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

Motivation

Motivation

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.

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

Alternatinve Data Sources for PPPs

Are Online Price Indices match CPIs

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

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

Are Price Levels Different?

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

PPP Series with Online Data

Methodology

A Country-level RER For tradable goods

Methodology

Methodology

Methodology

Methodology

Methodology

Methodology

Aggregate Results

Aggregate Results

Aggregate Results

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

Rates of Convergence and Half-lives of RERs Shocks

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

• 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

• 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

Brazil

UK

Australia

China

South Africa

A Vector Error Correction Model

Error Correction Model

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

Devation in Many Products

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