|
| |
|
|
베이지안 추정법을 활용한 양분선택형 조건부 가치측정모형
파일 
베이지안 추정법을 활용한 양분선택형 조건부 가치측정모형.hwp
[Size :
64 Kbyte
]
베이지안 추정법을 활용한 양분선택형 조건부 가치측정모형.hwp
[Size :
64 Kbyte
]
분량 13 Page
가격 1,500 원
×
링크주소가 복사 되었습니다.
원하는 곳에 붙혀넣기 하세요
원하는 곳에 붙혀넣기 하세요
카트
|
다운받기
|
 네이버 ID로 다운 받기  카카오 ID로 다운 받기  구글 ID로 다운 받기  페이스북 ID로 다운 받기 |
뒤로
|
자료설명
베이지안 추정법을 활용한 양분선택형 조건부 가치측정모형입니다.
up베이지안추정법을활용한양분선택형조건부가치측정모형
up베이지안추정법을활용한양분선택형조건부가치측정모형
목차/차례
- []
- Ⅰ. 서론
- Ⅱ. DC-CVM 모형
- 1. 고전적 ML 모형
- 2. 베이지안 접근법에 대한 개요
- 3. DC-CVM 모형에 대한 베이지안 분석
- Ⅲ. 실증사례연구
- 1. 자료
- 2. 추정결과
- 3. 후생의 추정
- 4. 후생의 추정에 대한 추가적인 논의
- Ⅳ. 결론
- [참고문헌]
본문/내용
후생의 추정치를 만들어내는 데 있어서, 본 연구에서 제안한 베이지안 추정법은 고전적인 ML 추정법에 비해 두 가지 중요한 장점을 가지고 있다. 이것은 베이지안 추정법이 모든 모수와 관심대상 모수들의 선형 또는 비선형 조합에 대해 사후적 분포를 만들어낼 수 있는 바람직한 성질을 가지고 있기 때문이다.
첫 번째 장점은 후생 추정치의 신뢰구간을 구하는 것과 관계가 있다. CVM을 이용하는 연구자는 앞에서 설명한 Krinsky and Robb의 몬테칼로 모의실험과 같이 항상 결합정규분포로부터 샘플링하는 방법을 이용해야 하는 어려움을 겪고 있다. 그러나, 베이지안 추정법은 추가적인 복잡한 모의실험을 할 필요가 없이 샘플링 알고리즘의 부산물로써 신뢰구간을 쉽게 만들어 낼 수 있다. 즉, GS로부터 도출된 각각의 에 대해 5,000개의 평균값 WTP와 5,000개의 중앙값 WTP를 단순하게 계산하여 이 값들의 실증적 분포의 좌우에서 각각 5.0%, 2.5%를 제거함으로써 90% 및 95% 신뢰구간을 계산할 수 있다.
첫 번째 장점은 후생 추정치의 신뢰구간을 구하는 것과 관계가 있다. CVM을 이용하는 연구자는 앞에서 설명한 Krinsky and Robb의 몬테칼로 모의실험과 같이 항상 결합정규분포로부터 샘플링하는 방법을 이용해야 하는 어려움을 겪고 있다. 그러나, 베이지안 추정법은 추가적인 복잡한 모의실험을 할 필요가 없이 샘플링 알고리즘의 부산물로써 신뢰구간을 쉽게 만들어 낼 수 있다. 즉, GS로부터 도출된 각각의 에 대해 5,000개의 평균값 WTP와 5,000개의 중앙값 WTP를 단순하게 계산하여 이 값들의 실증적 분포의 좌우에서 각각 5.0%, 2.5%를 제거함으로써 90% 및 95% 신뢰구간을 계산할 수 있다.
참고문헌
[참고문헌]
1. Adamowicz, W. L., T. Graham-Tomasi and J. J. Fletcher, `Inequality constrained estimation of consumer surplus,` Canadian Journal of Agricultural Economics, Vol. 37, 1989, pp. 407-420.
2. Albert, J. H. and S. Chib, “Bayesian Analysis of Binary and Polychotomous Response Data,” Journal of American Statistical Association, Vol. 88, 1993, pp. 669-679.
3. Arrow, K., R. Solow, P. R. Portney, E. E. Leamer, R. Radner and H. Schuman, “Report of the NOAA Panel on Contingent Valuation,” Federal Register, Vol. 58, 1993, pp. 4601-4614.
