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Does PPI lead CPI IN Brazil?

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  • Ivo da Rocha Lima Filho, Roberto

Abstract

The main question of the paper is to add an additional explanation to an economic empirical causality between PPI (Producer Price Index) and CPI (Consumer Price Index), that is, does PPI's final stage of processing, also known as final goods, is a lead indicator for the CPI in the Brazilian economy? If so, how strong or weak this relationship is? Is there a one-way or two-way causality? How does exogenous shocks affect both variables in terms of future trajectories? This is analysed empirically through a traditional VAR (Vector Autoregressive) and BVAR (Bayesian Vector Autoregressive) models so as to understand the dynamics of both PPI and CPI - inertial and principal components - and also out-of-sample forecasting. We conclude that it can be verified that PPI final goods can be a good leading indicator for the domestic CPI, purging ex - in natura foods.

Suggested Citation

  • Ivo da Rocha Lima Filho, Roberto, 2019. "Does PPI lead CPI IN Brazil?," International Journal of Production Economics, Elsevier, vol. 214(C), pages 73-79.
  • Handle: RePEc:eee:proeco:v:214:y:2019:i:c:p:73-79
    DOI: 10.1016/j.ijpe.2019.03.007
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    Cited by:

    1. Jia, Yanyan & Fang, Yi & Jing, Zhongbo & Lin, Faqin, 2022. "Price connectedness and input–output linkages: Evidence from China," Economic Modelling, Elsevier, vol. 116(C).
    2. Jing Sun & Jinhui Xu & Xin Cheng & Jichao Miao & Hairong Mu, 2023. "Dynamic causality between PPI and CPI in China: A rolling window bootstrap approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1279-1289, April.

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    More about this item

    Keywords

    Inflation; Vector autoregressive; Bayesian vector autoregressive;
    All these keywords.

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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