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(Showing results 1 to 5 of 5)



Explaining the Variation in Elasticity Estimates of Gasoline Demand in the United States: A Meta-Analysis

Molly Espey

Year: 1996
Volume: Volume17
Number: Number 3
DOI: 10.5547/ISSN0195-6574-EJ-Vol17-No3-4
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Abstract:
Meta-analysis is used to determine if there are factors that systematically affect price and income elasticity estimates in studies of gasoline demand in the United States. Elasticity estimates from previous studies are used as the dependent variable with data characteristics, model structure, and estimation technique as the independent variables. Included among the explanatory variables a rejunctional form, lag structure, time span, and national setting (U.S. versus the U.S. pooled with other countries). Inclusion of vehicle ownership in gasoline demand studies is found to result in lower estimates of income elasticity, data sets which pool U.S. and foreign data result in larger (absolute) estimates of both price and income elasticity, and the small difference between static and dynamic models suggests that lagged responses to price or income changes are relatively short. This study also found that elasticity estimates appear relatively robust across estimation techniques.



Carbon Abatement Costs: Why the Wide Range of Estimates?

Carolyn Fischer and Richard D. Morgenstern

Year: 2006
Volume: Volume 27
Number: Number 2
DOI: 10.5547/ISSN0195-6574-EJ-Vol27-No2-5
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Abstract:
Estimates of marginal abatement costs for reducing carbon emissions derived from major economic-energy models vary widely. Controlling for policy regimes we use meta-analysis to examine the importance of structural modeling choices in explaining differences in estimates. The analysis indicates that particular assumptions about perfectly foresighted consumers and Armington trade elasticities generate lower estimates of marginal abatement costs. Other choices are associated with higher cost estimates, including perfectly mobile capital, inclusion of a backstop technology, and greater disaggregation among regions and sectors. Some features, such as greater technological detail, seem less significant. Understanding the importance of key modeling assumptions, as well as the way the models are used to estimate abatement costs, can help guide the development of consistent modeling practices for policy evaluation.



A Meta-Analysis of the Economic Impacts of Climate Change Policy in the United States

Adam Rose and Noah Dormady

Year: 2011
Volume: Volume 32
Number: Number 2
DOI: 10.5547/ISSN0195-6574-EJ-Vol32-No2-6
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Abstract:
This paper provides a meta-analysis of a broad set of recent studies of the economic impacts of climate change mitigation policies. It evaluates the influences of the impacts of causal factors, key economic assumptions and macroeconomic linkages on the outcome of these studies. A quantile regression analysis is also performed on the meta sample, to evaluate the robustness of those key factors throughout the full range of macro findings. Results of these analyses suggest that study results are strongly driven by data inputs, economic assumptions and modeling approaches. However, they are sometimes affected in counterintuitive ways.



Is There Really Granger Causality Between Energy Use and Output?

Stephan B. Bruns, Christian Gross and David I. Stern

Year: 2014
Volume: Volume 35
Number: Number 4
DOI: 10.5547/01956574.35.4.5
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Abstract:
We carry out a meta-analysis of the very large literature on testing for Granger causality between energy use and economic output to determine if there is a genuine effect in this literature or whether the large number of apparently significant results is due to publication or misspecification bias. Our model extends the standard meta-regression model for detecting genuine effects in the presence of publication biases using the statistical power trace by controlling for the tendency to over-fit vector autoregression models in small samples. Granger causality tests in these over-fitted models have inflated type I errors. We cannot find a genuine causal effect in the literature as a whole. However, there is a robust genuine effect from output to energy use when energy prices are controlled for.



Does Daylight Saving Save Electricity? A Meta-Analysis

Tomas Havranek, Dominik Herman, and Zuzana Irsova

Year: 2018
Volume: Volume 39
Number: Number 2
DOI: 10.5547/01956574.39.2.thav
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Abstract:
The original rationale for adopting daylight saving time (DST) was energy savings. Modern research studies, however, question the magnitude and even direction of the effect of DST on electricity consumption. Representing the first meta-analysis in this literature, we collect 162 estimates from 44 studies and find that the mean reported estimate indicates slight electricity savings: 0.34% during the days when DST applies. The literature is not affected by publication bias, but the results vary systematically depending on the exact data and methodology applied. Using Bayesian model averaging we identify the most important factors driving the heterogeneity of the reported effects: data frequency, estimation technique (simulation vs. regression), and, importantly, the latitude of the country considered. Electricity savings are larger for countries farther away from the equator, while subtropical regions consume more electricity because of DST.





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