Daily exchange rate pass-through into micro prices

ABSTRACT

This article estimates the exchange rate pass-through (ERPT) by using good-level daily data on wholesale prices of imported agricultural products, where the identification is achieved by using daily data on the domestic inflation rate. The results of standard empirical analyses are in line with existing studies that employ lower frequencies of data by showing evidence for incomplete daily ERPT of about $5$%. The key innovation is achieved when nonlinearities in ERPT are considered, where ERPT is doubled to about $10$% when daily nominal exchange rate changes are above $0.55$%, daily frequencies of price change are above $3.12$% and storage life of a product is above $10$ weeks. Important policy implications follow.

KEYWORDS:

• Daily agricultural prices
• exchange rate pass-through
• good-level analysis

JEL CLASSIFICATION:

• E31
• F14
• F31

Acknowledgments

The authors thank David Peel, an anonymous referee, participants at the International Monetary Fund’s Western Hemisphere Department Seminar in Washington, D.C., the 37th MEEA/ASSA Annual Meeting in Chicago, IL, the 86th Annual Meetings of Southern Economic Association in Washington, D.C., and seminar participants at Florida International University for their helpful comments and suggestions. The usual disclaimer applies.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed here.

Notes

¹ This article follows the textbook definition of ERPT as described in Knetter (1997), which is the percentage change in local currency import prices resulting from a 1% change in the exchange rate. Accordingly, complete ERPT corresponds to a value of $1$ (or $100\mathrm{\%}$), while incomplete ERPT corresponds to values below $1$ (or $100\mathrm{\%}$).

² Another daily data set is by Lott (2013), who has considered daily ERPT evidence based on eBay transactions of US imports from Australia, Canada, Germany, Japan and the UK that constitute a small portion of the overall expenditure in the US without controlling for domestic price changes; in comparison to such online price data, our data set not only represents offline wholesale prices of agricultural products that constitute about 22% of overall consumption expenditure in Turkey (according to the Turkish Statistical Institute) but also controls for domestic price changes.

³ The web page of Istanbul Metropolitan Municipality is www.ibb.gov.tr. The descriptive statistics are provided in Appendix Table A.1.

⁴ In particular, for each day, we calculate the average percentage change in good-level prices, after ignoring the outlier goods defined as the ones that have price changes more than 2 SD away from the average inflation.

⁵ ADF and Phillips–Perron unit root tests suggest that the level of prices and exchange rates are nonstationarity, but both variables become stationary after considering daily log changes; this is the reason behind our specification. Cointegration tests based on the Engle–Granger procedure suggest that residuals are I(0).

⁶ The results based on higher number of lags have much higher AIC and BIC criteria (and thus are not selected) that have been skipped to save space, but they are available upon request.

⁷ In order to focus on daily ERPT, we use a slightly different approach compared to Gopinath, Itskhoki, and Rigobon (2010), who consider cumulative changes in nominal exchange rates when a price change is observed, whereas we consider daily (instantaneous) changes in the nominal exchange rate.