Peru: Selected Issues
Author: International Monetary Fund. Western Hemisphere Dept.
Publication Date:
13 Jan 2020
eISBN:
9781513526126
ISBN:
9781513526126
Language:
English
Keywords:
ISCR; CR; Peru; rate; policy rate gap; dollarization; inflation targeting framework; policy instrument; Policy rate decision; transmission mechanism; monetary policy rate; macro-prudential policies; Central bank policy rate; Reserve requirements; Real exchange rates; Value-added tax; Global; Caribbean; Asia and Pacific; inflation gap; monetary policy rate gap; Capital flows; Credit gaps; FX gap
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Selected Issues

Abstract

Selected Issues

Peru’s Inflation Targeting Framework—How Far From Orthodoxy?1

We benchmark Peru’s multi-instrument inflation targeting framework with Chile’s—a regional peer that follows an orthodox inflation targeting regime. These frameworks have performed similarly in terms of output growth and inflation, and their volatilities. We find that Peru’s framework appears to differ from the benchmark primarily along two dimensions: policy rate setting and real exchange rate outcomes. Policy rate decisions in Peru have been affected by reserve requirement settings, particularly in response to capital flow shocks. Policy rate decisions, foreign exchange intervention, and reserve requirements seem to explain differences in real exchange rate outcomes. Nonetheless, deviations from orthodoxy were largely modest and transitory.

A. Introduction

1. It is well documented that Peru relies on a multi-instrument policy framework to manage macroeconomic fluctuations (e.g., Armas and Vega, 2018). 2 Peru’s inflation targeting framework uses a traditional policy rate complemented by two other main policy instruments: foreign exchange intervention (FXI) and reserve requirements (RRs). As in the traditional inflation targeting practice, Peru’s central bank adjusts its policy rate to bring inflation back to the target range. FXI is used to manage exchange rate fluctuations in order to mitigate balance sheet risks associated with the country’s still high degree of financial dollarization. Finally, RRs are used as a tool to reduce financial dollarization, as well as to partially offset the impact of changes in external credit conditions.

2. It is much less understood, however, to what extent this multi-instrument framework drives Peru’s policies and economic outcomes away from an orthodox model. For instance, does the coexistence of multiple objectives hamper the implementation and performance of the inflation targeting framework? Or, does the deployment of multiple instruments generate distortions that can cause misalignments and sow the seeds for vulnerabilities to emerge in the short or medium terms? More generally, how far are Peru’s policy instruments and/or economic outcomes from those of a traditional inflation targeting framework, which relies only on adjustments to the monetary policy rate and on a freely-floating exchange rate?

3. In this chapter we try to shed light on this issue by benchmarking Peru with Chile—a regional peer that uses an orthodox inflation targeting framework. Chile is a particularly useful benchmark for several reasons. Both countries are inflation targetters and share important similarities, namely in their export structure dominated by mining and agricultural products). However, at the same time they have important differences in their policy frameworks, with Chile relying almost exclusively on a flexible exchange rate regime and adjustments to the monetary policy rate.

B. Analytical Strategy

4. We focus our attention on the main differences in the management of monetary instruments and economic outcomes. As illustrated in the next section, the differences in monetary policy management go beyond the obvious discrepancy in the number of instruments used in each country and extend to how intensively the monetary policy rate is used. In addition, while growth and inflation outcomes appear relatively similar, the behavior of the real exchange rate differs. In this context, we explore: (i) to what extent does the use of FXI and RRs affect the adjustment of the monetary policy rate? and (ii) to what extent does Peru’s framework generate policy-induced deviations of the real exchange rate (RER)? The first question goes to the core of the discussion on whether a framework with multiple objectives and multiple instruments can affect the capacity of the central bank to properly implement its de jure monetary framework. The second question also goes to a central issue, namely, whether the deployment of multiple instruments can generate distortions—such as real exchange rate misalignments—which could eventually translate into misallocation of resources or a vulnerable external position.

C. Economic Developments and Policy Instruments—An Overview for Peru and Chile

Economic Developments

5. Peru and Chile both exhibited strong economic performance in the period under study, with perhaps the most salient difference being their (real and nominal) exchange rate developments. More specifically, as illustrated in Figure 1 for the period January 2002-March 2019:

  • Terms of trade had very similar trends and cycles, as well as almost identical volatility (both in terms of standard deviation and coefficient of variation). Net capital inflows were on average somewhat higher for Peru, with a monthly average equivalent to 0.2 months of imports vis-à-vis an average of nearly zero for Chile. Both series had a similar volatility (measured in terms of standard deviation and range, since the coefficient of variation is not very appropriate in this case given the nearly zero mean for Chile).

