Applications of Simulation Methods in Environmental and Resource Economics

Anna Alberini

ISBN 10: 1402036833 / ISBN 13: 9781402036835

Published by Springer Netherlands, Springer Netherlands Aug 2005, 2005

About this Item

Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 448 pp. Englisch.

Seller Inventory # 9781402036835

Bibliographic Details

• Title: Applications of Simulation Methods in Environmental and Resource Economics
• Publisher: Springer Netherlands, Springer Netherlands Aug 2005
• Publication Date: 2005
• Language: English
• ISBN 10: 1402036833
• ISBN 13: 9781402036835
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 29D millimeters 241 millimeters width by 160 millimeters height by 29 millimeters depth
• Weight: 834 grams

About this title

Synopsis

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.