IDEAS home Printed from https://ideas.repec.org/p/ltr/wpaper/2010.06.html
   My bibliography  Save this paper

Improved Interval Estimation of Long Run Response from a Dynamic Linear Model: A Highest Density Region Approach

Author

Listed:
  • Jae H Kim

    () (Department of Economics and Finance, La Trobe University)

  • Iain Fraser

    (University of Kent)

  • Rob J. Hyndman

    (Monash University)

Abstract

This paper proposes a new method of interval estimation for the long run response (or elasticity) parameter from a general linear dynamic model. We employ the bias- corrected bootstrap, in which small sample biases associated with the parameter estimators are adjusted in two stages of the bootstrap. As a means of bias-correction, we use alternative analytic and bootstrap methods. To take atypical properties of the long run elasticity estimator into account, the highest density region (HDR) method is adopted for the construction of confidence intervals. From an extensive Monte Carlo experiment, we found that the HDR confidence interval based on indirect analytic bias-correction performs better than other alternatives, providing tighter intervals with excellent coverage properties. Two case studies (demand for oil and demand for beef) illustrate the results of the Monte Carlo experiment with respect to the superior performance of the confidence interval based on indirect analytic bias-correction.

Suggested Citation

  • Jae H Kim & Iain Fraser & Rob J. Hyndman, 2010. "Improved Interval Estimation of Long Run Response from a Dynamic Linear Model: A Highest Density Region Approach," Working Papers 2010.06, School of Economics, La Trobe University.
  • Handle: RePEc:ltr:wpaper:2010.06
    as

    Download full text from publisher

    File URL: http://www.latrobe.edu.au/__data/assets/pdf_file/0017/130922/2010.06.pdf
    File Function: First version, 2010.06.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Jeffrey H. Dorfman & Catherine L. Kling & Richard J. Sexton, 1990. "Confidence Intervals for Elasticities and Flexibilities: Reevaluating the Ratios of Normals Case," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 72(4), pages 1006-1017.
    2. Focarelli, Dario, 2005. "Bootstrap bias-correction procedure in estimating long-run relationships from dynamic panels, with an application to money demand in the euro area," Economic Modelling, Elsevier, vol. 22(2), pages 305-325, March.
    3. David Letson & B.D. McCullough, 1998. "Better Confidence Intervals: The Double Bootstrap with No Pivot," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(3), pages 552-559.
    4. Davidson, Russell & Flachaire, Emmanuel, 2008. "The wild bootstrap, tamed at last," Journal of Econometrics, Elsevier, vol. 146(1), pages 162-169, September.
    5. Kim, Jae H. & Silvapulle, Param & Hyndman, Rob J., 2007. "Half-life estimation based on the bias-corrected bootstrap: A highest density region approach," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3418-3432, April.
    6. Mario Mazzocchi, 2006. "No News Is Good News: Stochastic Parameters versus Media Coverage Indices in Demand Models after Food Scares," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(3), pages 727-741.
    7. Lutz Kilian, 1998. "Small-Sample Confidence Intervals For Impulse Response Functions," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 218-230, May.
    8. Kiviet, Jan F. & Phillips, Garry D. A., 1994. "Bias assessment and reduction in linear error-correction models," Journal of Econometrics, Elsevier, vol. 63(1), pages 215-243, July.
    9. John Leeming & Paul Turner, 2004. "The BSE crisis and the price of red meat in the UK," Applied Economics, Taylor & Francis Journals, vol. 36(16), pages 1825-1829.
    10. Diebold, Francis X. & Lamb, Russell L., 1997. "Why are estimates of agricultural supply response so variable?," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 357-373.
    11. Hendry, David F. & Ericsson, Neil R., 1991. "Modeling the demand for narrow money in the United Kingdom and the United States," European Economic Review, Elsevier, vol. 35(4), pages 833-881, May.
    12. Kajal Lahiri, 2005. "Analysis of Panel Data," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(4), pages 1093-1095.
    13. Bewley, Ronald & Fiebig, Denzil G, 1990. "Why Are Long-run Parameter Estimates So Disparate?," The Review of Economics and Statistics, MIT Press, vol. 72(2), pages 345-349, May.
    14. MacKinnon, James G. & Smith Jr., Anthony A., 1998. "Approximate bias correction in econometrics," Journal of Econometrics, Elsevier, vol. 85(2), pages 205-230, August.
    15. Marquez, Jaime & McNeilly, Caryl, 1988. "Income and Price Elasticities for Exports of Developing Countries," The Review of Economics and Statistics, MIT Press, vol. 70(2), pages 306-314, May.
    16. Askari, Hossein & Cummings, John Thomas, 1977. "Estimating Agricultural Supply Response with the Nerlove Model: A Survey," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 257-292, June.
    17. James M. Griffin & Craig T. Schulman, 2005. "Price Asymmetry in Energy Demand Models: A Proxy for Energy-Saving Technical Change?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 1-22.
    18. Dermot Gately & Hiliard G. Huntington, 2002. "The Asymmetric Effects of Changes in Price and Income on Energy and Oil Demand," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 19-55.
    19. Vinod, H. D. & McCullough, B. D., 1994. "Bootstrapping demand and supply elasticities: The Indian case," Journal of Asian Economics, Elsevier, vol. 5(3), pages 367-379.
    20. Iain Fraser & Imad A. Moosa, 2002. "Demand Estimation in the Presence of Stochastic Trend and Seasonality: The Case of Meat Demand in the United Kingdom," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(1), pages 83-89.
    21. Lutz Kilian, 1998. "Confidence intervals for impulse responses under departures from normality," Econometric Reviews, Taylor & Francis Journals, vol. 17(1), pages 1-29.
    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. Galip Altinay & A. Talha Yalta, 2016. "Estimating the evolution of elasticities of natural gas demand: the case of Istanbul, Turkey," Empirical Economics, Springer, vol. 51(1), pages 201-220, August.
    2. A. Talha Yalta, 2013. "The Dynamics of Road Energy Demand and Illegal Fuel Activity in Turkey: A Rolling Window Analysis," Working Papers 1304, TOBB University of Economics and Technology, Department of Economics, revised Jul 2013.
    3. Yalta, A. Talha & Yalta, A. Yasemin, 2016. "The dynamics of fuel demand and illegal fuel activity in Turkey," Energy Economics, Elsevier, vol. 54(C), pages 144-158.

    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:ltr:wpaper:2010.06. 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: (Stephen Scoglio). General contact details of provider: http://edirc.repec.org/data/sblatau.html .

    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.