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Dynamic Cost-offsets of Prescription Drug Expenditures: Panel Data Analysis Using a Copula-based Hurdle Model

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  • Partha Deb
  • Pravin K. Trivedi
  • David M. Zimmer

Abstract

This paper presents a new multivariate copula-based modeling approach for analyzing cost-offsets between drug and nondrug expenditures. Estimates are based on panel data from the Medical Expenditure Panel Survey (MEPS) with quarterly measures of medical expenditures. The approach allows for nonlinear dynamic dependence between drug and nondrug expenditures as well as asymmetric contemporaneous dependence. The specification uses the standard hurdle model with two significant extensions. First, it is adapted to the bivariate case. Second, because the cost-offset hypothesis is inherently dynamic, the bivariate hurdle framework is extended to accommodate dynamic relationships between drug and nondrug spending. The econometric analysis is implemented for six different groups defined by specific health conditions. There is evidence of modest cost-offsets of expenditures on prescribed drugs.

Suggested Citation

  • Partha Deb & Pravin K. Trivedi & David M. Zimmer, 2009. "Dynamic Cost-offsets of Prescription Drug Expenditures: Panel Data Analysis Using a Copula-based Hurdle Model," NBER Working Papers 15191, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:15191
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    1. Trivedi, Pravin K. & Zimmer, David M., 2007. "Copula Modeling: An Introduction for Practitioners," Foundations and Trends(R) in Econometrics, now publishers, vol. 1(1), pages 1-111, April.
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    Cited by:

    1. Albouy, Valerie & Davezies, Laurent & Debrand, Thierry, 2010. "Health expenditure models: A comparison using panel data," Economic Modelling, Elsevier, vol. 27(4), pages 791-803, July.
    2. William Encinosa & Didem Bernard & Avi Dor, 2010. "Does Prescription Drug Adherence Reduce Hospitalizations and Costs?," NBER Working Papers 15691, National Bureau of Economic Research, Inc.
    3. Christelis, Dimitris & Sanz-de-Galdeano, Anna, 2011. "Smoking persistence across countries: A panel data analysis," Journal of Health Economics, Elsevier, vol. 30(5), pages 1077-1093.
    4. Silvia Balia & Rinaldo Brau, 2014. "A Country For Old Men? Long‐Term Home Care Utilization In Europe," Health Economics, John Wiley & Sons, Ltd., vol. 23(10), pages 1185-1212, October.
    5. Brechmann, Eike & Czado, Claudia & Paterlini, Sandra, 2014. "Flexible dependence modeling of operational risk losses and its impact on total capital requirements," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 271-285.

    More about this item

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets

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