We analyse the economic conditions (the “shocks”) behind currency movements and show how that analysis can help address a range of questions, focussing on exchange rate pass-through to prices. We build on a methodology previously developed for the UK and adapt this framework so that it can be applied to a diverse sample of countries using widely available data. The paper provides three examples of how this enriched methodology can be used to provide insights into pass-through and other questions. First, it shows that exchange rate movements caused by monetary policy shocks consistently correspond to significantly higher pass-through than those caused by demand shocks in a cross-section of countries, confirming earlier results for the UK. Second, it shows that the underlying shocks (especially monetary policy shocks) are particularly important for understanding the time-series dimension of pass-through, while the standard structural variables highlighted in the previous literature are most important for the cross-section dimension. Finally, the paper explores how the methodology can be used to shed light on the effects of monetary policy and the debate on “currency wars”: it shows that the role of monetary policy shocks in driving the exchange rate has increased moderately since the global financial crisis in advanced economies.
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This literature includes Bils (1987), Dornbusch (1987), Krugman (1987), Klein (1990), Rotemberg and Woodford (1999), and more recently Corsetti et al. (2009) and Gilchrist and Zakrajsek (2015). Nakamura and Steinsson (2013) discuss the link between the micro- and macro-literature. For discussion of the implications for pass-through, see Burstein and Gopinath (2014), Campa and Goldberg (2005) and Ito and Sato (2008).
The shock-based approach in Shambaugh (2008) did not receive the attention it deserved, and its insights were not incorporated in subsequent research and estimation of pass-through. The framework developed in Forbes et al. (2018) improves on Shambaugh (2008) in three ways: (1) uses different shocks that are more closely linked to the theoretical literature and more straightforward to interpret for empirical analysis; (2) uses advances in SVAR methodology to better identify these richer shocks behind exchange rate fluctuations and better mitigate concerns with weak identification in SVAR models with long-run restrictions; and (3) considers the effects of these shocks on a broader set of variables—including interest rates and foreign export prices. These improvements link the empirical model more closely to the theoretical literature and make the framework more applicable and usable for forecasting key economic outcomes such as inflation.
See “Using rules of thumb for exchange rate pass-through could be misleading” by Forbes (2015), voxeu.org, 12 February 2016.
The de facto floating exchange rate regime categories in the IMF’s classification are “floating” and “free floating”. See the IMF’s 67th Annual Report on Exchange Rate Arrangements and Exchange Restrictions for 2015. Switzerland is the one country in our sample that is not continuously classified as “floating” since 2006 due to the ceiling on the Swiss franc from September 2011 to January 2015, but excluding it from the analysis does not impact the key results.
The last term is included to control for changes in domestic conditions which could affect prices directly rather than just through the exchange rate. We also estimated 27 variations of Eq. (1), including with no control for GDP growth and different controls (such as short-term interest rates, oil prices, and one to four lags of the dependent variable). The results were generally stable and similar to the baseline. See “Appendix B” in Forbes et al. (2017).
Despite the short sample periods of only 6 years, the differences in the estimated pass-through coefficients over these short periods are statistically significant and economically meaningful in some cases but not always. More specifically, the estimated pass-through coefficient for Japan, Korea, Brazil, Chile, Mexico, Poland, Romania, South Africa, Turkey and Uruguay differ significantly either between some of the 6-year windows or relative to the “long-sample” pass-through estimate (or both).
In particular, Forbes et al. (2018) only identifies global temporary and permanent shocks, while we add a third global shock (for monetary policy). This greater diversity of global shocks is important when extending the model to diverse countries to capture different monetary policy regimes and different effects of global commodity prices.
For additional references and evidence on these assumptions, see the model and discussion in Forbes et al. (2018).
At the 95% confidence level as indicated by the 2 standard error confidence bands of the mean group estimates in Fig. 2.
It is not surprising that global shocks have varied effects on different countries in our sample. Commodity and oil price movements (which are correlated with global demand and supply shocks) are an important driver of the exchange rate for some countries—but in some cases show positive correlations and in others negative, based on whether the country is a commodity importer or exporter. We allow for these heterogenous effects in the model by imposing no restrictions on the impact of global shocks on domestic prices, interest rates or the exchange rate in both the short and long run.
More precisely, the median pass-through for individual countries four quarters after the shock is negative for all countries; the ratio remains negative eight quarters after the shock for all countries apart from Mexico (where pass-through is negative at horizons of 1 to 7 quarters but switches sign in the eight quarter). At a horizon of eight quarters after the shock, the effect is significantly negative at the 68% confidence level for 14 out of the 26 countries in our sample; four quarters after the shock, the effect is significantly negative at the 68% confidence level for 19 of 26 countries.
These two shocks are not only most often significant, but as discussed in Sec. 3, explain the largest share of the variance of exchange rate movements and have consistently different effects on pass-through across the sample.
