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Un sistema lineal de gasto: identificando patrones de consumo de alimentos en Bolivia

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  • Cristian Ricardo Nogales Carvajal

    (Centro de Investigaciones Económicas y Empresariales, Universidad Privada Boliviana)

Abstract

El presente estudio aborda la identificación de patrones consumo, partiendo de una revisión teórica de las particularidades de las preferencias individuales subyacentes en el Sistema Lineal de Gasto (LES). Se describe, explica e implementa un método de estimación del Sistema por Máxima de Verosimilitud propuesto por Kao, Lee & Pitt (2001). Para facilitar la estimación, el método contempla la simulación de la parte de la función de verosimilitud que toma en cuenta de manera explícita la ausencia de consumo de algunos bienes por parte de algunos hogares. Para enriquecer el Sistema, el método permite incluir variables sociodemográficas susceptibles de determinar los patrones de consumo. Este método es aplicado al estudio de patrones de consumo en Bolivia, diferenciados de acuerdo a la zona geográfica: llanos, altiplano y valles. De manera general, se encuentra que los hogares bolivianos prefieren los alimentos que presentan un grado de elaboración intermedia. Se constata, entre otros resultados importantes, que este tipo de bienes presentan una sensibilidad relativamente alta a cambios en sus precios, el ingreso de los hogares y los precios del resto de los alimentos.

Suggested Citation

  • Cristian Ricardo Nogales Carvajal, 2009. "Un sistema lineal de gasto: identificando patrones de consumo de alimentos en Bolivia," Investigación & Desarrollo, Universidad Privada Boliviana, vol. 1(1), pages 27-43.
  • Handle: RePEc:iad:wpaper:0109
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    References listed on IDEAS

    as
    1. William A. Barnett & Ikuyasu Usui, 2007. "The Theoretical Regularity Properties of the Normalized Quadratic Consumer Demand Model," International Symposia in Economic Theory and Econometrics, in: Functional Structure Inference, pages 107-127, Emerald Group Publishing Limited.
    2. Barnett, William A. & Serletis, Apostolos, 2008. "Consumer preferences and demand systems," Journal of Econometrics, Elsevier, vol. 147(2), pages 210-224, December.
    3. Denis Conniffe & John Eakins, 2003. "Does the Stochastic Specification of the Linear Expenditure System Matter?," The Economic and Social Review, Economic and Social Studies, vol. 34(1), pages 23-32.
    4. Paul De Boer & Richard Paap, 2009. "Testing non‐nested demand relations: linear expenditure system versus indirect addilog," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 63(3), pages 368-384, August.
    5. Barnett, William A. & Serletis, Apostolos, 2008. "Measuring Consumer Preferences and Estimating Demand Systems," MPRA Paper 12318, University Library of Munich, Germany.
    6. Paris, Quirino & Perali, Carlo Federico & Piccoli, Luca, 2004. "Primal-Dual Estimation of a Linear Expenditure Demand System," Working Papers 93741, University of California, Davis, Department of Agricultural and Resource Economics.
    7. Cooper, Russel J & McLaren, Keith R, 1996. "A System of Demand Equations Satisfying Effectively Global Regularity Conditions," The Review of Economics and Statistics, MIT Press, vol. 78(2), pages 359-364, May.
    8. Chihwa Kao & Lung-fei Lee & Mark M. Pitt, 2001. "Simulated Maximum Likelihood Estimation of the Linear Expenditure System with Binding Non-Negativity Constraints," Annals of Economics and Finance, Society for AEF, vol. 2(1), pages 215-235, May.
    9. 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

    Sistema Lineal de Gasto; Máxima de Verosimilitud; Elasticidad Ingreso; Elasticidad Precio Directa; Elasticidad Precio Cruzada;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory

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