-run forecasts of the m...
This paper tests the out of sample predictive ability of reduced form models found in the literature...
We compare the most common reduced-form models used for emissions forecasting, point out shortcoming...
Due to criticisms of potential identification issues within spatial panel data models, this study co...
In recent years, the international community has been increasing its efforts to reduce the human foo...
We characterize the evolution of U.S. carbon dioxide (CO2) emissions using an index num-ber decompos...
As the regulation of carbon dioxide emissions play an increasingly important role in fighting agains...
Past statistical modeling of carbon dioxide (CO2) emissions has primarily followed the reduced-form ...
Climate change has been realized as a major concern worldwide and people along with the government a...
One of the major criticisms of past environmental Kuznets curve (EKC) studies is that the spatiotemp...
A model of carbon dioxide emissions of the USA is presented. The model consists of population, incom...
November 10, 2004, Comments Welcome 1 Understanding the distribution of per capita carbon dioxide em...
The key role that atmospheric carbon dioxide plays in climate warming makes it particularly relevant...
In this paper, we present four ARIMA (Autoregressive Integrated Moving Average) models for forecasti...