IDEAS home Printed from
   My bibliography  Save this article

Posterior Analysis of Environmental Damage Evaluation in Europe


  • Efthymios Tsionas
  • George Halkos


In this paper we consider environmental damage evaluation using two types of econometric models to analyse tree damage due to acid deposits, using cross-country data for several European countries. First, we use a set of univariate Poisson models and second, a multinomial probit model with the difference that we do not observe class specific data. The damage function is parameterized in terms of a number of economic and environmental variables. Statistical inference is conducted using Bayesian methods relying on Markov Chain Monte Carlo simulation. Natural by-products of our method include an implied ranking of countries according to environmental damage, and (exact, finite sample) posterior distributions of average country-specific environmental damage.

Suggested Citation

  • Efthymios Tsionas & George Halkos, 2000. "Posterior Analysis of Environmental Damage Evaluation in Europe," International Review of Applied Economics, Taylor & Francis Journals, vol. 14(3), pages 371-390.
  • Handle: RePEc:taf:irapec:v:14:y:2000:i:3:p:371-390
    DOI: 10.1080/02692170050084097

    Download full text from publisher

    File URL:
    Download Restriction: Access to full text is restricted to subscribers.

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

    References listed on IDEAS

    1. Halkos, George E, 1993. "Optimal sulphur emissions abatement in Europe," MPRA Paper 33536, University Library of Munich, Germany.
    2. Pearce, David, 1996. "Economic valuation and health damage from air pollution in the developing world," Energy Policy, Elsevier, vol. 24(7), pages 627-630, July.
    3. Terza, Joseph V & Wilson, Paul W, 1990. "Analyzing Frequencies of Several Types of Events: A Mixed Multinomial-Poisson Approach," The Review of Economics and Statistics, MIT Press, vol. 72(1), pages 108-115, February.
    4. Amemiya, Takeshi, 1981. "Qualitative Response Models: A Survey," Journal of Economic Literature, American Economic Association, vol. 19(4), pages 1483-1536, December.
    5. Kloek, Tuen & van Dijk, Herman K, 1978. "Bayesian Estimates of Equation System Parameters: An Application of Integration by Monte Carlo," Econometrica, Econometric Society, vol. 46(1), pages 1-19, January.
    6. Pearce, David & Crowards, Tom, 1996. "Particulate matter and human health in the United Kingdom," Energy Policy, Elsevier, vol. 24(7), pages 609-619, July.
    7. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
    8. Naylor, J. C. & Smith, A. F. M., 1988. "Econometric illustrations of novel numerical integration strategies for Bayesian inference," Journal of Econometrics, Elsevier, vol. 38(1-2), pages 103-125.
    9. Tsionas, Efthymios G., 1998. "Monte Carlo inference in econometric models with symmetric stable disturbances," Journal of Econometrics, Elsevier, vol. 88(2), pages 365-401, November.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. Dimitris Christopoulos & Efthymios Tsionas, 2002. "Unemployment and government size: Is there any credible causality?," Applied Economics Letters, Taylor & Francis Journals, vol. 9(12), pages 797-800.
    2. Dimitris Christopoulos & John Loizides & Efthymios Tsionas, 2005. "The Abrams curve of government size and unemployment: evidence from panel data," Applied Economics, Taylor & Francis Journals, vol. 37(10), pages 1193-1199.
    3. Dimitris K. Christopoulos & Efthymios G. Tsionas, 2005. "Productivity growth and inflation in Europe: Evidence from panel cointegration tests," Empirical Economics, Springer, vol. 30(1), pages 137-150, January.

    More about this item


    Access and download statistics


    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:taf:irapec:v:14:y:2000:i:3:p:371-390. 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: (Chris Longhurst). General contact details of provider: .

    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 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.

    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.