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| Wolff, Hendrik; Chong, Howard; Auffhammer, Maximilian. |
| This paper examines the consequences of data error in data series used to construct aggregate indicators. Using the most popular indicator of country level economic development, the Human Development Index (HDI), we identify three separate sources of data error. We propose a simple statistical framework to investigate how data error may bias rank assignments and identify two striking consequences for the HDI. First, using the cutoff values used by the United Nations to assign a country as 'low', 'medium', or 'high' developed, we find that currently up to 45% of developing countries are misclassified. Moreover, by replicating prior development/macroeconomic studies, we find that key estimated parameters such as Gini coefficients and speed of convergence... |
| Tipo: Working or Discussion Paper |
Palavras-chave: Measurement Error; International Comparative Statistics; Research Methods/ Statistical Methods; O10; C82. |
| Ano: 2008 |
URL: http://purl.umn.edu/6502 |
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| Wolff, Hendrik; Chong, Howard; Auffhammer, Maximilian. |
| This paper examines the consequences of data error in data series used to construct aggregate indicators. Using the most popular indicator of country level economic development, the Human Development Index (HDI), we identify three separate sources of data error. We propose a simple statistical framework to investigate how data error may bias rank assignments and identify two striking consequences for the HDI. First, using the cutoff values used by the United Nations to assign a country as ‘low’, ‘medium’, or ‘high’ developed, we find that currently up to 45% of developing countries are misclassified. Moreover, by replicating prior development/macroeconomic studies, we find that key estimated parameters such as Gini coefficients and speed of convergence... |
| Tipo: Working or Discussion Paper |
Palavras-chave: Measurement Error; International Comparative Statistics; International Development; O10; C82. |
| Ano: 2009 |
URL: http://purl.umn.edu/49763 |
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| Auffhammer, Maximilian; Steinhauser, Ralf. |
| This paper provides comparisons of a 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 decreasing per... |
| Tipo: Working or Discussion Paper |
Palavras-chave: Environmental Economics and Policy. |
| Ano: 2006 |
URL: http://purl.umn.edu/7157 |
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| Auffhammer, Maximilian; Carson, Richard T.; Garin-Munoz, Teresa. |
| Forecasts of Chinese carbon dioxide (CO2) emissions are critical to any global agreement on mitigating possible global climate change. We provide such forecasts through 2050 using a reduced form model selected using a general to simple search strategy. These estimates are the first based upon provincial-level data (1985-2000). The model chosen by the information criterion is one that melds the standard approach taken in the science and engineering literature with the environmental Kuznets curve approach popular in the economics literature whereby per capita emissions can first rise and then fall with increases in income. Other aspects of the model allow for the possibility that the rate of technological change varies across provinces and the possibility of... |
| Tipo: Working or Discussion Paper |
Palavras-chave: Environmental Economics and Policy. |
| Ano: 2004 |
URL: http://purl.umn.edu/25109 |
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| Auffhammer, Maximilian; Carson, Richard T.. |
| Our results suggest that the anticipated path of China's Carbon Dioxide (CO2) emissions has dramatically increased over the last five years. The magnitude of the projected increase in Chinese emissions out to 2015 is several times larger than reductions embodied in the Kyoto Protocol. Our estimates are based on a unique provincial level panel data set from the Chinese Environmental Protection Agency. This dataset contains considerably more information relevant to the path of likely Chinese greenhouse gas emissions than national level time series models currently in use. Model selection criteria clearly reject the popular static environmental Kuznets curve specification in favor of a class of dynamic models with spatial dependence. |
| Tipo: Working or Discussion Paper |
Palavras-chave: Forecasting; Climate Change; China; Model Selection; Environmental Economics and Policy; Q43; C53. |
| Ano: 2006 |
URL: http://purl.umn.edu/7197 |
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