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Using panel econometric methods to estimate the effect of milk consumption on the mortality rate of prostate and ovarian cancer

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  • Hagen, Tobias
  • Waldeck, Stefanie

Abstract

Recently prostate and ovarian cancer has been related to milk consumption. However, existing observational studies based on country level data do not attempt to identify causal effects since they are only based on simple cross-sectional analyses. This paper takes a step toward estimating of causal effects of milk consumption on cancer by applying panel econometric models and by using the within-country variation of the mortality rates and food consumption instead of the between-country variation in a panel of up to 50 countries for 1990 to 2008. Possible methodological problems arising from omitted variables (confounding factors), heterogeneity, and outliers are carefully discussed and a wide range of recent panel econometric estimators are applied. The results indicate fairly well that milk consumption increases both the mortality rate of prostate cancer as well as the mortality rate of ovarian cancer. The estimated effects are also important in quantitative terms, i.e., a reduction in the consumption of milk products can reduce the number of people dying of prostate and ovarian cancer appreciably. Furthermore, the consumption of other animal food products as well as sugar seems to be harmful. For the mortality rate of ovarian cancer we find that total calories intake increases the mortality rate too.

Suggested Citation

  • Hagen, Tobias & Waldeck, Stefanie, 2014. "Using panel econometric methods to estimate the effect of milk consumption on the mortality rate of prostate and ovarian cancer," Working Paper Series 03, Frankfurt University of Applied Sciences, Faculty of Business and Law.
  • Handle: RePEc:zbw:fhfwps:03
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    as
    1. Stephen Bond & Anke Hoeffler & Jonathan Temple, 2001. "GMM Estimation of Empirical Growth Models," Economics Papers 2001-W21, Economics Group, Nuffield College, University of Oxford.
    2. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    3. Vogelsang, Timothy J., 2012. "Heteroskedasticity, autocorrelation, and spatial correlation robust inference in linear panel models with fixed-effects," Journal of Econometrics, Elsevier, vol. 166(2), pages 303-319.
    4. Magnus, J.R. & Powell, O.R. & Prüfer, P., 2008. "A Comparison of Two Averaging Techniques with an Application to Growth Empirics," Other publications TiSEM 0392dffa-51e0-4bc9-9644-f, Tilburg University, School of Economics and Management.
    5. Bruno, Giovanni S.F., 2005. "Approximating the bias of the LSDV estimator for dynamic unbalanced panel data models," Economics Letters, Elsevier, vol. 87(3), pages 361-366, June.
    6. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    7. Ivan A. Canay, 2011. "A simple approach to quantile regression for panel data," Econometrics Journal, Royal Economic Society, vol. 14(3), pages 368-386, October.
    8. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    9. John C. Driscoll & Aart C. Kraay, 1998. "Consistent Covariance Matrix Estimation With Spatially Dependent Panel Data," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 549-560, November.
    10. Giovanni S. F. Bruno, 2005. "Estimation and inference in dynamic unbalanced panel-data models with a small number of individuals," Stata Journal, StataCorp LP, vol. 5(4), pages 473-500, December.
    11. Han, Chirok & Phillips, Peter C. B., 2010. "Gmm Estimation For Dynamic Panels With Fixed Effects And Strong Instruments At Unity," Econometric Theory, Cambridge University Press, vol. 26(1), pages 119-151, February.
    12. Phillips, Peter C.B. & Sul, Donggyu, 2007. "Bias in dynamic panel estimation with fixed effects, incidental trends and cross section dependence," Journal of Econometrics, Elsevier, vol. 137(1), pages 162-188, March.
    13. Levine, Ross & Renelt, David, 1992. "A Sensitivity Analysis of Cross-Country Growth Regressions," American Economic Review, American Economic Association, vol. 82(4), pages 942-963, September.
    14. David Roodman, 2009. "A Note on the Theme of Too Many Instruments," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(1), pages 135-158, February.
    15. Stephen Bond & Anke Hoeffler, 2001. "GMM Estimation of Empirical Growth Models," Economics Series Working Papers 2001-W21, University of Oxford, Department of Economics.
    16. Christopher F Baum, 2001. "Residual diagnostics for cross-section time series regression models," Stata Journal, StataCorp LP, vol. 1(1), pages 101-104, November.
    17. Henry Kaiser, 1974. "An index of factorial simplicity," Psychometrika, Springer;The Psychometric Society, vol. 39(1), pages 31-36, March.
    18. Daniel Hoechle, 2007. "Robust standard errors for panel regressions with cross-sectional dependence," Stata Journal, StataCorp LP, vol. 7(3), pages 281-312, September.
    19. Magnus, Jan R. & Powell, Owen & Prüfer, Patricia, 2010. "A comparison of two model averaging techniques with an application to growth empirics," Journal of Econometrics, Elsevier, vol. 154(2), pages 139-153, February.
    20. Giuseppe De Luca & Jan R. Magnus, 2011. "Bayesian model averaging and weighted-average least squares: Equivariance, stability, and numerical issues," Stata Journal, StataCorp LP, vol. 11(4), pages 518-544, December.
    21. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    22. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    23. Emily Oster, 2013. "Unobservable Selection and Coefficient Stability: Theory and Validation," NBER Working Papers 19054, National Bureau of Economic Research, Inc.
    24. Ben Jann, 2013. "Plotting regression coefficients and other estimates in Stata," University of Bern Social Sciences Working Papers 1, University of Bern, Department of Social Sciences, revised 18 Sep 2017.
    25. Emad Abd Elmessih Shehata, 2012. "XTREGDHP: Stata module to estimate Han-Philips (2010) Linear Dynamic Panel Data Regression," Statistical Software Components S457456, Boston College Department of Economics, revised 19 May 2013.
    26. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    27. Windmeijer, Frank, 2005. "A finite sample correction for the variance of linear efficient two-step GMM estimators," Journal of Econometrics, Elsevier, vol. 126(1), pages 25-51, May.
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    More about this item

    Keywords

    Panel Econometrics; GMM; Dynamic Panel Data Methods; Fixed-Effects; Quantile Regression; Prostate Cancer; Ovarian Cancer; Cross-Country Analysis; Causal Effect; Quantile Regression; Bayesian Model Averaging; Extreme Bounds Analysis;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • Q18 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Policy; Food Policy; Animal Welfare Policy
    • I19 - Health, Education, and Welfare - - Health - - - Other

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