The explanatory variables are also re-calculated to correspond to the 6-year windows over which the pass-through coefficients are estimated. The shock contributions, for instance, are now calculated as the sum of squared contributions of each shock to the historical decomposition of the exchange rate divided by the sums of squared contributions of all shocks within each 6-year window. The structural control variables in the baseline specification in Table 1 are the standard deviation of quarterly inflation and the average share of imports in GDP over the respective 6-year windows.
See Forbes et al. (2017) for a subset of these results.
Including the impact of demand shocks has no meaningful impact as the relevant coefficient is almost 0.
Possible explanations are that unconventional policies work more through the term premium (and therefore long-term securities) and/or are interpreted as a longer-term commitment than conventional policy. See Brainard (2017) for a summary of arguments, and see Neely (2015), Glick and Leduc (2015), Curcuru (2017), Ferrari et al. (2017) and Hatzius et al. (2017) for analysis if the effects of unconventional monetary policy are different than conventional policies.
When estimating a panel regression of quarterly contributions of monetary policy shocks to exchange rate movements for the pre-crisis period, and inserting a dummy for each group of countries considered subsequently, none of these are found to be significant. But for the post-crisis period, the dummy for advanced economies not constrained by the lower bound on interest rates is significant at the 95% level.
It is worth noting that some of these patterns change when giving equal weight to all quarterly exchange rate movements, regardless of their size. In this case, the share of all exchange rate movements driven by monetary policy shocks has increased more since the crisis, but this is driven by emerging markets and countries not at the ELB.
The short-term market-based rates we experimented with were the 3-month, 3-month and 6-month, 6-month forward interbank rates. We used two different measures of shadow interest rates: by De Rezende and Ristiniemi (2018) for Sweden and the UK; and by Krippner (2016) for the UK, Switzerland, and Japan.
See Group of Seven (2013), “Statement by G7 Finance Ministers and Central Bank Governors”, February 12, available at: www.g8.utoronto.ca/finance/fm130212.htm.
The main differences between this framework and that in Forbes et al. (2018a, b) are: we include more foreign variables to identify a set of well-defined global shocks alongside the domestic shocks, and that we exclude import prices from the SVAR. Unfortunately, there are not sufficient, reliable data on import prices over the time-series needed to estimate the model with import prices for the larger set of countries. We also do not allow for exogenous shocks to the exchange rate, as it is no longer possible to identify this shock with only four domestic variables.
While sufficient to identify the desired shocks, this set-up relies exclusively on long-run restrictions to differentiate domestic supply shocks from other domestic shocks. Long-run restrictions can lead to weak identification when imposed on relatively short sample periods (see Pagan and Robertson 1998; Faust and Leeper 1997 and Christiano 2007), so we retain the sign restrictions on CPI in our preferred baseline model in the main text.
This means excluding 10 quarterly observations, since using the first two quarters of 2010 requires using the last quarters of 2009 as explanatory variables given our two-lag VAR model.
We include the additional dummies in the cross-section and 6-year windows. The commodity exporters (importers) dummy is based on UNCTAD data (from 1995 to 2015) with commodity exporters (importers) defined as being a net exporter (importer) over the relevant period and commodities defined as SITC 0 + 1+2 + 3+4 + 68.
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The authors would like to thank Ines Nicole Lee for excellent research assistance. The authors are also grateful to Philippe Martin and Robert Kollmann for insightful discussions and to Ambrogio Cesa-Bianchi, Roland Meeks, Marek Raczko and participants at conferences and seminars at the Bank of England, European Central Bank, International Monetary Fund, and Riksbank, for helpful comments and further thank for input from Linda Tesar and two anonymous referees. The views in this paper do not represent the official views of the Bank of England or its Monetary Policy Committee. Any errors are our own.
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Appendix to “International Evidence on Shock-Dependent Exchange Rate Pass-Through”
Appendix to “International Evidence on Shock-Dependent Exchange Rate Pass-Through”
Appendix A: Data Sources and Sample Periods
Appendix B: SVAR Methodology and Country-Specific Results
We modify the methodology developed in Forbes et al. (2018a, b) to derive shock-dependent estimates of pass-through for each of the 26 countries for which we computed reduced-form estimates of pass-through in Sect. 2.Footnote 28 More specifically, we estimate an SVAR with seven variables for each country: changes in nominal trade-weighted exchange rates, consumer price inflation, real GDP growth, short-term interest rates, and changes in trade-weighted foreign GDP and consumer prices as well as foreign short-term interest rates. Detailed definitions are available in "Appendix A", and all variables are at a quarterly frequency. We allow for seven shocks that can affect each country’s exchange rate: three domestic shocks (supply, demand, and monetary policy) and three global shocks (global supply, demand, and monetary policy), and we leave one shock unidentified so as to capture any shocks that don’t satisfy the characteristics of the six well-identified shocks.
