IDEAS home Printed from https://ideas.repec.org/a/eee/jpolmo/v33y2011i4p618-635.html
   My bibliography  Save this article

Nonparametric modelling of biodiversity: Determinants of threatened species

Author

Listed:
  • Halkos, George E.

Abstract

This study uses a sample of 71 countries and nonparametric quantile and partial regressions to model a number of threatened species (reptiles, mammals, fish, birds, trees, plants) in relation to various economic and environmental variables (GDPc, CO2 emissions, agricultural production, energy intensity, protected areas, population and income inequality). From the analysis and due to high asymmetric distribution of the dependent variables it seems that a linear regression is not adequate and cannot capture properly the dimension of the threatened species. We find that using OLS instead of non-parametric techniques over- or under-estimates the parameters which may have serious policy implications.

Suggested Citation

  • Halkos, George E., 2011. "Nonparametric modelling of biodiversity: Determinants of threatened species," Journal of Policy Modeling, Elsevier, vol. 33(4), pages 618-635, July.
  • Handle: RePEc:eee:jpolmo:v:33:y:2011:i:4:p:618-635
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0161893810001195
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. R. K. Turner & Kenneth Button & Peter Nijkamp (ed.), 1999. "Ecosystems and Nature," Books, Edward Elgar Publishing, number 1518.
    2. Moshe Buchinsky, 1998. "Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 88-126.
    3. R. J. Scholes & R. Biggs, 2005. "A biodiversity intactness index," Nature, Nature, vol. 434(7029), pages 45-49, March.
    4. Costanza, Robert & Fisher, Brendan & Mulder, Kenneth & Liu, Shuang & Christopher, Treg, 2007. "Biodiversity and ecosystem services: A multi-scale empirical study of the relationship between species richness and net primary production," Ecological Economics, Elsevier, vol. 61(2-3), pages 478-491, March.
    5. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731.
    6. Rolf Groeneveld & Carla Grashof-Bokdam & Ekko van Ierland, 2005. "Metapopulations in Agricultural Landscapes: A Spatially Explicit Trade-off Analysis," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 48(4), pages 527-547.
    7. Gregory M Mikkelson & Andrew Gonzalez & Garry D Peterson, 2007. "Economic Inequality Predicts Biodiversity Loss," PLOS ONE, Public Library of Science, vol. 2(5), pages 1-5, May.
    8. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    9. Koenker, Roger & Bassett, Gilbert, Jr, 1982. "Robust Tests for Heteroscedasticity Based on Regression Quantiles," Econometrica, Econometric Society, vol. 50(1), pages 43-61, January.
    10. Holden, Stein T. & Shiferaw, Bekele & Wik, Mette, 1998. "Poverty, market imperfections and time preferences: of relevance for environmental policy?," Environment and Development Economics, Cambridge University Press, vol. 3(1), pages 105-130, February.
    11. Cao, Shixiong & Wang, Xiuqing, 2010. "Unsustainably low birth rates: A potential crisis leading to loss of racial and cultural diversity in China," Journal of Policy Modeling, Elsevier, vol. 32(1), pages 159-162, January.
    12. Moon, Sunung & Jeon, Yongil, 2009. "How valid are long-term government plans? Technological forecasting of the Korean biotechnology industry," Journal of Policy Modeling, Elsevier, vol. 31(6), pages 891-902, November.
    13. Duval, Romain & de la Maisonneuve, Christine, 2010. "Long-run growth scenarios for the world economy," Journal of Policy Modeling, Elsevier, vol. 32(1), pages 64-80, January.
    14. Samuel Brody, 2003. "Examining the Effects of Biodiversity on the Ability of Local Plans to Manage Ecological Systems," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 46(6), pages 817-837.
