Back Matter
  • 1 https://isni.org/isni/0000000404811396, International Monetary Fund

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ANNEX

Table A1.

Import Price Data Sources

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Source: Authors’ compilation.Note: Local currency denotes whether the import price data is directly reported in local currency by the source.
Table A2.

Estimates of Pass-through from Bilateral Exchange Rate

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Note: Beta represents the cumulative response of headline consumer prices to a 1-percent innovation in the nominal bilateral exchange rate vis-à-vis the U.S. dollar after 12 months and 24 months respectively, estimated with monthly data over 2000–2015. The import content corresponds to the share of household final consumption that is imported (including direct imports and the import content of domestically produced goods consumed locally) averaged over the same sample (2000–2015).
°

The authors thank JaeBin Ahn, Hamid Faruqee, Alexander Guarín, Dora Iakova, Andrew Powell, Pau Rabanal, Alejandro Werner, and seminar participants at the IMF, the IDB’s Financial Stability and Development group seminar on “Risks of Currency Depreciation” in Montevideo, the LACEA 2016 Annual Meeting in Medellín, and the XXI CEMLA Annual Meeting of the Central Bank Researchers Network in Brasilia for valuable comments and suggestions.

1

Burstein, Eichenbaum, and Rebelo (2005) show that the usual decomposition of consumer prices into tradeable and non-tradeable components that relies on retail prices can be misleading for pass-through analysis. The reason is that the retail price of tradeable goods includes two sizeable non-tradeable components: distribution costs—including wholesale and retail services, marketing, advertising, and local transportation services—and local goods that are produced only for the local market. These components reflect the pricing of locally-produced goods and services that are unlikely to be arbitraged in international markets, while prices of imported goods at the border better capture the pricing behavior of “pure” traded goods.

2

Theoretical work has also argued that, as average inflation or inflation volatility increase, so does exchange rate pass-through to aggregate prices—although at a decreasing rate—as firms adjust prices more frequently (Devereux and Yetman, 2010). Bouakez and Rebei (2008) estimate a dynamic general equilibrium model for the Canadian economy and conclude that the decline of consumer price pass-through can be largely attributed to the adoption of inflation targeting.

3

Devereux, Engel, and Storgaard (2004) argue that improvements in the monetary environment may lead to lower pass-through to import prices, as foreign firms may choose the currency of invoicing by taking into account monetary performance in the importing economy. Campa and Goldberg (2005) and Frankel, Parsley, and Wei (2012) found that while higher inflation and exchange rate volatility are associated with higher pass-through to import prices, they are not of first-order importance in explaining its cross-country and time variation.

4

Section III provides additional details on the sample and the construction of the variables used in the analysis.

5

For completeness, we report pass-through estimates from the bilateral exchange rate against the U.S. dollar in Table A2.

6

See Jordà (2005) for a discussion of each aspect of the method.

7

Note also that the local projections method does not impose smoothness on the impulse response, as is the case under a traditional VAR approach, allowing us to uncover any non-linearity in the impulse response function. Local projections also accommodate estimation of other non-linearities and interactions. See Caselli and Roitman (2016) for an application to non-linear exchange rate pass-through in emerging economies.

8

This procedure has been implemented in a different context by Faust and Wright (2011), who show that augmenting a forecasting model with ex-post forecast errors observed between t and t + h improves forecast accuracy by reducing the variance of the error term.

9

Teulings and Zubanov (2014) show that not controlling for innovations in the regressors between periods t and t + h when estimating the impulse response at horizon h can bias the local projection estimates of the impulse response. However, innovations in those regressors are included in the error term, which means that augmenting the regression with the residual from the previous stage regression (h – 1) can approximate the solution proposed by Teulings and Zubanov (2014) to address this problem.

10

Since we have defined the dependent variable in our regression equation (1) in cumulative terms, the value of the cumulative impulse response is provided directly by the estimate of β0h.

11

The sample for Argentina uses data from January 2000 to December 2010, before a gap between the official and the parallel exchange rate emerged. CPI data after December 2006 corresponds to private analysts’ estimates.

12

While the focus on multilateral exchange rates is well justified and common in the literature, we report the pass-through estimates from the bilateral exchange rate against the U.S. dollar in Table A1. More precisely, we substitute neert in equation (2) with et, the natural logarithm of the country’s bilateral exchange rate.

13

Devereux, Engel, and Storgaard (2004) argue that agents will choose to price their goods in the currency that most reliably holds its value. Accordingly, delivering price stability is expected to trigger an endogenous fall in the pass-through of the exchange rate to import prices.

14

The sample of countries with available import price data, the data sources, and the currency in which the original data is reported, are documented in Table A1.

15

While some point estimates for pass-through to import prices are slightly above one in our sample (similarly to findings in Choudhri, Faruqee, and Hakura, 2005; and Ca’Zorzi, Hahn, and Sánchez, 2007), complete pass-through cannot be rejected in those cases.

16

Imports within the euro area are included in our measures of import content. One could expect this would worsen the divergence between the import content and the empirical estimates. However, the estimated pass- through for advanced economies remains below the benchmarks even when euro area countries are excluded.

17

We use the inverse of the variance of the estimated pass-through coefficient as weights to give more weight to those coefficients estimated more precisely in the first-stage regressions. We restrict the sample to those countries that have data for all variables in Xi,τ.

18

Disagreement among forecasters also captures factors besides monetary policy performance. The variability of shocks affecting the economy is also expected to increase disagreement among forecasters. However, Dovern, Fritsche, and Slacalek (2012) show that the relationship between disagreement and central bank independence is robust to controlling for macroeconomic volatility.

19

Since only inflation-targeting central banks announce explicit targets for inflation, the sample shrinks considerably.

20

We use the inverse of the variance of the estimated pass-through coefficient as weights to give more weight to those coefficients estimated more precisely in the first-stage regressions. We restrict the sample to those countries that have data for all variables in Xi,τ.

21

The sample is smaller than that of Tables 3 and 4 since fewer countries have monthly import price data for the whole period.

Monetary Policy Credibility and Exchange Rate Pass-Through
Author: Mr. Yan Carriere-Swallow, Bertrand Gruss, Mr. Nicolas E Magud, and Mr. Fabian Valencia