  • Real GDP growth was on average higher for Peru (5.3 versus 3.9 percent) while inflation was on average lower (2.7 versus 3.2 percent). Volatility was lower for Peru for both series (as measured by the coefficient of variation). In addition, one can observe a wider range for inflation in Chile, which is driven by the fluctuations observed in the months around the global financial crisis (GFC).

  • Exchange rate volatility was also lower for Peru. Chile’s RER exhibited a coefficient of variation of 5.6 and a range of about 30 points, while both figures were lower for Peru (with the former at 4.1 and the latter at 17). These results were primarily the reflection of higher nominal exchange rate volatility, with Chile’s nominal exchange rate exhibiting a coefficient of variation of 13.5 and a range of 46, and the values for Peru being 9.1 and 31, respectively. It is striking, at least ex-post, that deviations between the two nominal series tended to be corrected by adjustments in Chile’s nominal exchange rate and that the inflexion points of the two series are largely the same.

Figure 1.
Figure 1.

Peru and Chile: Selected Macroeconomic Series

Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A003

Sources: BCRP, Haver, IMF INS database, and IMF staff estimates.

Policy Instruments

6. As anticipated in the previous sections, the behavior of policy instruments differed in a fundamental way—with a more active use of the policy rate in Chile and FXI and RRs in Peru. As illustrated in Figure 2, the monetary policy rates had very similar cycles (both in nominal and real terms) and averages (although the average real rate was about 40 basis points higher in Peru). However, volatility was higher in the case of Chile, suggesting a more active use of this instrument. On the other hand, volatility of FXI and RRs was significantly higher in Peru, with Chile barely using these instruments (FXI was used in just three episodes of relatively small scale while reserve requirements were not changed at all).

Figure 2.
Figure 2.

Peru and Chile: Policy Instruments

Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A003

Sources: BCRP, Haver, Larrain and Saravia (2018), and IMF staff estimates.

Other Policy and Structural Differences

7. Factors other than policy instruments may play a role in explaining policy differences and outcomes. In particular,

  • Fiscal FX transactions. Government ownership of mining/hydrocarbon resources is higher in Chile than in Peru (where it is mainly private). During the commodity boom, Chile’s government accumulated a large stock of external assets, which alleviated pressures on the FX market.

  • Restrictions to capital flows. While Chile had already eliminated some of the capital control measures imposed in the 1990s, it seems to have nonetheless remained more restrictive in this area than Peru (see, for instance, the index developed in Fernandez, Klein, Rebucci, Schindler, and Uribe (2016)). This could explain why capital flows were lower and less volatile in Chile than in Peru.

  • Structure of the financial sector. Banks are relatively more dominant in Peru’s financial sector than in Chile’s, which may explain why RRs could be more effective in the former than in the latter.

8. The empirical analysis conducted below, however, remains relevant and may be relatively robust to these factors. The potential impact of fiscal interventions poses perhaps the most significant hurdle for our analysis, as they may have exchange rate implications not captured by our variables. However, it is important to note that some FX transactions by Peru’s Fiscal Stabilization Fund may have also contributed to mitigate pressures on the FX market. In addition, the tax stability arrangements between Peru and mining companies may have had a similar effect by limiting the companies’ tax liabilities and the amount of FX they needed to sell. This factor would bias our results towards non-significance. The other two factors mentioned above—restrictions to capital flows and structure of the financial sector—can be considered in our empirical analysis as country-specific factors. To the extent that their time variability is limited, then their impact would be captured by the constant term in our regression (see specification in the next section).

D. Estimation Strategy and Data

9. In order to estimate the policy questions raised in section B, we analyze the deviations of the relevant Peruvian variables vis-à-vis their Chilean counterparts. For this, our starting point is to assume that the relevant variables for each country behave as denoted in equation (1) below. By taking differences for the two countries we arrive at equation (2), which indicates that the difference in the variable of interest (Y) can be explained by a set of economic fundamentals (X) and a set of policy instruments (Z). One advantage of using the formulation in equation (2) over the formulation in equation (1) is that the former helps control for the impact of unobservable common shocks (i.e., the v term in equation (1)).

