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On the Evolution of the United Kingdom Price Distributions

Author

Listed:
  • Ba M. Chu
  • Kim Huynh
  • David T. Jacho-Chávez
  • Oleksiy Kryvtsov

Abstract

We propose a functional principal components method that accounts for stratified random sample weighting and time dependence in the observations to understand the evolution of distributions of monthly micro-level consumer prices for the United Kingdom (UK). We apply the method to publicly available monthly data on individual-good prices collected in retail stores by the UK Office for National Statistics for the construction of the UK Consumer Price Index from March 1996 to September 2015. In addition, we conduct Monte Carlo simulations to demonstrate the effectiveness of our methodology. Our method allows us to visualize the dynamics of the price distribution and uncovers interesting patterns during the sample period. Further, we demonstrate the efficacy of our methodology with an out-of-sample forecasting algorithm that exploits the time dependence of distributions. Our out-of-sample forecast compares favorably with the random walk forecast.

Suggested Citation

  • Ba M. Chu & Kim Huynh & David T. Jacho-Chávez & Oleksiy Kryvtsov, 2018. "On the Evolution of the United Kingdom Price Distributions," Staff Working Papers 18-25, Bank of Canada.
  • Handle: RePEc:bca:bocawp:18-25
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    References listed on IDEAS

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    5. Oleksiy Kryvtsov, 2016. "Is there a quality bias in the Canadian CPI? Evidence from microdata," Canadian Journal of Economics, Canadian Economics Association, vol. 49(4), pages 1401-1424, November.
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    Cited by:

    1. Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023. "Big data forecasting of South African inflation," Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
    2. Richard Davies, 2021. "Prices and inflation in the UK - A new dataset," CEP Occasional Papers 55, Centre for Economic Performance, LSE.
    3. Gustavo Canavire-Bacarreza & Luis C. Carvajal-Osorio, 2020. "Two Stories of Wage Dynamics in Latin America: Different Policies, Different Outcomes," Journal of Labor Research, Springer, vol. 41(1), pages 128-168, June.
    4. Petrella, Ivan & Santoro, Emiliano & Simonsen, Lasse de la Porte, 2018. "Time-varying Price Flexibility and Inflation Dynamics," CEPR Discussion Papers 13027, C.E.P.R. Discussion Papers.
    5. Carvalho, Carlos & Kryvtsov, Oleksiy, 2021. "Price selection," Journal of Monetary Economics, Elsevier, vol. 122(C), pages 56-75.
    6. Joseph, Andreas & Kalamara, Eleni & Kapetanios, George & Potjagailo, Galina & Chakraborty, Chiranjit, 2021. "Forecasting UK inflation bottom up," Bank of England working papers 915, Bank of England, revised 27 Sep 2022.

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    More about this item

    Keywords

    Econometric and statistical methods; Inflation and prices;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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