Disentangling demand and supply inflation shocks from electronic payments data

被引:0
作者
Carlomagno, Guillermo [1 ]
Eterovic, Nicolas [1 ]
Hernandez-Roman, Luis G. [2 ,3 ]
机构
[1] Cent Bank Chile, Agustinas 1180, Rm 8340454, Chile
[2] Univ Warwick, Warwick Business Sch, Coventry CV4 7AL, England
[3] Bank Mexico, Directorate Gen Econ Res, Cinco Mayo 18, Mexico City 06059, DF, Mexico
关键词
Emerging economy; COVID-19; Inflation; Supply and demand shocks; SVAR; STRUCTURAL VECTOR AUTOREGRESSIONS; MONETARY-POLICY; AGGREGATE; RESTRICTIONS; CONSUMPTION; FORECAST; PRICES; SVAR; SIGN;
D O I
10.1016/j.econmod.2024.106871
中图分类号
F [经济];
学科分类号
02 ;
摘要
We propose a novel way to track inflation dynamics by identifying supply and demand shocks at a highly disaggregated level using electronic payments data. We estimate SVAR models and group historical decompositions at the product level into categories of the CPI. Our approach differs from others by explicitly estimating the shocks and retrieving their time-series dynamics. This information is valuable for monetary policy design, as it allows us to assess: (i) the type of shock driving any inflation category, (ii) whether shocks are generalized or driven by large shocks to specific items, and (iii) how the shocks evolve over time. An application to Chile suggests three distinct phases of inflation dynamics since COVID-19. In 2020, negative supply and demand shocks nearly offset each other. In 2021, demand shocks were boosted by massive liquidity injections. In 2022, global supply shocks introduced additional pressures on top of already elevated inflation.
引用
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页数:11
相关论文
共 66 条
  • [1] Aastveit K.A., 2020, Norges Bank Res., V17
  • [2] Acevedo P., 2023, Invoices Rather Than Surveys: Using ML to Build Nominal and Real Indices
  • [3] International Income Inequality: Measuring PPP Bias by Estimating Engel Curves for Food
    Almas, Ingvild
    [J]. AMERICAN ECONOMIC REVIEW, 2012, 102 (02) : 1093 - 1117
  • [4] Consumer responses to the COVID-19 crisis: evidence from bank account transaction data
    Andersen, Asger Lau
    Hansen, Emil Toft
    Johannesen, Niels
    Sheridan, Adam
    [J]. SCANDINAVIAN JOURNAL OF ECONOMICS, 2022, 124 (04) : 905 - 929
  • [5] Can we measure inflation expectations using Twitter?
    Angelico, Cristina
    Marcucci, Juri
    Miccoli, Marcello
    Quarta, Filippo
    [J]. JOURNAL OF ECONOMETRICS, 2022, 228 (02) : 259 - 277
  • [6] Aprigliano V, 2019, INT J CENT BANK, V15, P55
  • [7] The systematic component of monetary policy in SVARs: An agnostic identification procedure
    Arias, Jonas E.
    Caldara, Dario
    Rubio-Ramirez, Juan F.
    [J]. JOURNAL OF MONETARY ECONOMICS, 2019, 101 : 1 - 13
  • [8] Inference Based on Structural Vector Autoregressions Identified With Sign and Zero Restrictions: Theory and Applications
    Arias, Jonas E.
    Rubio-Ramirez, Juan F.
    Waggoner, Daniel F.
    [J]. ECONOMETRICA, 2018, 86 (02) : 685 - 720
  • [9] How Does Household Spending Respond to an Epidemic? Consumption during the 2020 COVID-19 Pandemic
    Baker, Scott R.
    Farrokhnia, Robert A.
    Meyer, Steffen
    Pagel, Michaela
    Yannelis, Constantine
    [J]. REVIEW OF ASSET PRICING STUDIES, 2020, 10 (04) : 834 - 862
  • [10] Real-time nowcasting of nominal GDP with structural breaks
    Barnett, William A.
    Chauvet, Marcelle
    Leiva-Leon, Danilo
    [J]. JOURNAL OF ECONOMETRICS, 2016, 191 (02) : 312 - 324