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Stated Preference Methods Using R [Kõva köide]

(Hokkaido University, Japan), (Rakuno Gakuen University, Hokkaido, Japan), (Hokkaido University, Japan)
  • Formaat: Hardback, 265 pages, kõrgus x laius: 234x156 mm, kaal: 498 g, 8 Tables, black and white; 41 Illustrations, black and white
  • Sari: Chapman & Hall/CRC The R Series
  • Ilmumisaeg: 15-Aug-2014
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 1439890471
  • ISBN-13: 9781439890479
Teised raamatud teemal:
Stated Preference Methods Using RSuurem pilt
  • Formaat: Hardback, 265 pages, kõrgus x laius: 234x156 mm, kaal: 498 g, 8 Tables, black and white; 41 Illustrations, black and white
  • Sari: Chapman & Hall/CRC The R Series
  • Ilmumisaeg: 15-Aug-2014
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 1439890471
  • ISBN-13: 9781439890479
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781439890479.html
Märksõnad:
Stated Preference Methods Using R explains how to use stated preference (SP) methods, which are a family of survey methods, to measure peoples preferences based on decision making in hypothetical choice situations. Along with giving introductory explanations of the methods, the book collates information on existing R functions and packages as well as those prepared by the authors. It focuses on core SP methods, including contingent valuation (CV), discrete choice experiments (DCEs), and bestworst scaling (BWS).

Several example data sets illustrate empirical applications of each method with R. Examples of CV draw on data from well-known environmental valuation studies, such as the Exxon Valdez oil spill in Alaska. To explain DCEs, the authors use synthetic data sets related to food marketing and environmental valuation. The examples illustrating BWS address valuing agro-environmental and food issues. All the example data sets and code are available on the authors website, CRAN, and R-Forge, allowing readers to easily reproduce working examples.

Although the examples focus on agricultural and environmental economics, they provide beginners with a good foundation to apply SP methods in other fields. Statisticians, empirical researchers, and advanced students can use the book to conduct applied research of SP methods in economics and market research. The book is also suitable as a primary text or supplemental reading in an introductory-level, hands-on course.

Arvustused

"This is a very useful introduction to the econometrics of stated preference methods. A very significant strength of the book is the use of R. I have been teaching this type of course for many years without the benefit of this book. I wish I had it many years ago." Bengt Kriström, Professor and Chair, Department of Forest Economics, Swedish University of Agricultural Sciences (SLU), and Research Director, Centre for Environmental and Resource Economics (CERE)

"It is wonderful to finally see a book on how to use R to estimate the welfare measures commonly used in nonmarket valuation studies. The authors provide a set of R functions for some of the procedures most commonly used with stated preference data. Just as important, like almost all R functions, the user can see how these functions were coded as a way of understanding how they work and how new functions can be created. Using R opens up a very wide range of statistical procedures and visualization tools that will allow researchers to look at their stated preference data in new ways." Richard T. Carson, Professor, Department of Economics, University of California, San Diego

"There are a number of very expensive statistical packages that can be used to analyze stated preference data. R is free so it is wonderful for teaching undergraduate students and others who dont have access to expensive packages. Aizaki, Nakatani, and Sato have provided a valuable reference book for stated preference researchers and teachers who want to use R. I used the book to install R and the contingent valuation package in a few minutes. I was estimating CVM models soon afterwards. Im especially excited that the package contains an easy-to-use nonparametric willingness to pay estimator that is superior to the spreadsheet methods Ive been using for years. The package includes very well-known (Exxon Valdez) and less well-known (Albemarle-Pamlico) contingent valuation data. These data allow the user to play around with the package and compare results to what has been published in the literature. This book is an ideal reference for advanced undergraduate and graduate courses in environmental valuation." John C. Whitehead, Professor, Department of Economics, Appalachian State University

