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Peru: Selected Issues

Author(s):
International Monetary Fund. Western Hemisphere Dept.
Published Date:
January 2014
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China’s Spillovers to Peru: Insights from a Macroeconomic Model for a Small Open and Partially Dollarized Economy1

This chapter quantifies the spillover effects of China on Peru’s economic activity, with particular attention to the impact of China’s investment slowdown. The estimates suggest that the shock to investment growth in China has a significant impact on Peru’s economic growth. Furthermore, a macroeconomic model for a small open and partially dollarized economy is built and estimated to measure the transmission channels, and simulate Peru’s macroeconomic and policy responses to the shock of China’s investment slowdown. The counterfactual analyses suggest that Peru’s output is exposed to the shock despite a well-targeted inflation, and underscore the importance of a flexible exchange rate in light of external demand shocks.

A. Introduction

1. As a small open economy, Peru is exposed to external shocks, particularly the shocks from China. One of Peru’s largest trading partners is China. Peru sold 17 percent of its total exports to China in 2012 (about 4 percent of GDP), of which 81 percent are metals. Cross-country comparison shows that over a third of Peru’s copper exports, 64 percent of gold exports, and 22 percent of other mineral commodities went to China during 2008–12. However, China’s mineral imports from Peru including copper and gold are still at a relatively small share in its total mineral imports.

Peru: Exports by Destination

(Percent of total exports)

Sources: UN Commodity Trade Statistics; and Fund staff calculations.

1/ Comprising LA6 economies, Argentina, Bolivia, Ecuador, Paraguay, and Venezuela.

Peru’s Real Exports To China: Mineral v.s. Non-Mineral

(Percent of real total exports to China)

Sources: UN Commodity Trade Statistics; IFS; and Fund staff calculations.

Peru’s Real Mineral Exports by Destination

(Percent of corresponding exports to the world during 2008-12)

Sources: UN Commodity Trade Statistics; IFS; and Fund staff calculations.

1/ Comprising LA6 economies, Argentina, Bolivia, Ecuador, Paraguay, and Venezuela.

China’s Real Mineral Imports by Origin

(Percent of corresponding imports from the world during 2008-12)

Sources: UN Commodity Trade Statistics; IFS; and Fund staff calculations.

1/ Comprising LA6 economies (excluding Peru), Argentina, Bolivia, Ecuador, Paraguay, and Venezuela.

2. The IMF’s 2012 Spillover Report found that China has significant spillover effects on its main trading partners and world prices, mainly through its high growth of investment. Investment has been a key driver of economic growth and lower external surpluses in China. As found in the IMF’s 2012 Spillover Report, “a slowdown in China’s investment growth, while desirable to rebalance demand towards consumption in the medium term, could in the interim hit partners and world prices, especially if the adjustment were to be sharp and disorderly.”

3. This chapter quantifies the inward spillovers from China’s investment slowdown to Peru’s economic activity using Vector Autoregressive (VAR) models. The results suggest that shocks to China’s investment growth have significant overall effects on Peru’s output growth, and these effects mainly operate through their impact on world metal prices, and hence Peru’s terms of trade and real GDP growth.

4. In addition, this chapter estimates and simulates Peru’s macroeconomic and policy responses to China’s investment slowdown using a new-Keynesian macroeconomic model. In particular, we measure the main spillover channels, and conduct counterfactual analyses to simulate Peru’s macroeconomic and policy responses, following Salas (2010).

5. This chapter is organized as follows. Section B quantifies the overall spillover effects of China’s investment slowdown on Peru’s real GDP growth, and identifies the main spillover channels. Section C develops the macroeconomic model for a small open and partially dollarized economy. Section D describes the data and estimation strategy of the model, and discusses briefly the estimation results. Section E presents and discusses the counterfactuals generated from the model under two scenarios, a baseline scenario and an alternative scenario where China’s investment growth slows down. The final section concludes with a brief discussion of policy implications.

