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On endogeneity of consumer expenditures in the estimation of households demand system

Author

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  • Timofeeva, Anastasiia

    (Novosibirsk State Technical University, Novosibirsk, Russia)

Abstract

The paper analyzes one case of the endogeneity problem, namely, the presence of measurement error in consumer expenditures by estimation of the demand system according to the household sample surveys. Based on factor analysis a novel approach is proposed and tested. It allows weakening the bias of demand elasticity estimates and does not require additional information (instrumental variables, repeated observations). Its advantage is the possibility of estimating the parameters of the true consumer expenditures distribution which give an idea of the distortion degree of information on the population expenditures.

Suggested Citation

  • Timofeeva, Anastasiia, 2015. "On endogeneity of consumer expenditures in the estimation of households demand system," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 37(1), pages 87-106.
  • Handle: RePEc:ris:apltrx:0259
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    References listed on IDEAS

    as
    1. Deaton, Angus, 1986. "Demand analysis," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 3, chapter 30, pages 1767-1839, Elsevier.
    2. Richard Blundell & Xiaohong Chen & Dennis Kristensen, 2007. "Semi-Nonparametric IV Estimation of Shape-Invariant Engel Curves," Econometrica, Econometric Society, vol. 75(6), pages 1613-1669, November.
    3. Hausman, J. A. & Newey, W. K. & Powell, J. L., 1995. "Nonlinear errors in variables Estimation of some Engel curves," Journal of Econometrics, Elsevier, vol. 65(1), pages 205-233, January.
    4. Peter Ebbes, 2007. "A non-technical guide to instrumental variables and regressor-error dependencies (in Russian)," Quantile, Quantile, issue 2, pages 3-20, March.
    5. Jerry Hausman, 2001. "Mismeasured Variables in Econometric Analysis: Problems from the Right and Problems from the Left," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 57-67, Fall.
    6. James B. McDonald, 2008. "Some Generalized Functions for the Size Distribution of Income," Economic Studies in Inequality, Social Exclusion, and Well-Being, in: Duangkamon Chotikapanich (ed.), Modeling Income Distributions and Lorenz Curves, chapter 3, pages 37-55, Springer.
    7. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
    8. Brown, Alan & Deaton, Angus S, 1972. "Surveys in Applied Economics: Models of Consumer Behaviour," Economic Journal, Royal Economic Society, vol. 82(328), pages 1145-1236, December.
    9. Peter Ebbes & Michel Wedel & Ulf Böckenholt, 2009. "Frugal IV alternatives to identify the parameter for an endogenous regressor," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(3), pages 446-468, April.
    10. Steven Vanduffel & Tom Hoedemakers & Jan Dhaene, 2005. "Comparing Approximations for Risk Measures of Sums of Nonindependent Lognormal Random Variables," North American Actuarial Journal, Taylor & Francis Journals, vol. 9(4), pages 71-82.
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    More about this item

    Keywords

    endogenous regressor; measurement error; consumer expenditures; income elasticity of demand; factor analysis; method of instrumental variables.;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution

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