Yi,t=αi+βXi,t+γZi,t+vt+ui,t(1)
Yi,t−Yj,t=(αi−αj)+β(Xi,t−Xj,t)+γ(Zi,t−Zj,t)+(ui,t−uj,t)(2)

All variables are measured monthly. The sample period is January 2003-March 2019, but most data used in the regressions start only in September 2003, which is the first month when the policy rate for Peru becomes available. The real policy rate is calculated by subtracting 12-month inflation expectations from the nominal policy rate. Finally, the FXI variable is deflated by imports of goods and services (hence, the unit of measure becomes central bank intervention in months of imports) to account for differences in size of the economies (both over time and between each other) and facilitate the analytical interpretation of the associated coefficient (for details on data sources and definitions, see Annex I).

For the rest of the analysis, we use the term “gap” to denote the differences between Peru’s economic variables with respect to their Chilean counterparts. Naturally, the policy rate gap and the real exchange rate gap will occupy a central place in the analysis given the two questions we are tackling. But other gaps (inflation, terms of trade, FXI, etc.) will also be considered.

E. Analysis and Regression Results

Policy question 1: to what extent does the use of FXI and reserve requirements affect the adjustment of the monetary policy rate?

10. Before evaluating the regression results, it is useful to analyze the interest rate gap. The real interest rate gap is stationary and has an average of around 0.4 percentage points, reflecting the previously mentioned fact that the average real policy rate was higher in Peru by that amount. In addition, there were very large gaps (in both directions) around the GFC, but these were short lived (text chart).

uA03fig01

Peru and Chile: Real Monetary Policy Rate Gap

(Percentage points)

Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A003

Sources: Haver and IMF staff estimates.

11. The results in Table 1 show that the real interest rate gap is affected by both fundamentals and one policy instrument—RRs. Table 1 shows the results of a regression in which the real interest rate gap is explained by: (i) the inflation gap—under the hypothesis that the country with relatively higher (lower) inflation will tend to increase (reduce) its real rate in order to maintain inflation within the inflation target; and (ii) the policy instrument gaps—which are used to address the policy question of interest. The results indicate that a higher inflation gap will indeed induce the country to set a higher real interest rate—with the coefficients (Table 1, third column) indicating that a one percentage point increase in the inflation gap will translate into a 0.11 percentage points increase in the real policy rate gap. The results also indicate that reserve requirements appear to be a substitute for policy rate adjustments (given the negative sign of the coefficient). A 10- percentage point increase in reserve requirements translates into a reduction of 0.2 percentage points in the real policy rate. This link is also well illustrated in the text chart, which shows that periods of high (low) real interest rate gap were associated with period of low (high) RR gap (please note the inverted scale in the chart), particularly before 2015. On the other hand, FXI does not appear to influence the interest rate setting, as the coefficient of this variable is not statistically significant.

Table 1.

Peru and Chile: Regression Analysis. Estimating the Effect of Policy Instruments on the Real Monetary Policy Rate Gap

article image
Source: Author’s calculations.Notes: Robust Least Squares regressions for the period Dec. 2002-Mar. 2019.But most regressions start in September 2003, when Peru’s policy rate becomes available.Robust standard errors in parentheses. *, **, ***, denote significance at 10, 5, and 1 percent, respectively.
uA03fig02

Peru and Chile: Real Monetary Policy Rate and Reserve Requirements

(In percent points and in percent [RHS])

Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A003

Sources: Haver and IMF staff estimates.

Understanding the Link between the Real Policy Rate and RRs

12. The importance of RRs may be related to the source of the shocks faced by Peru. As illustrated in the text chart, reserve requirements were highly correlated with the capital flows gap. Hence, when faced with the need to tighten monetary conditions in a context of capital inflows, Peru may have preferred to use reserve requirements rather than the policy rate—as increasing the latter could have triggered additional capital inflows.

uA03fig03

Peru and Chile: Capital Inflows and Reserve Requirements

(USD billion, in percent [RHS])

Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A003

Sources: Haver, BCRP, and IMF staff estimates.

13. We can use the relationship between RRs and capital flows to test the robustness of our previous results to some forms of endogeneity problems arising from correlation of the regressors with the disturbance term. Policy instruments are endogenous by nature since they reflect the auhtorities’ decision making process, which takes into account all the available information and encompasses all the existing instruments. As a result, the error term of the dependent variable (in this case the real policy rate gap) could possibly be contemporaneously correlated with the independent variable (in this case the RRs gap). In order to address this possible problem, we instrument the RRs with lagged values (three, six, and nine month lags) of the capital flows gap, which should not be correlated with the error term. The results, which are shown in the fourth column of Table 1, show that the instrumented RR remains a significant explanatory variable—with its coefficient actually increasing—of the real policy rate gap.