B. Stylized Facts

6. Peru’s economic activity is closely linked to China’s investment growth. The correlation between Peru’s real GDP growth and China’s investment growth during 1995–2012 is 0.5. However, the IMF’s 2012 Spillover Report did not find significant impact of China’s investment slowdown on Peru’s output growth. This is because the Spillover Report only measured the direct trade exposures, and the indirect transmission channels (for instance, through the impact on Peru’s terms of trade) were not captured.

Peru: Real GDP and China’s Investment

(Annual percent change; 1995-2012)

Sources: Haver Analytics; Fund staff calculations.

Direct transmission channels

7. The direct trade spillover effects of China’s investment growth on Peru’s economic growth are relatively limited.2 Peru’s mineral export volumes to China do not seem to be positively affected by China’s investment growth. This suggests that the quantity effect of a lower investment growth in China on China’s demand for Peru’s mineral exports is likely to be small, or in other words, a small income elasticity of China’s demand for Peru’s mineral exports.

Peru: Mineral Export Volumes to China and China’s Investment

(Annual percent change; 2001-12)

Sources: UN Commodity Trade Statistics; Haver Analytics; and Fund staff calculations.

8. China is a relatively small source of foreign direct investment (FDI) for Peru. According to Peru’s investment promotion agency (Proinversion), the stock of Chinese FDI (as measured by contributed capital) in Peru was US$208 million as of 2012, which is still far below Peru’s top three sources of FDI: Spain (US$4.7 billion), the U.K. (US$4.5 billion), and the U.S. (US$3.2 billion). China’s FDI is mainly focused in the mining (US$158 million) and financial (US$50 million) sectors.

Indirect transmission channels

10. The main indirect spillovers of China’s investment growth are transmitted through its impact on Peru’s terms of trade (price effect).3 According to the IMF’s 2012 Spillover Report, China’s investment slowdown has significant impact on world metal prices. In particular, the report estimates that a one standard deviation decline in China’s investment growth is likely to reduce world copper prices and world metal prices by about 5½ percent and 4½ percent in one year, respectively. Thus, due to a high correlation of 0.9 between Peru’s terms of trade and world metal prices, as well as a large impact of terms of trade on real GDP growth, China’s investment growth is likely to influence Peru’s economic growth, as reflected by the charts below.

World Metal Prices and China’s Investment

(Annual percent change; 1995-2012)

Sources: Haver Analytics; International Financial Statistics; Fund staff calculations.

Peru: Terms of Trade and World Metal Prices

(Annual percent change; 1995-2012)

Sources: Haver Analytics; International Financial Statistics; Fund staff calculations.

Peru: Real GDP and Terms of Trade

(Annual percent change; 1995-2012)

Sources: Haver Analytics; Fund staff calculations.

Peru: Real GDP and China’s Investment

(Annual percent change; 1995-2012)

Sources: Haver Analytics; Fund staff calculations.

11. VAR models suggest that a one standard deviation decline in China’s investment growth is likely to reduce Peru’s real GDP growth by 0.4 percentage points cumulatively over one year after the shock.4 A quarterly VAR model is estimated over the sample period 2000Q1-2013Q3 to assess the overall impact of China’s investment slowdown on Peru’s real GDP growth. The VAR model includes China’s investment growth5, Peru’s terms of trade, real exchange rate (vis-à-vis the U.S. dollar), the index of Lima Stock Exchange (IGBVL), and Peru’s real GDP growth as endogenous variables, and the U.S. and euro area real GDP growth as exogenous variables. Cholesky decomposition is used as shock identification strategy, and the Cholesky ordering is the same as listed above. The impulse response functions to the negative shock suggest a deterioration in Peru’s terms of trade, a decline in the stock index IGBVL, and a drop in Peru’s real GDP growth. The cumulative impact of the shock on Peru’s real GDP growth is approximately 0.4 percentage points in one year after the shock.6 In addition, we estimate the spillovers of China’s consumption using the same VAR model except that the investment growth is replaced by consumption growth.7 The impulse response functions for a one standard deviation negative shock to China’s consumption growth suggests insignificant spillover effects. Thus, a rebalancing of the Chinese economy towards consumption could negatively affect Peru’s economy.

