Economics and management of national economy

The panel data regression concept in consumption modelling

Economic Annals-XXI

Economic Annals-ХХI: Volume 185, Issue 9-10, Pages: 61-69

Citation information:
Szwacka-Mokrzycka, J. (2020). The panel data regression concept in consumption modelling. Economic Annals-XXI, 185(9-10), 61-69. doi: https://doi.org/10.21003/ea.V185-06

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Joanna Szwacka-Mokrzycka
D. Sc. (Economics),
Full Professor,
Department of Development Policy and Marketing,
Institute of Economics and Finance,
Warsaw University of Life Sciences (SGGW)
166 Nowoursynowska Str., Warsaw, 02-787, Poland
joanna_szwacka@sggw.edu.pl
ORCID ID: https://orcid.org/0000-0001-9243-5404

The panel data regression concept in consumption modelling

Abstract. Panel data research constitutes a new methodological approach to studies covering the area of food consumption. The modelling procedure described in this article was carried out taking into account the cross-section of individual types of households and product categories. Product categories included: bread and cereals, cakes and bakery products, meat, fish, milk, yoghurts and dairy drinks, cheese, oils and other vegetable fats, animal fats, fruits, vegetables, confectionery products and juices. Surveys focusing on households’ budgets in the period of 2003-2019 and provided by the Central Statistical Office of Poland (CSO) were the source of information used for the panel data analysis applied in this work.

The author has undertaken the task of building all the presented models for panel data, namely fixed effects, random effects and pooled regression. It turned out that the correct models are those with fixed individual effects.

In conclusion, it was established that panel models are a useful tool for modelling the consumption of food products. When analysing the constructed models, it is possible to observe significant differences in the consumption of the examined products between the correspondent quintile groups. This study confirms the tendency in food consumption in Poland, observed in 2003-2015.

Keywords: Panel Regression; Households’ Budgets; Individual Effects for Product Categories; Modelling the Consumption of Food Products

JEL Classification: C1; C5; D1

Acknowledgements and Funding: The paper was prepared on the basis of the results of own research conducted in the years 2013-2019.

Contribution: The author contributed personally to this work.

DOI: https://doi.org/10.21003/ea.V185-06

References

  1. Baltagi, B. H. (2013). Econometric Analysis of Panel Data (Fifth Edition). Chichester: John Wiley and Sons, Ltd.
  2. Biørn, E. (2016). Econometrics of Panel Data: Methods and Applications (1st edition). Oxford: OUP.
  3. Blundell, R., & Bond, S. (2000). GMM estimation with persistent panel data: an application to production functions. Econometric Reviews, 19(3), 321- 340.
    https://doi.org/10.1080/07474930008800475
  4. Bruno, G. (2004). Limited dependent panel data models: a comparative analysis of classical and Bayesian inference among econometric packages. Computing in Economics and Finance, 41.
    https://econpapers.repec.org/paper/scescecf4/41.htm
  5. Central Statistical Office. (2004, 2011, 2016, 2020). Household budgets in the years 2003, 2010, 2015. Warsaw (in Pol.).
  6. Dańska, B., & Laskowska, I. (2011). The use of panel models to study the diversification of economic expenses for food, hygiene and health protection. Works of the Institute of Econometrics and Statistics of the Łodz University (in Pol.).
  7. Dańska-Borsiak, B. (2009). Application of dynamic panel models in microeconomic and macroeconomic research. Statistical Review, 56(2), 25-41.
    https://ps.stat.gov.pl/Article/2009/2/025-041 (in Pol.)
  8. Hsiao, C. (2003). Analysis of panel data. Cambridge University Press, Cambridge.
  9. Islam, N. (1995). Growth empirics: a panel data approach. Quarterly Journal of Economics, 110(4), 1127-1170.
    https://doi.org/10.2307/2946651
  10. Karabiyik, H., Palm, F. C., & Urbain, J. P. (2019). Econometric Analysis of Panel Data Models with Multifactor Error Structures. Annual Review of Economics, 11, 495-522.
    https://doi.org/10.1146/annurev-economics-063016-104338
  11. Kehlbacher, A. (2012). Estimating Food and Drink Demand Elasticities using the Family Food Module of the LCF data. University of Reading Department of Food Economics and Marketing.
  12. Klonaris, S. (2001). Structural change in Greek meat demand. Agricultural Economics Review, 2(2), 31-41.
    https://www.researchgate.net/publication/23772308_Structural_Change_in_Greek_Meat_Demand
  13. Kwasek, M. (2008). Income elasticity of food in Poland. Statistical News, 39-51 (in Pol.).
  14. Kwasek, M. (Ed.). (2015). Analysis of Poland’s food security. Multiannual Program Monographs, Warsaw (in Pol.).
  15. Maddala, G. S. (2001). Introduction to Econometrics. Chichester: John Wiley and Sons Ltd.
  16. Osińska, M. (Ed.). (2007). Contemporary econometrics. Toruń: Print – Intro, Inowrocław SA (in Pol.).
  17. Szwacka-Mokrzycka, J. (2018). Paradigms of food consumption development in Poland. Warsaw: Polish Scientific Publishing House (in Pol.).
  18. Wooldridge, J. H. (2001). Econometric Analysis of Cross Section and Panel Data. Cambridge: The MIT Press.

Received 25.09.2020
Received in revised form 20.10.2020
Accepted 26.10.2020
Available online 21.11.2020
Updated version of the paper 18.02.2021

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