4. Bishop, R. C. and T. A. Heberlein, “Measuring Values of Extra-Market Goods: Are Indirect Measures Biased?,” American Journal of Agricultural Economics, 61, 1979, pp. 926-930.
5. Brown, T. C., P. A. Champ, R. C. Bishop and D. W. McCollum, “Which Response Format Reveals the Truth about Donations to a Public Good?,” Land Economics, Vol. 72, 1996, pp. 152-166.
6. Cameron, T. A., “A New Paradigm for Valuing Non-market Goods Using Referendum Data: Maximum Likelihood Estimation by Censored Logistic Regression,” Journal of Environmental Economics and Management, Vol. 15, 1988, pp. 355-379.
7. Cameron, T. A. and D. James, “Efficient Estimation Methods for ‘Closed-Ended’ Contingent Valuation Surveys,” Review of Economics and Statistics, Vol. 69, 1987, pp. 269-276.
8. Casella, G. and E. I. George, “Explaining the Gibbs Sampler,” American Statistician, Vol. 46, 1992, pp. 167-174.
9. Chib, S., “Bayes Inference in the Tobit Censored Regression Model,” Journal of Econometrics, Vol. 51, 1992, pp. 79-99.
10. Devroye L., Non-Uniform Random Variate Generation, New York, Springer-Verlag, 1986.
11. Gelfand, A. E. and A. F. M Smith, “Sampling-Based Approaches to Calculating Marginal Densities,” Journal of American Statistical Association, Vol. 85, 1990, pp. 398-409.
12. Gelfand, A. E., E. H. Susan, R. -P. Amy and A. F. M Smith, “Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling,” Journal of American Statistical Association, Vol. 85, 1990, pp. 972-985.
13. Griffiths, W. E., R. C. Hill and P. J. Pope, “Small Sample Properties of Probit Model Estimators,” Journal of American Statistical Association, Vol. 82, 1987, pp. 929-937.
14. Haab, T. C. and K. E. McConnell, “Referendum Models and Economic Values: Theoretical, Intuitive, and Practical Bounds on Willingness to Pay,” Land Economics, Vol. 74, 1998, pp. 216-229.
15. Hanemann, W. M. and B. Kanninen, “The Statistical Analysis of Discrete-Response CV Data,” in I. J. Bateman and K. E. Willis, ed., Valuing Environmental Preferences: Theory and Practice of the Contingent Valuation Method in the US, EU, and Developing Countries. Oxford: Oxford University Press, 1999.
16. Hanemann, W. M., “Welfare Evaluations in Contingent Valuation Experiments with Discrete Responses,” American Journal of Agricultural Economics, Vol. 66, 1984, pp. 332-341.
17. Hanemann, W. M., “Welfare Evaluations in Contingent Valuation Experiments with Discrete Responses: Reply,” American Journal of Agricultural Economics, Vol. 71, 1989, pp. 1057-1061.
18. Judge, G. G., R. C. Hill, W. E. Griffiths, H. Lutkepohl and T. C. Lee, Introduction to the Theory and Practice of Econometrics. New York: John Wiley & Sons, 1982.
19. Kim, C. -J. and C. R. Nelson, State-Space Models with Regime Switching: Classical and Gibbs Sampling Approaches with Applications. Cambridge: The MIT Press, 1999.
20. Krinsky, I. and A. L. Robb, “On Approximating the Statistical Properties of Elasticities,” Review of Economics and Statistics, Vol. 68, 1986, pp. 715-719.
21. Len, C. J. and F. J. Vzquez-Polo, “A Bayesian Approach to Double Bounded Contingent Valuation”, Environmental and Resource Economics, Vol. 11, 1998, pp. 197-215.
22. LeSage, J. P., “Bayesian Estimation of Spatial Autoregressive Models,” International Regional Science Review, Vol. 20, 1997, pp. 113-129.
23. McConnell, K. E., “Models for Referendum Data: the Structure of Discrete Choice Models for Contingent Valuation,” Journal of Environmental Economics and Management, Vol. 18, 1990, pp. 19-34.
24. McCulloch, R. and P. E. Rosi, “An Exact Likelihood Analysis of the Multinomial Probit Model,” Journal of Econometrics, Vol. 64, 1994, pp. 207-240.