    15. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    16. Nunes, Paulo A. L. D. & van den Bergh, Jeroen C. J. M., 2001. "Economic valuation of biodiversity: sense or nonsense?," Ecological Economics, Elsevier, vol. 39(2), pages 203-222, November.
    17. Di Vita, Giuseppe, 2008. "Is the discount rate relevant in explaining the Environmental Kuznets Curve?," Journal of Policy Modeling, Elsevier, vol. 30(2), pages 191-207.
    18. Buchinsky, Moshe, 1995. "Estimating the asymptotic covariance matrix for quantile regression models a Monte Carlo study," Journal of Econometrics, Elsevier, vol. 68(2), pages 303-338, August.
    19. Bockermann, Andreas & Meyer, Bernd & Omann, Ines & Spangenberg, Joachim H., 2005. "Modelling sustainability: Comparing an econometric (PANTA RHEI) and a systems dynamics model (SuE)," Journal of Policy Modeling, Elsevier, vol. 27(2), pages 189-210, March.
    20. Newey, Whitney K & Powell, James L, 1987. "Asymmetric Least Squares Estimation and Testing," Econometrica, Econometric Society, vol. 55(4), pages 819-847, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Halkos, George & Gkargkavouzi, Anastasia & Matsiori, Steriani, 2018. "Teachers’ environmental knowledge and pro-environmental behavior: An application of CNS and EID scales," MPRA Paper 84505, University Library of Munich, Germany.
    2. Halkos, George & Polemis, Michael, 2018. "Does market structure trigger efficiency? Evidence for the USA before and after the financial crisis," MPRA Paper 84511, University Library of Munich, Germany.
    3. Anastasia Gkargkavouzi & George Halkos & Steriani Matsiori, 2019. "A Multi-dimensional Measure of Environmental Behavior: Exploring the Predictive Power of Connectedness to Nature, Ecological Worldview and Environmental Concern," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 143(2), pages 859-879, June.
    4. Halkos, George & Leonti, Aikaterini & Sardianou, Eleni, 2022. "Determinants of willingness to pay for entrance to urban parks: A quantile regression analysis," Economic Analysis and Policy, Elsevier, vol. 74(C), pages 421-431.
    5. Natina Yaduma & Mika Kortelainen & Ada Wossink, 2015. "The environmental Kuznets curve at different levels of economic development: a counterfactual quantile regression analysis for CO 2 emissions," Journal of Environmental Economics and Policy, Taylor & Francis Journals, vol. 4(3), pages 278-303, November.
    6. Muzafar Shah Habibullah* & Badariah Haji Din & Wei-Chong Choo & Siow-Hooi Tan, 2018. "The Number of Tourist Arrivals, Governance and Their Impact on Threatened Bird Species: Worldwide Evidence," The Journal of Social Sciences Research, Academic Research Publishing Group, pages 28-36:6.
    7. Halkos, George & Managi, Shunsuke, 2017. "Land use, forest preservation and biodiversity in Asia," MPRA Paper 82883, University Library of Munich, Germany.
    8. George E. Halkos & Michael L. Polemis, 2019. "The impact of market structure on environmental efficiency in the United States: A quantile approach," Business Strategy and the Environment, Wiley Blackwell, vol. 28(1), pages 127-142, January.
    9. Halkos, George & Matsiori, Steriani, 2018. "Environmental attitudes and preferences for coastal zone improvements," Economic Analysis and Policy, Elsevier, vol. 58(C), pages 153-166.
    10. Halkos, George & Petrou, Kleoniki Natalia, 2018. "Assessment of national waste generation in EU Member States’ efficiency," MPRA Paper 84590, University Library of Munich, Germany.
    11. Halkos, George & Matsiori, Steriani, 2017. "Estimating recreational values of coastal zones," MPRA Paper 80911, University Library of Munich, Germany.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Halkos, George, 2010. "Modelling biodiversity," MPRA Paper 39075, University Library of Munich, Germany.
    2. Parente, Paulo M.D.C. & Smith, Richard J., 2011. "Gel Methods For Nonsmooth Moment Indicators," Econometric Theory, Cambridge University Press, vol. 27(1), pages 74-113, February.