C. A Macroeconomic Model for a Small Open and Partially Dollarized Economy

12. A new-Keynesian macroeconomic model for Peru is developed to better study the policy responses and conduct counterfactual analyses in the context of a small open and partially dollarized economy. The model is based on a general equilibrium and rational expectations model developed by Salas (2010), which consists of a core set of behavioral equations. Since the model is relatively small compared to the traditional dynamic stochastic general equilibrium (DSGE) models and yet has a well-grounded economic interpretation (Berg et al., 2008), it has been used by the Central Reserve Bank of Peru (BCRP) for policy making. The model has four building blocks, namely: (i) an IS curve or aggregate demand equation; (ii) an expectations-augmented Phillips Curve or aggregate supply equation; (iii) a Taylor-type monetary policy rule for the short-term interest rate; and (iv) an uncovered interest rate parity (UIP) condition. Three features of the model are worth noting. First, in the context of a small open economy, terms of trade and foreign output are included as exogenous variables in the aggregate demand equation. Second, in a partially dollarized economy, agents can take loans in U.S. dollars, thus the interest rate in U.S. dollars also enters the aggregate demand equation as an exogenous variable. Third, to capture the frequent foreign exchange interventions conducted by the central bank, a backward-looking behavior in the determination of the exchange rate expectations is considered in the model.8

13. Aggregate demand.Equation (1) characterizes the aggregate demand or IS curve, which describes the dynamics of the output gap (yt).

In this equation, rt is the real interest rate in domestic currency, and rt$ is the real interest rate in U.S. dollars. Their effects on output gap are affected by a common coefficient ar and idiosyncratic coefficients, or weighting parameters, βr and βrs. ToTt is the terms-of-trade gap, i.e. the gap of international relative prices9, and this captures the indirect spillover channel from China to Peru. It is assumed that both contemporaneous and lagged terms of trade could directly affect current output gap, and βtot is the weighting parameter for current terms-of-trade gap. qt is the gap of real effective exchange rate (REER).10 The external demand measured by foreign output gap, yt* is also considered. We assume that Peru, as a small open economy, does not affect its terms or trade or external demand. In other words, terms of trade and external demand in equation (1) are exogenous variables. Finally, the disturbance term εty denotes a demand shock. Appendix 1 provides a detailed description of the data.

14. Aggregate supply or Phillips curve.Equation (2) is a standard new-Keynesian aggregate supply equation or expectations-augmented Phillips curve, which characterizes inflation πt.

In this equation, inflation has both backward- and forward-looking behaviors, indicated by the two components, πt-1 and Ett+1), respectively, where Ett+1) is the inflation expectation. (πtm+stst1) is the imported inflation measured in domestic currency, computed as the sum of imported inflation πtm (measured in U.S. dollars), and the nominal exchange rate variation, (st - st-1). The disturbance term εtπ represents a supply shock.

15. Monetary (interest rate) policy rule.Equation (3) describes a Taylor-type monetary policy rule characterizing the determination of the short-term nominal interest rate (it).

In this equation, i¯ is the steady-state nominal interest rate or the neutral rate, and π¯ is the inflation target. Following Barro (1989), an interest rate smoothing is considered when the central bank determines the interest rate, as indicated by its first lag, it-1. In addition, interest rate also responds to output gap yt and the deviations of inflation πt and expected inflation Ett+1) from the inflation target π¯. Thus, the interest rate has a forward-looking behavior as both inflation and inflation expectations are anchored by this policy rule. The disturbance term εti denotes a monetary policy shock.

16. Uncovered interest rate parity (UIP). The nominal exchange rate st is determined by the uncovered interest rate parity as expressed in equation (4).

In equation (4), the expected quarterly exchange rate variation [Et(st+1) - st] is multiplied by 4 to be transformed into an annual term, which is equal to the differential between the interest rate in domestic currency and the interest rate in foreign currency (or U.S. dollars in this paper) plus an error term εts. Following Salas (2010), we assume that the exchange rate expectation Et(st+1) is determined by a weighted average of a backward-looking component (st-1) and a forward-looking component (st+1), as specified in equation (5), which is in line with the authorities’ objective of interventions to reduce the excess volatility of the exchange rate.