25. McLeod, D. M. and O. Bergland, “Willingness-to-Pay Estimates Using the Double-Bounded Dichotomous-Choice Contingent Valuation Format: A Test for Validity and Precision in a Bayesian Framework,” Land Economics, Vol. 75, 1999, pp. 115-125.
26. Mitchell, R.C. and R. T. Carson, Using Surveys to Public Goods: The Contingent Valuation Method. Washington, DC: Resources for the Future, 1989.
27. Park, T., J. B. Loomis and M. Creel, “Confidence Intervals for Evaluating Benefits Estimates from Dichotomous Choice Contingent Valuation Studies,” Land Economics, Vol. 67, 1991, pp. 64-73.
28. Ready, R. C. and D. Hu, “Statistical Approaches to the Fat Tail Problem for Dichotomous Choice Contingent Valuation,” Land Economics, Vol. 71, 1995, pp. 491-499.
29. Tanner, T. A. and W. H. Wong, “The Calculation of Posterior Distributions by Data Augmentation,” Journal of American Statistical Association, Vol. 82, 1987, pp. 528-549.
30. Willig, R. D., “Consumer Surplus without Apology,” American Economic Review, Vol. 66, 1976, pp. 587-597.
31. Yoo, S. -H. and K. -S. Chae, “Measuring the Economic Benefits of the Ozone Pollution Control Policy in Seoul: Results of a Contingent Valuation Survey,” Urban Studies, Vol. 38, 2001, pp. 49-60.
32. Zellner, A. and P. E. Rossi, “Bayesian Analysis of Dichotomous Quantal Response Models,” Journal of Econometrics, Vol. 25, 1984, pp. 365-393.
33. Zellner, A., Basic Issues in Econometrics. Chicago: University of Chicago Press, 1984.
1. Adamowicz, W. L., T. Graham-Tomasi and J. J. Fletcher, `Inequality constrained estimation of consumer surplus,` Canadian Journal of Agricultural Economics, Vol. 37, 1989, pp. 407-420.
2. Albert, J. H. and S. Chib, “Bayesian Analysis of Binary and Polychotomous Response Data,” Journal of American Statistical Association, Vol. 88, 1993, pp. 669-679.
3. Arrow, K., R. Solow, P. R. Portney, E. E. Leamer, R. Radner and H. Schuman, “Report of the NOAA Panel on Contingent Valuation,” Federal Register, Vol. 58, 1993, pp. 4601-4614.
4. Bishop, R. C. and T. A. Heberlein, “Measuring Values of Extra-Market Goods: Are Indirect Measures Biased?,” American Journal of Agricultural Economics, 61, 1979, pp. 926-930.
5. Brown, T. C., P. A. Champ, R. C. Bishop and D. W. McCollum, “Which Response Format Reveals the Truth about Donations to a Public Good?,” Land Economics, Vol. 72, 1996, pp. 152-166.
6. Cameron, T. A., “A New Paradigm for Valuing Non-market Goods Using Referendum Data: Maximum Likelihood Estimation by Censored Logistic Regression,” Journal of Environmental Economics and Management, Vol. 15, 1988, pp. 355-379.
7. Cameron, T. A. and D. James, “Efficient Estimation Methods for ‘Closed-Ended’ Contingent Valuation Surveys,” Review of Economics and Statistics, Vol. 69, 1987, pp. 269-276.
8. Casella, G. and E. I. George, “Explaining the Gibbs Sampler,” American Statistician, Vol. 46, 1992, pp. 167-174.
9. Chib, S., “Bayes Inference in the Tobit Censored Regression Model,” Journal of Econometrics, Vol. 51, 1992, pp. 79-99.
10. Devroye L., Non-Uniform Random Variate Generation, New York, Springer-Verlag, 1986.
11. Gelfand, A. E. and A. F. M Smith, “Sampling-Based Approaches to Calculating Marginal Densities,” Journal of American Statistical Association, Vol. 85, 1990, pp. 398-409.
12. Gelfand, A. E., E. H. Susan, R. -P. Amy and A. F. M Smith, “Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling,” Journal of American Statistical Association, Vol. 85, 1990, pp. 972-985.
13. Griffiths, W. E., R. C. Hill and P. J. Pope, “Small Sample Properties of Probit Model Estimators,” Journal of American Statistical Association, Vol. 82, 1987, pp. 929-937.
