Alberto Cavallo Presentation

 

PPPs and Exchange Rates
Evidence from Online Data in Seven Countries

 

Alberto Cavallo
MIT and NBER

March 2015

 

Agenda

  • motivation
  • Data
  • RERs
  • Shock Adjustments(half-lives, persistence comparisons)
  • Relative prices vs E shocks

Conclusions

  • RERs shock adjustment is much faster than previously recorded âžœ months, not years
    • micro data necessary to compare relative price levels
    • Tradable, perfectly matched goods
  • Deviations of RER from normal levels increase pressure to adujust either Relative Prices or E
  • Adjustment margin and speeds vary by country
    • Argentina, Chaina, Australia âžœ mostly prices
    • Brazil, South Africa, UK âžœ mostly E

Motivation

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Motivation

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Motivation

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Online Data and the Billion Prices Project

  • Academic Project at Mit to collect data from retailers that post prices online.
  • Started in 2008, joint with Roberto Rigobon (MIT)
  • Objective: inflation measurement and macro/international research
  • We collect daily data from hundreds of large retailers, for all goods sold, in 50 counteries.

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How does Data Scraping work

  • Every day, our software downloada a public webpage, analyses its HTML code, extract price data, and stores it in a database

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Alternatinve Data Sources for PPPs

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Are Online Price Indices match CPIs

  • Our Online Price Indices match CPIs âžœ implies price change are similar

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But for PPP comparisons, price levels are key.

Are Price Levels Different?

  • Cavallo (2015) âžœ simultaneous random sampling of online and offline prices using barcode-scanning app and crowdsourced workers

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Are Price Levels Different?

  • Cavallo (2015) âžœ simultaneous random sampling of online and offline prices using barcode-scanning app and crowdsourced workers

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PPP Series with Online Data

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Methodology

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A Country-level RER For tradable goods

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Methodology

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Methodology

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Methodology

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Methodology

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Methodology

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Methodology

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Aggregate Results

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Aggregate Results

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Aggregate Results

  • Most RERs appear to fluctuate around certain levels (relative PPP)
    • Are these levels reasonable
    • Comparison to ICP

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Rates of Convergence and Half-lives of RERs Shocks

  • There is much faster mean reversion in these RERs than in the literature

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  • Why faster adjustment?
    • Micro date more volatile âžœ control for sale events, stockouts
    • Only tradable goods
    • Identical Goods across country âžœ perfect matching

Co-movement in Relative prices and E

  • Deviation of RER from normal level increase pressure to adjust

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  • Adjustment may come through relative prices or the exchange rate
    • Magnitude persistence of each shock could matter
    • Implies that E co-moves with Pus/PLc the PPP exchange rate

Argentina

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Brazil

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UK

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Australia

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China

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South Africa

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A Vector Error Correction Model

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Error Correction Model

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Extension

  • We can further:
    •  Restrict the analysis to periods when the deviation is big (RERs over a given threshold)
      • Speeds of adjustment increase
    • Distinguish between devation in one sector/product vs deviation multiple sectors/products
      • Single-sector Deviation (eg gas prices) tend to be corrected via prices, while multi-sector deviations leads to mecro adjustments via the nominal exchange rate


Conclusions

  • RERs shock adjustment is much faster than previously recorded months, not years
    • Micro-date necessary to compare relative price levels
    • Tradable perfectly matched goods
  • Deviation of RER from normal levels increase pressure to adjust either relative price or E
  • Adjustment margin and speeds vary by country
  • Argentina, China, Australia mostly Prices
  • Brazil, South Africa, UK mostly E


Using CPIs:Relative Prices and E

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Devation in Many Products

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