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Stochastic Trends, Demographics and Demand Systems

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  • Clifford Attfield

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Abstract

Techniques for determining the number of stochastic trends generating a set of non-stationary panel data are applied to budget shares for a number of commodity groups from the Family Expenditure Survey (FES) for the UK for the years 1973-2001. It is argued that some stochastic trends in macro data are generated by the aggregation of fixed demographic effects in the micro data. From cross section data, fixed effect coefficients are estimated which incorporate both age and income distribution effects. The estimated coefficients are combined with age proportion variables to form a set of I(1) indices for broad commodity groups which are then incorporated into a system of aggregate demand equations. The equations are estimated and tested in a non-stationary time series setting.

Suggested Citation

  • Clifford Attfield, 2004. "Stochastic Trends, Demographics and Demand Systems," Bristol Economics Discussion Papers 04/563, Department of Economics, University of Bristol, UK.
  • Handle: RePEc:bri:uobdis:04/563
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    References listed on IDEAS

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    1. Ng, Serena, 1995. "Testing for Homogeneity in Demand Systems When the Regressors Are Nonstationary," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 147-163, April-Jun.
    2. Lewbel, Arthur, 1996. "Aggregation without Separability: A Generalized Composite Commodity Theorem," American Economic Review, American Economic Association, vol. 86(3), pages 524-543, June.
    3. Choi, In, 2001. "Unit root tests for panel data," Journal of International Money and Finance, Elsevier, vol. 20(2), pages 249-272, April.
    4. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    5. James Banks & Richard Blundell & Arthur Lewbel, 1997. "Quadratic Engel Curves And Consumer Demand," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 527-539, November.
    6. Jushan Bai & Serena Ng, 2004. "A PANIC Attack on Unit Roots and Cointegration," Econometrica, Econometric Society, vol. 72(4), pages 1127-1177, July.
    7. Blundell, Richard & Pashardes, Panos & Weber, Guglielmo, 1993. "What Do We Learn About Consumer Demand Patterns from Micro Data?," American Economic Review, American Economic Association, vol. 83(3), pages 570-597, June.
    8. 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.
    9. Bai, Jushan, 2004. "Estimating cross-section common stochastic trends in nonstationary panel data," Journal of Econometrics, Elsevier, vol. 122(1), pages 137-183, September.
    10. Osterwald-Lenum, Michael, 1992. "A Note with Quantiles of the Asymptotic Distribution of the Maximum Likelihood Cointegration Rank Test Statistics," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 54(3), pages 461-472, August.
    11. Kao, Chihwa, 1999. "Spurious regression and residual-based tests for cointegration in panel data," Journal of Econometrics, Elsevier, vol. 90(1), pages 1-44, May.
    12. Deaton, Angus S & Muellbauer, John, 1980. "An Almost Ideal Demand System," American Economic Review, American Economic Association, vol. 70(3), pages 312-326, June.
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    Cited by:

    1. L. Pieroni & D. Lanari & L. Salmasi, 2013. "Food prices and overweight patterns in Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 14(1), pages 133-151, February.

    More about this item

    Keywords

    Demand Equations; Age Demographics; Stochastic Trends.;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • D1 - Microeconomics - - Household Behavior

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