The parameter γ (bounded between 0 and 1) implicitly measures the extent to which the exchange rate is smoothed by the central bank’s foreign exchange interventions. More specifically, the higher γ is, the higher the degree of exchange rate smoothing is.

In addition, following Salas (2010), REER (in gap terms) in equation (1) is determined by its first lag, nominal exchange rate variation, and the differential between domestic and foreign inflation, as specified in equation (6).

πt* is the foreign inflation, and the disturbance term εtq denotes a shock to the REER.

Finally, the real interest rates rt and rt$ in the aggregate-demand equation (1) are linked to the nominal interest rates it and it* in domestic currency and U.S. dollars, respectively, by the following two equations.

17. External shocks. Terms of trade, external demand, and interest rate in U.S. dollars are the main exogenous variables in this model and the main channels that external shocks spillover to Peru. We assume that the terms-of-trade gap evolves according to the following dynamics.

In equation (7), Mt is the world metal price (in gap terms), and εttot is the disturbance term or the terms-of-trade shock stripping out the shocks to world metal prices.

External demand yt* is approximated by a trade-weighted average of Peru’s top three trading partners’ output gaps, including the U.S., China, and the euro area, as specified in equation (7).

In equation (8), yt is the output gap of each of these three economies, and wt is the trade weight between each of the three economies and Peru (summing up to 1).

18. China’s investment slowdown is transmitted through its impact on China’s output and world metal prices. There are mainly two channels for the transmission of the shock in this model. First, as discussed above, China’s investment slowdown directly reduces China’s growth and output gap ytCHN, and thus the external demand yt* according to (7) (the direct channel). Second, China’s investment slowdown imposes downward pressures on world metal prices Mt and hence on Peru’s terms of trade according to (6) (the indirect channel).

D. Data and Estimation

19. The model is estimated with quarterly data of Peru over the sample period 2000Q1–2013Q3. It has six endogenous variables, namely, output gap, inflation, interbank interest rate, nominal exchange rate (quarterly rate of change), REER gap, and terms-of-trade gap. All the gap variables are computed with the Hodrick-Prescott (HP) filter. The inflation expectations Ett+1) are proxied by the one-year-ahead inflation expectations, obtained from the central bank’s inflation expectation survey. The inflation is computed as the seasonally-adjusted annualized rate, and the inflation target π¯=2 according to the central bank. The Appendix provides a detailed description of the data and their sources. Finally, the model is estimated as a system using the Generalized Method of Moments (GMM), and the endogenous variables are instrumented by their first lags in the estimation to avoid the endogeneity problem. The weighting parameters βr, βr$, and βToT are not estimated but calibrated according to Salas (2010) as follows: βr = 0.3, βr$ = 0.15, and γtot = 0.48.

20. Estimation results are presented in Tables 1-4. The estimates are in line with Salas (2010) and other literature such as Berg et al. (2008). Three points are worth mentioning. First, world metal prices are statistically significant in terms of driving Peru’s terms-of-trade dynamics, which confirms our previous empirical finding that China’s investment slowdown is likely to have significant spillover effects on Peru’s GDP growth through terms of trade. Second, both domestic currency-denominated and U.S. dollar-denominated interest rates have significantly negative impact on Peru’s output gap. This suggests a downward sloping IS curve for Peru, and thus also implies that the monetary policy in the U.S. might have a significant impact on Peru’s real economy. Third, similar to Salas (2010), we also find a significantly high degree of exchange rate smoothing partly due to the central bank’s foreign exchange interventions.

E. China’s Spillovers to Peru: Counterfactuals and Macroeconomic Responses

21. This section examines the macroeconomic responses generated by the estimated model under two different external scenarios. The first is a baseline scenario under the IMF’s projections, and the second is an alternative scenario where China’s investment growth declines by one standard deviation from the baseline in 2014Q1.11 The simulation horizon is 2013Q4-2018Q4.

