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Applications of Simulation Methods in Environmental and Resource Economics 2005 Edition
Contributor(s): Scarpa, Riccardo (Editor), Alberini, Anna (Editor)

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 ISBN: 1402036833     ISBN-13: 9781402036835
Publisher: Springer
OUR PRICE: $161.49  

Binding Type: Hardcover - See All Available Formats & Editions
Published: August 2005
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Annotation: Simulation methods are revolutionizing the practice of applied economic analysis. This volume collects eighteen chapters written by leading researchers from prestigious research institutions the world over. The common denominator of the papers is their relevance for applied research in environmental and resource economics.

The topics range from discrete choice modeling with heterogeneity of preferences, to Bayesian estimation, to Monte Carlo experiments, to structural estimation of Kuhn-Tucker demand systems, to evaluation of simulation noise in maximum simulated likelihood estimates, to dynamic natural resource modeling. Empirical cases are used to show the practical use and the results brought forth by the different methods.

Click for more in this series: Economics of Non-Market Goods and Resources
Additional Information
BISAC Categories:
- Business & Economics | Econometrics
- Science | Environmental Science (see Also Chemistry - Environmental)
- Business & Economics | Economics - Macroeconomics
Dewey: 339.49
LCCN: 2006615273
Series: Economics of Non-Market Goods and Resources
Physical Information: 1" H x 6.14" W x 9.21" L (1.76 lbs) 410 pages
Features: Bibliography, Illustrated, Index
 
Descriptions, Reviews, Etc.
Publisher Description:

Simulation methods are revolutionizing the practice of applied economic analysis. This volume collects eighteen chapters written by leading researchers from prestigious research institutions the world over. The common denominator of the papers is their relevance for applied research in environmental and resource economics.

The topics range from discrete choice modeling with heterogeneity of preferences, to Bayesian estimation, to Monte Carlo experiments, to structural estimation of Kuhn-Tucker demand systems, to evaluation of simulation noise in maximum simulated likelihood estimates, to dynamic natural resource modeling. Empirical cases are used to show the practical use and the results brought forth by the different methods.
 
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