14. The relationship between RRs and capital flows may, however, point towards other forms of endogeneity. If capital flows also impacted the real policy gap, then the link between RRs and the real policy rate would be driven by their common dependence on a third variable (in this case the capital flows gap) rather than by a direct effect of one variable over the other. If we include the capital flows gap as a control variable, the RR gap loses its significance (Table 1, fifth column). This likely reflects endogeneity between the RR and the capital flows gap, as well as their their high correlation (0.58). While the endogeneity problems make it difficult to disentangle the precise link between RRs and the policy rate, the analysis above, nonetheless, suggests that RRs seems to have played an important role to respond to capital inflows and that their use seems to have influenced the intesnsity in the use of the policy rate. We return to endogeneity issues later in the chapter.

Policy question 2: to what extent does Peru’s framework generate policy-induced deviations of the real exchange rate (RER)?

15. Before conducting the regression analysis, it is interesting to analyze the patterns of the gaps:

  • The real exchange rate gap measures deviations of Peru’s RER vis-à-vis Chile’s, which means that upward movements denote relative appreciation of Peru’s RER and downward movements relative depreciations (Figure 3, upper chart).3 This gap displays stationary behavior, with deviations from its mean reaching over 12 percentage points several times (and almost 20 points in a couple of cases). The gap was relatively more volatile in the earlier part of the sample, but nonetheless has exhibited significant volatility in the later part.

  • The real exchange rate gap appears to be correlated with the foreign exchange intervention gap and the interest rate gap. Regarding the latter, the middle chart of Figure 3 illustrates that periods of relative appreciation (depreciation) of Peru’s RER are correlated with periods of relatively higher (lower) policy rates, although they have diverged recently. Regarding the link with the FXI gap, the lower chart of Figure 3 illustrates that periods of relative appreciation (depreciation) of Peru’s RER are correlated with periods of relatively higher FX sales (purchases).

Figure 3.
Figure 3.

Peru and Chile: Real Exchange Rate Gap and its Determinants

Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A003

Source: IMF staff estimates.

The fundamentals in our estimation include only the gap in the terms of trade, while the policy variables include: (i) the real policy rate gap, the FXI gap, and the RR gap.

16. Regression results confirm the importance of policy instruments in determining the real exchange rate gap. As illustrated in Table 2, the terms of trade gap does not play a major role since it is never significant in the specifications. On the other hand, all the policy gaps have the correct sign and are statistically significant at the one percent level. More specifically,

  • Increases in the policy rate gap are estimated to lead to appreciations of the real exchange rate. The quantitative impact appears significant, with the coefficient estimated in equation (4) suggesting that a one percentage point increase in the policy rate gap would lead to an almost 3½ percentage points appreciation of the real exchange rate.

  • Decreases in the FXI gap (i.e., sales of foreign exchange) lead to appreciations of the currency. The quantitative impact also appears significant, with foreign exchange sales in the order of one month of imports of goods and services (i.e., the equivalent to almost two percent of GDP) leading to an appreciation of the real exchange rate in the order of over 5 percentage points (based on the estimated coefficient in equation 4).

  • Increases in the RR gap lead to appreciations of the real exchange rate, although the quantitative impact appears more moderate. In particular, the estimated coefficient in equation (4) implies that a 10 percent increase in the reserve requirements leads to a 1.8 percent appreciation of the real exchange rate. Furthermore, as indicated in the analysis of the impact of reserve requirements on interest rate setting, higher reserve requirements will lead to a lower policy rate (hence offsetting the impact of reserve requirements on the real exchange rate).

Table 2.

Peru and Chile: Regression Analysis. Estimating the Effect of Policy Instruments on the Real Exchange Rate Gap

article image
Source: Author’s calculations.Notes: Robust Least Squares regressions for the period Dec. 2002-Mar. 2019.But most regressions start in September 2003, when Peru’s policy rate begins to be a policy variable. Robust standard errors in parentheses.*, **, ***, denote significance at 10, 5, and 1 percent, respectively.

17. Endogeneity issues appear less of a concern in this part of the analysis. The inclusion of the capital flows gap as a regressor has the same impact on the significance of RRs as in Table 1 but does not have a material effect on the other two variables (the real interest rate gap and the FXI gap).

F. Other Linkages

18. In addition to the findings discussed above, it is also useful to document other linkages. We focus below on exploring the behavior of the credit growth gap and its linkages with other domestic gaps and instruments. This may not only help shed light on the functioning of Peru’s policy framework, but also expand our understanding of macrofinancial links.