List of Figures xi
List of Tables xiii
Preface xv
Authors xvii
1 Introduction 1(18)
1.1 Stated preference methods and the role of R
1(3)
1.2 Objective of this book
4(2)
1.3 Overview of CV, DCEs, and BWS
6(4)
1.3.1 Contingent valuation
6(1)
1.3.2 Discrete choice experiments
7(1)
1.3.3 Best-worst scaling
8(2)
1.4 Random utility theory and discrete choice models
10(5)
1.4.1 Random utility theory
10(1)
1.4.2 Discrete choice models
11(4)
1.5 Summary of the rest of this book
15(4)
2 Contingent Valuation 19(50)
2.1 Introduction
19(2)
2.2 Overview of contingent valuation
21(11)
2.2.1 Elicitation formats
21(1)
2.2.2 Bid design
22(1)
2.2.3 Parametric estimation method
23(6)
2.2.4 Nonparametric estimation method
29(3)
2.3 An R package for analyzing SBDC and DBDC CV data
32(7)
2.3.1 Overview of package DCchoice
32(1)
2.3.2 Installing the DCchoice package
33(1)
2.3.3 Loading the package
33(1)
2.3.4 Preparing datasets
34(1)
2.3.5 Example datasets
35(4)
2.4 Parametric estimation of WTP
39(13)
2.4.1 Estimating WTP with SBDC data
39(5)
2.4.2 Estimating WTP with DBDC data
44(6)
2.4.3 Constructing confidence intervals
50(2)
2.5 Nonparametric estimation of WTP
52(12)
2.5.1 Kristrom's nonparametric estimation of SBDC data
52(7)
2.5.2 Kaplan-Meier-Turnbull estimation of SBDC data
59(2)
2.5.3 Kaplan-Meier-Turnbull estimation of DBDC data
61(3)
2.6 Concluding remarks
64(1)
2.A Appendix
65(4)
3 Discrete Choice Experiments 69(64)
3.1 Introduction
69(2)
3.2 Overview of DCEs
71(9)
3.2.1 Basic terms used in DCEs
71(4)
3.2.2 Steps for implementing DCEs
75(5)
3.3 R functions for DCEs
80(20)
3.3.1 Overview
80(1)
3.3.2 Creating a DCE design
81(4)
3.3.3 Converting a DCE design into questionnaire format
85(2)
3.3.4 Creating a design matrix
87(3)
3.3.5 Creating a dataset
90(4)
3.3.6 Conducting statistical analysis
94(2)
3.3.7 Calculating goodness-of-fit measures
96(1)
3.3.8 Calculating MWTPs
97(2)
3.3.9 Testing the difference between two independent MWTP distributions
99(1)
3.4 Example DCEs using R
100(30)
3.4.1 Unlabeled DCE example
101(7)
3.4.2 Labeled design example
108(9)
3.4.3 BDCE example
117(13)
3.5 Concluding remarks
130(3)
4 Best-Worst Scaling 133(44)
4.1 Introduction
133(2)
4.2 Outline of BWS
135(9)
4.2.1 BWS basics
135(2)
4.2.2 Steps in BWS
137(7)
4.3 R functions for BWS
144(13)
4.3.1 Overview
144(1)
4.3.2 Constructing choice sets
144(3)
4.3.3 Preparing BWS questions
147(1)
4.3.4 Creating the dataset
148(6)
4.3.5 Analyzing responses using the counting approach
154(2)
4.3.6 Analyzing responses using the modeling approach
156(1)
4.4 Example BWS using R
157(15)
4.4.1 BWS based on a two-level OMED
157(8)
4.4.2 BWS based on a BIBD
165(7)
4.5 Concluding remarks
172(1)
4.A Appendix: Profile case BWS and multiprofile case BWS
172(5)
4.A.1 Profile case BWS
172(2)
4.A.2 Multiprofile case BWS
174(3)
5 Basic Operations in R 177(26)
5.1 Introduction
177(1)
5.2 Getting started with R
177(3)
5.2.1 Obtaining and installing R
177(1)
5.2.2 Starting and quitting R
178(1)
5.2.3 Using R as a calculator
178(2)
5.2.4 Changing appearance
180(1)
5.2.5 Accessing help
180(1)
5.3 Enhancing R
180(3)
5.3.1 Installing contributed add-on packages
180(2)
5.3.2 Reading source code
182(1)
5.3.3 Loading source code
182(1)
5.4 Importing and exporting data
183(2)
5.4.1 File formats
183(1)
5.4.2 Importing data from a CSV file
183(1)
5.4.3 Exporting R objects
184(1)
5.5 Manipulating vectors and matrices
185(7)
5.5.1 Manipulating vectors
185(2)
5.5.2 Manipulating matrices
187(1)
5.5.3 Operations on indexes
188(3)
5.5.4 Random number generation
191(1)
5.6 Data and object types
192(3)
5.6.1 Data types
192(1)
5.6.2 Object types
192(1)
5.6.3 Examples
193(2)
5.7 Implementing linear regression
195(5)
5.7.1 Conducting the analysis
195(1)
5.7.2 Displaying and summarizing output
196(2)
5.7.3 Creating dummy variables
198(1)
5.7.4 Updating the model
199(1)
5.8 Drawing figures
200(3)
Appendix A Other Packages Related to This Book 203(4)
A.1 Introduction
203(1)
A.2 Contingent valuation
203(1)
A.3 Discrete choice models
204(1)
A.4 Cluster, component, and factor analysis
205(1)
A.5 Conjoint analysis
206(1)
Appendix B Examples of Contrivance in Empirical Studies 207(4)
B.1 Introduction
207(1)
B.2 Providing information to respondents
207(1)
B.3 Using product/service samples
208(1)
B.4 Cost-benefit analysis and valuation
209(1)
B.5 Using SP study results in simulations
210(1)
Bibliography 211(24)
Index 235
Hideo Aizaki, Tomoaki Nakatani, Kazuo Sato