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

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Author Info

  • 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.

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File URL: http://www.latrobe.edu.au/__data/assets/pdf_file/0017/130922/2010.06.pdf
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Bibliographic Info

Paper provided by School of Economics, La Trobe University in its series Working Papers with number 2010.06.

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Length: 35 pages
Date of creation: 2010
Date of revision:
Handle: RePEc:ltr:wpaper:2010.06

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Related research

Keywords: ARDL model; Bias-correction; Bootstrapping; Highest density region; Long run elasticity EDIRC Provider-Institution: RePEc:edi:smlatau;

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References

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  1. 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.
  2. 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 306-14, May.
  3. Davidson, R. & Flachaire, E., 1999. "The Wild Bootstrap, Tamed at Last," G.R.E.Q.A.M. 99a32, Universite Aix-Marseille III.
  4. 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.
  5. 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.
  6. Lutz Kilian, 1998. "Confidence intervals for impulse responses under departures from normality," Econometric Reviews, Taylor & Francis Journals, vol. 17(1), pages 1-29.
  7. 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.
  8. 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 345-49, May.
  9. Kling, Catherine L. & Dorfman, Jeffrey & Sexton, Richard, 1990. "Confidence Intervals for Elasticities and Flexibilities: Re-Evaluating the Ratios of Normals Case," Staff General Research Papers 1582, Iowa State University, Department of Economics.
  10. 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.
  11. James G. MacKinnon & Anthony A. Smith Jr., 1995. "Approximate Bias Correction in Econometrics," Working Papers 919, Queen's University, Department of Economics.
  12. Jae Kim & Param Silvapulle & Rob J. Hyndman, 2006. "Half-Life Estimation based on the Bias-Corrected Bootstrap: A Highest Density Region Approach," Monash Econometrics and Business Statistics Working Papers 11/06, Monash University, Department of Econometrics and Business Statistics.
  13. 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 257-92, June.
  14. Russell L. Lamb & Francis X. Diebold, 1996. "Why are estimates of agricultural supply response so variable?," Finance and Economics Discussion Series 96-8, Board of Governors of the Federal Reserve System (U.S.).
  15. 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.
  16. 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.
  17. David F. Hendry & Neil R. Ericsson, 1990. "Modeling the demand for narrow money in the United Kingdom and the United States," International Finance Discussion Papers 383, Board of Governors of the Federal Reserve System (U.S.).
  18. 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.
  19. 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.
  20. 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.
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Cited by:
  1. 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.

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