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Spatial Dependence of Average Prices for Product Categories and Its Change over Time: Evidence from Daily Data

Author

Listed:
  • Venera Timiryanova

    (Institute of Economics, Finance and Business, Ufa University of Science and Technology, Ufa 450076, Russia)

  • Irina Lakman

    (Institute of Economics, Finance and Business, Ufa University of Science and Technology, Ufa 450076, Russia)

  • Vadim Prudnikov

    (Institute of Economics, Finance and Business, Ufa University of Science and Technology, Ufa 450076, Russia)

  • Dina Krasnoselskaya

    (Institute of Economics, Finance and Business, Ufa University of Science and Technology, Ufa 450076, Russia
    Institute of Business Ecosystems and Creative Industries, Ufa State Petroleum Technological University, Ufa 450064, Russia)

Abstract

The price of market products is the result of the interaction of supply and demand. However, within the same country, prices can vary significantly, especially during crisis periods. The purpose of this study is to identify patterns in the changing spatial dependence of the prices of certain product categories, namely pasta, potatoes, sugar, candies, poultry and butter. We used daily data from 1 January 2019, to 31 March 2022, and analyzed two important indicators: spatial variation and spatial autocorrelation of average daily prices. The analysis showed that spatial dependency changes over time and follows its own pattern for each product category. We recognized cyclic changes in spatial autocorrelation and noticed the effect of legislative restrictions on spatial correlations. It has been shown that the spatial variation of prices and spatial autocorrelation can change in different directions.

Suggested Citation

  • Venera Timiryanova & Irina Lakman & Vadim Prudnikov & Dina Krasnoselskaya, 2022. "Spatial Dependence of Average Prices for Product Categories and Its Change over Time: Evidence from Daily Data," Forecasting, MDPI, vol. 5(1), pages 1-25, December.
    • Handle: RePEc:gam:jforec:v:5:y:2022:i:1:p:4-126:d:1021836
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    References listed on IDEAS

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    1. Jens Mehrhoff, 2019. "Introduction – The Value Chain of Scanner and Web Scraped Data," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 509, pages 5-11.
    2. José‐María Montero & Tiziana Laureti & Román Mínguez & Gema Fernández‐Avilés, 2020. "A Stochastic Model with Penalized Coefficients for Spatial Price Comparisons: An Application to Regional Price Indexes in Italy," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 66(3), pages 512-533, September.
    3. Bartlomiej Rokicki & Geoffrey J. D. Hewings, 2019. "Regional price deflators in Poland: evidence from NUTS-2 and NUTS-3 regions," Spatial Economic Analysis, Taylor & Francis Journals, vol. 14(1), pages 88-105, January.
    4. Kitenge, Erick M. & Morshed, A.K.M. Mahbub, 2019. "Price convergence among Indian cities: The role of linguistic differences, topography, and aggregation," Journal of Asian Economics, Elsevier, vol. 61(C), pages 34-50.
    5. Ilaria Benedetti & Tiziana Laureti & Luigi Palumbo & Brandon M. Rose, 2022. "Computation of High-Frequency Sub-National Spatial Consumer Price Indexes Using Web Scraping Techniques," Economies, MDPI, vol. 10(4), pages 1-20, April.
    6. Marie Leclair & Isabelle Léonard & Guillaume Rateau & Patrick Sillard & Gaëtan Varlet & Pierre Vernédal, 2019. "Scanner Data: Advances in Methodology and New Challenges for Computing Consumer Price," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 509, pages 13-29.
    7. Eric Girardin & Cheikh A. T. Sall, 2018. "Inflation Dynamics of Franc-Zone Countries Determinants, Co-movements and Spatial Interactions," Open Economies Review, Springer, vol. 29(2), pages 295-320, April.
    8. Konstantin Gluschenko, 2016. "Distribution dynamics of Russian regional prices," Empirical Economics, Springer, vol. 51(3), pages 1193-1213, November.
    9. Sebastian Weinand & Ludwig von Auer, 2020. "Anatomy of regional price differentials: evidence from micro-price data," Spatial Economic Analysis, Taylor & Francis Journals, vol. 15(4), pages 413-440, October.
    10. Amita Majumder & Ranjan Ray, 2020. "National and subnational purchasing power parity: a review," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 47(2), pages 103-124, June.
    11. Abraham, Katharine G. & Jarmin, Ron S. & Moyer, Brian C. & Shapiro, Matthew D. (ed.), 2022. "Big Data for Twenty-First-Century Economic Statistics," National Bureau of Economic Research Books, University of Chicago Press, edition 1, number 9780226801254, August.
    12. Distefano, Tiziano & Chiarotti, Guido & Laio, Francesco & Ridolfi, Luca, 2019. "Spatial Distribution of the International Food Prices: Unexpected Heterogeneity and Randomness," Ecological Economics, Elsevier, vol. 159(C), pages 122-132.
    13. Kakpo, Ange & Mills, Bradford F. & Brunelin, Stéphanie, 2022. "Weather shocks and food price seasonality in Sub-Saharan Africa: Evidence from Niger," Food Policy, Elsevier, vol. 112(C).
    14. Isabelle Léonard & Patrick Sillard & Gaëtan Varlet & Jean-Paul Zoyem, 2019. "Spatial Differences in Price Levels between French Regions and Cities with Scanner Data," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 509, pages 69-82.
    15. Fok, Dennis & Hans Franses, Philip & Paap, Richard, 2007. "Seasonality and non-linear price effects in scanner-data-based market-response models," Journal of Econometrics, Elsevier, vol. 138(1), pages 231-251, May.
    16. Katharine G. Abraham & Ron S. Jarmin & Brian Moyer & Matthew D. Shapiro, 2022. "Big Data for Twenty-First-Century Economic Statistics," NBER Books, National Bureau of Economic Research, Inc, number abra-7, January.
    17. Manisha Chakrabarty & Amita Majumder & Ranjan Ray, 2018. "A Framework for the Simultaneous Measurement of Spatial Variation and Temporal Movement in Prices in a Heterogeneous Country: The Dynamic Household Regional Product Dummy Model," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 64(3), pages 703-730, September.
    18. Jessie Handbury & David E. Weinstein, 2015. "Goods Prices and Availability in Cities," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(1), pages 258-296.
    19. Laureti Tiziana & Polidoro Federico, 2022. "Using Scanner Data for Computing Consumer Spatial Price Indexes at Regional Level: An Empirical Application for Grocery Products in Italy," Journal of Official Statistics, Sciendo, vol. 38(1), pages 23-56, March.
    20. Rishab Guha & Serena Ng, 2019. "A Machine Learning Analysis of Seasonal and Cyclical Sales in Weekly Scanner Data," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 403-436, National Bureau of Economic Research, Inc.
    21. Barry K. Goodwin & Thomas J. Grennes & Christine McCurdy, 1999. "Spatial price dynamics and integration in russian food markets," Journal of Economic Policy Reform, Taylor & Francis Journals, vol. 3(2), pages 157-193.
    22. Baruah, Prerona, 2020. "Seasonality in commodity prices across India: Extent and implications," 94th Annual Conference, April 15-17, 2020, K U Leuven, Belgium (Cancelled) 303694, Agricultural Economics Society - AES.
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