In order to identify the shocks, we use a set of standard and straightforward long- and short-run zero restrictions and sign restrictions, summarized in Appendix Table 4. We impose this identification using an algorithm based on Rubio-Ramirez et al. (2010) and Binning (2013) and estimate the model using Bayesian methods with Minnesota priors.Footnote 29 The resulting “shock-dependent” estimates of pass-through tend to be very close to the reduced-form estimates of pass-through (from Sect. 2). For a detailed comparison of the estimates obtained using these two different methods, see Fig. 6 in Forbes et al. (2017).
Contributions of Shocks to Exchange Rate Fluctuations for Individual Countries and Over Time
Appendix C: Sensitivity Tests
This appendix discusses a series of sensitivity tests assessing if various assumptions and choices significantly affect the main results. In almost all cases, the choices do not significantly affect key results—so we will only briefly summarize the tests.
Different Approaches to Estimating Long-Sample Pass-Through
First, we examine if changes in our baseline specification of reduced-form pass-through significantly affect key results. Section 2 of the paper discussed the parsimonious distributed lag regression in Eq. (1) which is standard in the literature and used as our baseline. We also estimate 26 alternative specifications (using different control variables and lag structures), which are detailed in "Appendix B" of Forbes et al. (2017a, b). The results for pass-through from these sensitivity tests are not systematically different from the baseline estimates, but it is possible that certain controls in the baseline specification could affect pass-through estimates in ways that are correlated with the shocks behind exchange rate movements. For example, controlling for GDP growth in the first stage could capture some of the impact of the shocks (such as a demand shock), and thereby make it more difficult to find a significant role for the shock variables in the second-stage estimates discussed in Sect. 4. Or, adding oil prices to the first stage could capture some of the impact of global shocks, and therefore reduce the significance of the global supply and demand shock variables in the second-stage estimates. To test these hypotheses, we estimate Eq. (1) with no control for GDP growth, and then with an additional control for oil prices. In each case, the main results from the second-stage regressions are very similar, but with small changes that follow these predictions of the correlations between GDP growth, oil prices, and the shock variables. The shock variables are somewhat more often significant when the control for GDP growth is excluded from the first stage, and the shock variables are slightly less often significant when the control for oil prices is included in the first stage. These changes are very marginal, however, and the general conclusions do not change.
Next, we note that the reduced-form estimates of exchange rate pass-through presented in Fig. 1 are sometimes imprecisely estimated and can take on implausibly high values for some countries. Our generalized least square estimates in the second stage utilize the variance of the estimated coefficients to attach lower weight on the more imprecise estimates. This approach, however, does not address issues arising from potentially biased estimated coefficients with narrow confidence bands. The high pass-through estimate of 67% for Turkey in Fig. 1 stands out as the most notable outlier with narrow confidence bands. We therefore ran all second-stage regressions in Table 1 without the estimates for Turkey. The key results were unchanged, but some estimated coefficients became slightly larger or more significant. For example, the trade openness in Table 1 was usually significant (at least at the 10% level). The coefficients on our shock variables were unchanged.
Different Shock Identification Assumptions and Excluding the Crisis Period
To more precisely identify domestic shocks, our baseline SVAR model imposes sign restrictions on the response of domestic prices within the quarter a shock occurs (see Appendix Table 4). We then study the response of domestic consumer prices relative to the exchange rate eight quarters after that shock when we discuss shock-dependent pass-through. To ensure that our results are not generated by assumption, we drop all sign restrictions on the domestic CPI and re-estimate the SVAR model relying only on sign and zero restrictions on other variables to identify the six shocks of interest.Footnote 30 Figure 9 shows that the results do not change when we modify the sign restrictions imposed on the CPI; pass-through is still significantly lower for domestic demand shocks compared to domestic monetary policy shocks. The second-stage regressions based on these estimates and explaining variations in pass-through are also broadly similar, with the coefficients on the shock variables of similar magnitudes, albeit the marginally significant coefficients in some cross-country regressions become insignificant.
Second, our sample includes the financial crisis—an episode when many countries experienced particularly large exchange rate movements. To check if the estimated differences between pass-through after different shocks are driven by the crisis only, we re-estimate the SVAR without data for 2008 and 2009.Footnote 31 The average shock-dependent pass-through across countries is plotted in Fig. 10 and is similar to our main result. Indeed, the relative responses of domestic prices and exchange rates are less dispersed across countries when the crisis is excluded from the estimation period, as indicated by the somewhat narrower confidence bands. Estimating the same second-stage regressions as in Sect. 4 is only feasible across countries, but not over the 6-year periods within countries (as these cover the crisis). The resulting coefficients on shock variables are similar to the baseline, but not significant.