14. Haab, T. C. and K. E. McConnell, “Referendum Models and Economic Values: Theoretical, Intuitive, and Practical Bounds on Willingness to Pay,” Land Economics, Vol. 74, 1998, pp. 216-229.
15. Hanemann, W. M. and B. Kanninen, “The Statistical Analysis of Discrete-Response CV Data,” in I. J. Bateman and K. E. Willis, ed., Valuing Environmental Preferences: Theory and Practice of the Contingent Valuation Method in the US, EU, and Developing Countries. Oxford: Oxford University Press, 1999.
16. Hanemann, W. M., “Welfare Evaluations in Contingent Valuation Experiments with Discrete Responses,” American Journal of Agricultural Economics, Vol. 66, 1984, pp. 332-341.
17. Hanemann, W. M., “Welfare Evaluations in Contingent Valuation Experiments with Discrete Responses: Reply,” American Journal of Agricultural Economics, Vol. 71, 1989, pp. 1057-1061.
18. Judge, G. G., R. C. Hill, W. E. Griffiths, H. Lutkepohl and T. C. Lee, Introduction to the Theory and Practice of Econometrics. New York: John Wiley & Sons, 1982.
19. Kim, C. -J. and C. R. Nelson, State-Space Models with Regime Switching: Classical and Gibbs Sampling Approaches with Applications. Cambridge: The MIT Press, 1999.
20. Krinsky, I. and A. L. Robb, “On Approximating the Statistical Properties of Elasticities,” Review of Economics and Statistics, Vol. 68, 1986, pp. 715-719.
21. Len, C. J. and F. J. Vzquez-Polo, “A Bayesian Approach to Double Bounded Contingent Valuation”, Environmental and Resource Economics, Vol. 11, 1998, pp. 197-215.
22. LeSage, J. P., “Bayesian Estimation of Spatial Autoregressive Models,” International Regional Science Review, Vol. 20, 1997, pp. 113-129.
23. McConnell, K. E., “Models for Referendum Data: the Structure of Discrete Choice Models for Contingent Valuation,” Journal of Environmental Economics and Management, Vol. 18, 1990, pp. 19-34.
24. McCulloch, R. and P. E. Rosi, “An Exact Likelihood Analysis of the Multinomial Probit Model,” Journal of Econometrics, Vol. 64, 1994, pp. 207-240.
25. McLeod, D. M. and O. Bergland, “Willingness-to-Pay Estimates Using the Double-Bounded Dichotomous-Choice Contingent Valuation Format: A Test for Validity and Precision in a Bayesian Framework,” Land Economics, Vol. 75, 1999, pp. 115-125.
26. Mitchell, R.C. and R. T. Carson, Using Surveys to Public Goods: The Contingent Valuation Method. Washington, DC: Resources for the Future, 1989.
27. Park, T., J. B. Loomis and M. Creel, “Confidence Intervals for Evaluating Benefits Estimates from Dichotomous Choice Contingent Valuation Studies,” Land Economics, Vol. 67, 1991, pp. 64-73.
28. Ready, R. C. and D. Hu, “Statistical Approaches to the Fat Tail Problem for Dichotomous Choice Contingent Valuation,” Land Economics, Vol. 71, 1995, pp. 491-499.
29. Tanner, T. A. and W. H. Wong, “The Calculation of Posterior Distributions by Data Augmentation,” Journal of American Statistical Association, Vol. 82, 1987, pp. 528-549.
30. Willig, R. D., “Consumer Surplus without Apology,” American Economic Review, Vol. 66, 1976, pp. 587-597.
31. Yoo, S. -H. and K. -S. Chae, “Measuring the Economic Benefits of the Ozone Pollution Control Policy in Seoul: Results of a Contingent Valuation Survey,” Urban Studies, Vol. 38, 2001, pp. 49-60.
32. Zellner, A. and P. E. Rossi, “Bayesian Analysis of Dichotomous Quantal Response Models,” Journal of Econometrics, Vol. 25, 1984, pp. 365-393.
33. Zellner, A., Basic Issues in Econometrics. Chicago: University of Chicago Press, 1984.
| 📝 Regist Info |
|
I D : khgy****** Date : 2014-07-28 FileNo : 16135905 |
Cart  | |
| |