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Estimating Demand with Varied Levels of Aggregation

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  • Grose, S.
  • McLaren, K.

Abstract

The response of consumer demand to prices, income, and other characteristics is important for a range of policy issues. Naturally, the level of detail for which consumer behaviour can be estimated depends on the level of disaggregation of the available data. However, it is often the case that the available data is differently aggregated in different time periods, with the information available in later time periods usually being more detailed. The applied researcher is thus faced with choosing between detail, in which case the more highly aggregated data is ignored; or duration, in which case the data must be aggregated up to the "lowest common denominator". This paper develops a specification/estimation technique that exploits the entire information content of a variably-aggregated data set.

Suggested Citation

  • Grose, S. & McLaren, K., 2000. "Estimating Demand with Varied Levels of Aggregation," Monash Econometrics and Business Statistics Working Papers 1/00, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2000-1
    as

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    File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2000/wp1-00.pdf
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    References listed on IDEAS

    as
    1. Fry, Jane M. & Fry, Tim R. L. & McLaren, Keith R., 1996. "The stochastic specification of demand share equations: Restricting budget shares to the unit simplex," Journal of Econometrics, Elsevier, vol. 73(2), pages 377-385, August.
    2. de Boer, P. M. C. & Harkema, R., 1986. "Maximum likelihood estimation of sum-constrained linear models with insufficient observations," Economics Letters, Elsevier, vol. 20(4), pages 325-329.
    3. 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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    More about this item

    Keywords

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth

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