Credit Growth Gap

19. Credit developments are highly correlated with growth developments but less so with inflation developments. As illustrated in the upper panel of Figure 4, the behavior of the real credit growth gap is highly correlated (correlation of 0.55) with the behavior of the GDP growth gap, with trends being quite synchronized. On the other hand, the real credit growth gap shows a weak and negative correlation with the inflation gap (correlation of -0.26), suggesting that the inflation gap has unlikely been related with credit-fueled economic overheating.

Figure 4.
Figure 4.

Peru and Chile: Linkages of the Real Credit Gap

Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A003

Source: IMF staff estimates.

20. The link of the credit growth gap with policy instruments is relatively weak. In particular, the links appear to be quite period-specific and it is difficult to identify more general patterns (Figure 5). For instance, Peru increased RRs in the years before the global financial crisis (GFC) when the credit gap was highly positive, and subsequently reduced RRs when credit decelerated. However, other periods of RR tightening do not appear correlated with developments of the real credit gap (but, as identified before, they appear highly correlated with changes in the capital flows gap). The large and positive credit gap before the GFC also took place in a period with negative policy rate gap, which may suggest that easier monetary conditions in Peru contributed to that gap. Subsequently, both the interest rate and credit gaps have narrowed, but it is difficult to identify strong similarities in the patterns of those two series. The same can be said about the link of the FXI gap with the credit gap. The pre-GFC period shows an increase in the credit gap accompanied by FX purchases, but the subsequent period shows a gradually narrowing credit gap while the FX gap oscillated at high frequency.

Figure 5.
Figure 5.

Peru and Chile: Linkages of the Real Credit Gap and Policy Instruments

Citation: IMF Staff Country Reports 2020, 004; 10.5089/9781513526126.002.A003

Source: IMF staff estimates.

G. Conclusions

21. Peru’s multi-instrument inflation targeting framework appears to have caused deviations vis-à-vis a more orthodox framework primarily along two dimensions: policy rate setting and real exchange rate outcomes. Peru and Chile’s policy frameworks performed similarly in terms of output growth and inflation and their volatility, the latter being only slightly lower in Peru for both series. On the other hand, Peru’s (real and nominal) monetary policy rate as well as its (real and nominal) exchange rates were significantly less volatile and this result appears to have been influenced by the effect of policy instruments on those variables. For the case of the real policy rate, the most relevant instrument appears to have been the RRs (although largely dependent on developments in capital flows). For the case of the real exchange rate, the most relevant instruments appear to have been the real policy rate and FXI, although the RRs may also have played a role (again dependent on developments of capital flows).

22. Nonetheless, the deviations from orthodoxy were modest and transitory. For the case of the (real) policy rate, part of the deviations can be explained by fundamental factors (i.e., different inflation developments) and part could be attributed to the use of reserve requirements, which may have partially offset the need to adjust interest rates during periods of high capital flows. This latter effect is, however, moderate with a 10 percentage points increase in reserve requirements leading to the real interest rate being 0.2 percentage points lower than its orthodox benchmark. For the case of the real exchange rate, the role of the policy variables appears to be stronger, with all the three policy variables playing a role in explaining deviations of the real exchange rate from its benchmark. However, also in this case the deviations were transitory and of moderate magnitude. For instance, the average monthly FX sale episode was around 0.25 months of imports, which translates into an appreciation of the real exchange rate in the order of 1.3 percent.

23. It is less clear how credit developments influenced the management of the multi-instrument framework. Real credit gaps appear to have been highly correlated with real GDP gaps, but only weakly correlated with inflation gaps. Also, the real credit gap is only weakly correlated with the use of different policy instruments.

24. Further work appears warranted to better understand the impact of policy instruments on economic outcomes. One important area for further research is related with better understanding the endogeneity linkages among outcomes and instruments (Appendix II provides some initial work in this regard). In addition, it would be useful to expand the analysis to include other countries as well as to explore the impact of some of the gaps on economic vulnerabilities and welfare. Furthermore, the analysis of the links between the gaps and policy instruments may usefully be complemented with country-specific studies, which would allow us to relax the assumption of identical coefficients made in our empirical regression analysis.

References

  • Armas A. and M. Vega (2018), “Peru: Foreign Exchange Intervention under Financial Dollarization”, in M. Chamon, D. Hofman, N. Magud, and A. Werner (Eds) Foreign Exchange Intervention in Inflation Targeters in Latin America, Washington D. C., International Monetary Fund, pp. 227–248.