Different Measures of “Structure” Variables
Many of the structural country characteristics that the previous literature has highlighted as affecting pass-through are highly correlated with each other. This, especially when combined with our limited sample size, makes it difficult to control for many variables simultaneously. Therefore, we estimate the main regressions using a range of different possible “structural” variables and show a sample of the results below. Table 5 presents an extended version of columns 1–7 of Table 1, where each set of structural characteristics is first included simultaneously and then kept in the more parsimonious regressions including the shocks only if significant. Table 6 presents a similar set of regressions, controlling for more structural characteristics, but using 6-year windows, as in columns 8–14 of Table 1. As discussed in the text, these highlight the consistent importance of inflation volatility for explaining pass-through estimates across countries and over time, supporting our choice of variables in the baseline regressions presented in the main text of this paper.
Different Measures for Monetary Policy
One challenge during the period in our analysis is measuring changes in monetary policy, as several economies were at their effective lower bound (ELB) for their policy interest rate and adjusted monetary policy through other tools. More specifically, in the wake of the global financial crisis, the central banks of Canada, Israel, Japan, UK, Sweden, and Switzerland all lowered their interest rates to levels below 1% and some of them introduced additional unconventional easing measures such as quantitative easing or forward guidance. Given these policies, official interest rates are likely to miss changes in the monetary policy stance, affecting our identification of monetary policy shocks.
To address this potential issue, we estimated the SVAR model using several alternative interest rate series for these countries. First, we re-estimate the SVAR model for Japan and the UK—two countries in our sample that used quantitative easing most extensively—using shadow interest rates developed by Krippner (2016). The resulting mean group estimates of the relative responses of domestic consumer prices and the exchange rate, conditional on each shock identified in our SVAR, are almost identical to the baseline results using central bank rates shown in Fig. 2 of the paper. The second-stage regression results for the role of the shocks in explaining differences in exchange rate pass-through across countries and over time are reported in the left of Tables 7 and 8 alongside the baseline results from Table 1 of the paper for the cross-country results (columns 2, 3, and 7) and time-series results (in columns 9, 10, and 14). The results are almost identical.
As no shadow rates were available for Canada, Israel, Sweden, and Switzerland, we also performed two more robustness checks using financial market forward rates of different maturities to proxy for the stance of monetary policy. We re-estimated the SVAR model for the six countries that were near the zero lower bond using 3-month, 3-month and 6-month, 6-month forward rates from interest rate swap markets. The resulting mean group estimates of shock-dependent exchange rate pass-through are again very similar to the baseline results presented in the paper. Second-stage regression results for the role of shocks in explaining variations in reduced-form pass-through are also similar and shown to the right of Tables 7 and 8.
In a final set of sensitivity tests, we explore the role of commodity exporters/importers and emerging markets. As noted, these different groups of countries often have different characteristics of pass-though, such as emerging markets generally having higher pass-through and commodity exporters being differentially affected by permanent global shocks than commodity importers. To better explore these relationships, we included an emerging market dummy variable in the second-stage regressions wherever possible, and then a dummy variable capturing whether the country is a commodity exporter or importer.Footnote 32 These additional dummy variables are usually insignificant and do not change the key results. We also considered the possibility that the impact of the shocks we focus on differ depending on whether the country considered is a net commodity exporter or importer. To test for these types of effects, we include an interaction between the commodity exporter/importer dummy and the shock contributions in the second-stage regressions. The coefficients on the interaction term were not significant and the key results are robust. A more thorough analysis of the importance of commodity price shocks for exporters and importers of those commodities, and how these may affect pass-through, requires a VAR model which identifies commodity shocks separately. While this could potentially be important for some countries, e.g. New Zealand as found in Parker and Wong (2014), we leave that for future research.
Finally, we explore whether changes in the VAT/GST could explain some of the sharp changes in pass-through that occur for specific countries over time. For our main analysis, we measure the pass-through of exchange rate movements to the consumer price index (not adjusted for changes in VAT or other government policies) as this measure is most widely available across countries. For several countries, however, there is a period when estimated pass-through changes around the same time that there were changes in the VAT (such as in Australia in 2000, New Zealand in 2010, and the UK in 2008, 2010 and 2011). Therefore, for these three countries, we re-estimate the rolling regressions of pass-through, but add dummy variables for increases or decreases in the VAT/GST. This flattens the short-term jumps in estimated pass-through experienced by New Zealand from 2011 to 2014, but it has minimal effect on the pass-through estimates for Australia and the UK.
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Forbes, K., Hjortsoe, I. & Nenova, T. International Evidence on Shock-Dependent Exchange Rate Pass-Through. IMF Econ Rev 68, 721–763 (2020). https://doi.org/10.1057/s41308-020-00124-2
- Exchange rate
- Price level
- Monetary policy
- Currency wars