    3. White, Halbert & Kim, Tae-Hwan, 2002. "Estimation, Inference, and Specification Testing for Possibly Misspecified Quantile Regression," University of California at San Diego, Economics Working Paper Series qt1s38s0dn, Department of Economics, UC San Diego.
    4. Kollias Christos & Tzeremes Panayiotis & Paleologou Suzanna-Maria, 2020. "Defence Spending and Unemployment in the USA: Disaggregated Analysis by Gender and Age Groups," Peace Economics, Peace Science, and Public Policy, De Gruyter, vol. 26(2), pages 1-13, May.
    5. Bilias, Yannis & Chen, Songnian & Ying, Zhiliang, 2000. "Simple resampling methods for censored regression quantiles," Journal of Econometrics, Elsevier, vol. 99(2), pages 373-386, December.
    6. Huber, Martin & Melly, Blaise, 2011. "Quantile Regression in the Presence of Sample Selection," Economics Working Paper Series 1109, University of St. Gallen, School of Economics and Political Science.
    7. Whang, Yoon-Jae, 2006. "Smoothed Empirical Likelihood Methods For Quantile Regression Models," Econometric Theory, Cambridge University Press, vol. 22(2), pages 173-205, April.
    8. Agbeyegbe, Terence D., 2015. "An inverted U-shaped crude oil price return-implied volatility relationship," Review of Financial Economics, Elsevier, vol. 27(C), pages 28-45.
    9. Gaglianone, Wagner Piazza & Lima, Luiz Renato & Linton, Oliver & Smith, Daniel R., 2011. "Evaluating Value-at-Risk Models via Quantile Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 150-160.
    10. Moshe Buchinsky, 1998. "Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 88-126.
    11. William M. Rodgers, 2006. "Male White‐Black Wage Gaps, 1979‐1994: A Distributional Analysis," Southern Economic Journal, John Wiley & Sons, vol. 72(4), pages 773-793, April.
    12. Maria Marino & Alessio Farcomeni, 2015. "Linear quantile regression models for longitudinal experiments: an overview," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 229-247, August.
    13. Michelle L. Barnes & Anthony W. Hughes, 2002. "A quantile regression analysis of the cross section of stock market returns," Working Papers 02-2, Federal Reserve Bank of Boston.
    14. Daniel Pollmann & Thomas Dohmen & Franz Palm, 2020. "Robust Estimation of Wage Dispersion with Censored Data: An Application to Occupational Earnings Risk and Risk Attitudes," De Economist, Springer, vol. 168(4), pages 519-540, December.
    15. Pitselis, Georgios, 2020. "Multi-stage nested classification credibility quantile regression model," Insurance: Mathematics and Economics, Elsevier, vol. 92(C), pages 162-176.
    16. Thomas Q. Pedersen, 2015. "Predictable Return Distributions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 114-132, March.
    17. Mahadevan, Renuka & Suardi, Sandy, 2013. "Is there a role for caste and religion in food security policy? A look at rural India," Economic Modelling, Elsevier, vol. 31(C), pages 58-69.
    18. Genya Kobayashi & Hideo Kozumi, 2012. "Bayesian analysis of quantile regression for censored dynamic panel data," Computational Statistics, Springer, vol. 27(2), pages 359-380, June.
    19. LI, Tao & SUN, Laixiang & ZOU, Liang, 2009. "State ownership and corporate performance: A quantile regression analysis of Chinese listed companies," China Economic Review, Elsevier, vol. 20(4), pages 703-716, December.
    20. Vighneswara Swamy & M. Dharani, 2020. "RETRACTED ARTICLE: Google Search Intensity and the Investor Attention Effect: A Quantile Regression Approach," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 18(2), pages 403-423, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jpolmo:v:33:y:2011:i:4:p:618-635. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: http://www.elsevier.com/locate/inca/505735 .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/505735 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.