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  • Fernandez, A., M. Klein, A. Rebucci, M. Schindler, and M. Uribe (2016) “Capital Control Measures: A New Dataset”, IMF Economic Review, Vol. 64 (3), pp. 548–574

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  • Larrain C. and D. Saravia (2018), “Interventions in Chile”, in M. Chamon, D. Hofman, N. Magud, and A. Werner (Eds) Foreign Exchange Intervention in Inflation Targeters in Latin America, Washington D. C., International Monetary Fund, pp. 115–135.

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Annex I. Data Sources and Definitions

article image

All variables are measured monthly. The sample period is January 2003-March 2019. Gaps are calculated by taking differences between the Peru variable and the corresponding Chile variable. For the real exchange rate gap and the terms of trade gap, natural logs are taken to the REER and TOT indexes before taking the difference. For the capital flows gap, the difference is taken directly to the figures in USD billions (this variable works better in the instrumental variables than the series divided by imports of goods and services, but the behavior of the two series is quite similar).

Annex II. Addressing Endogeneity through VAR Analysis

1. One way to address endogeneity problems is to treat all variables as endogenous and estimate the model through a VAR. We explore this approach below but restrict our analysis to assessing to what extent the results obtained in the main text are also present in the VAR analysis. A more comprehensive VAR analysis, including the analysis of impulse response functions, is left for future work.

2. Table A.1 presents the results of the VAR estimation. While the interpretation of the coefficients in the VAR is different from those in the equations presented above, there are several interesting results to highlight:

  • First, the equation for the real policy rate (first column) shows that the inflation gap is a significant explanatory variable and has the same sign as in the regressions presented in Table 1. The RR gap also has the same sign, but the significance in this equation falls slightly short of the standard significance levels.

  • Second, the equation for the real exchange rate gap (fifth column) shows that the FXI gap is highly significant and has the same sign as in Table 2. The real policy rate also exhibits similar results as those in Table 2, but the significance level is borderline. Interestingly, the terms of trade gap, which was not significant in Table 2, is significant in this estimation.

Table A1.

Peru and Chile: VAR Estimation Dependent (Variables indicated at the top of the column)

article image
Source: Author’s calculations.Notes: Standard errors in parentheses, t-statistics in brackets.Estimations are for the period Dec. 2002-Mar. 2019. But most regressions start in September 2003, when Peru’s policy rate becomes available.
1

Prepared by Pedro Rodriguez.

2

In the remaining of this work we use the term policy framework to refer to the set of objectives and instruments used by the central banks to conduct their monetary and exchange rate policies. This definition does not include macroprudential policies and capital flow measures.

3

The real exchange rate gap is measured as the difference in the logarithms of the real exchange rate indexes. Hence, the gap can be interpreted as a percent deviation from its benchmark.

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Cover IMF Staff Country Reports
Peru: Selected Issues
Author: International Monetary Fund. Western Hemisphere Dept.
Volume/Issue:
Volume 2020: Issue 004
Publisher:
International Monetary Fund
ISBN:
9781513526126
ISSN:
1934-7685
Pages:
60
DOI:
https://doi.org/10.5089/9781513526126.002
  • View in gallery

    Peru and Chile: Selected Macroeconomic Series

  • View in gallery

    Peru and Chile: Policy Instruments

  • View in gallery

    Peru and Chile: Real Monetary Policy Rate Gap

    (Percentage points)

  • View in gallery

    Peru and Chile: Real Monetary Policy Rate and Reserve Requirements

    (In percent points and in percent [RHS])

  • View in gallery

    Peru and Chile: Capital Inflows and Reserve Requirements

    (USD billion, in percent [RHS])

  • View in gallery

    Peru and Chile: Real Exchange Rate Gap and its Determinants

  • View in gallery

    Peru and Chile: Linkages of the Real Credit Gap

  • View in gallery

    Peru and Chile: Linkages of the Real Credit Gap and Policy Instruments

Peru and Chile: Selected Macroeconomic Series

Peru and Chile: Policy Instruments

Peru and Chile: Real Monetary Policy Rate Gap

(Percentage points)

Peru and Chile: Real Monetary Policy Rate and Reserve Requirements

(In percent points and in percent [RHS])

Peru and Chile: Capital Inflows and Reserve Requirements

(USD billion, in percent [RHS])

Peru and Chile: Real Exchange Rate Gap and its Determinants

Peru and Chile: Linkages of the Real Credit Gap

Peru and Chile: Linkages of the Real Credit Gap and Policy Instruments