22. The baseline assumptions for external variables over the simulation horizon come from IMF projections. World metal prices, foreign inflation (proxied by world inflation), and the U.S., euro area, and China’s output gaps are taken from the IMF’s Global Assumptions (GAS) and World Economic Outlook (WEO) projections. U.S. dollar-denominated interest rates are assumed to remain unchanged throughout the simulation horizon. Projections for imported inflation measured in U.S. dollars and inflation expectations are taken from the projections of the IMF’s Peru team.

23. The alternative scenario assumes a one standard deviation decline in China’s investment growth compared to the baseline scenario. This negative shock influences the Peruvian economy through two external variables in the model, namely, world metal prices and China’s output gap. First, the impact on world metal prices has been analyzed and quantified by the IMF’s 2012 Spillover Report and Ahuja and Nabar (2012). A one standard deviation decline in China’s investment growth is likely to reduce world metal prices by 3¼ percent over a year.12 Second, to obtain the impact of this shock on China’s output gap, we estimate the impact of this shock on China’s output growth using a simple VARX model with China’s investment growth and real GDP growth as endogenous variables, and the real GDP growth of the U.S. and euro area as exogenous variables.13 Finally, the assumptions for the other external variables remain the same as those in the baseline scenario.

24. Counterfactual analyses suggest that a one standard deviation negative shock to China’s investment growth is likely to reduce Peru’s output gap by about 0.2 percentage points cumulatively one year after the shock. The simulated dynamics for all endogenous variables are shown in the Figure. The shock is estimated to deteriorate Peru’s terms-of-trade gap by about 1¼ percentage points within one year after the shock. This decline in the terms of trade widens Peru’s negative output gap by about 0.2 percentage points cumulatively over the year 2014. In the medium term, the output gap in the alternative scenario also converges to zero, but the convergence happens 4 quarters later than in the baseline scenario.

Figure.Counterfactual Analysis: Peru’s Macroeconomic Responses in Baseline and Alternative Scenarios

1/ Baseline Scenario: IMF’s GAS and WEO assumptions. Simulation horizon: 2013Q4–2018Q4.

2/ Alternative Scenario: A one standard deviation decline in China’s investment growth in 2014Q1 compared to the baseline scenario. (A one standard deviation decline in growth is equivalent to a 2.5 percent decline in China’s investment levels from the baseline.) Simulation horizon: 2013Q4–2018Q4.

Sources: Central Reserve Bank of Peru; National Institute of Statistics; Haver Analytics; International Financial Statistics; Information Notice System; World Economic Outlook databases; and Fund staff estimates.

25. The shock has relatively limited impact on inflation, implying a well-anchored inflation targeting system. Inflation declines by only about 1 percentage point (year-on-year) cumulatively throughout the simulation horizon, despite the relatively large decline in the interest rate. This is also partly due to our assumption of well-anchored inflation expectations.

26. The shock has a relatively large impact on the domestic interest rate, suggesting a large response of monetary policy to output gap variations. The nominal short-term interest rate is about 30 basis points lower than in the baseline scenario in the medium term as a response to a widened negative output gap.

27. The shock has little impact on the exchange rate, reflecting a high degree of exchange rate smoothing. Nominal exchange rate depreciates by only 0.1 percent in the medium term cumulatively compared to the baseline scenario, partly due to the foreign exchange interventions by the central bank.

F. Concluding Remarks

28. This chapter finds that Peru’s economic activity is vulnerable to China’s investment slowdown. The estimates from VAR models suggest that a one standard deviation decline in China’s investment growth is likely to reduce Peru’s real GDP growth by about 0.4 percentage points cumulatively over one year after the shock. The counterfactual analyses from the macroeconomic model for a small open and partially dollarized economy suggest that a one standard deviation decline in China’s investment growth in 2014Q1 is likely to widen Peru’s output gap by about 0.2 percentage points cumulatively one year after the shock. Furthermore, the main impact of the shock is transmitted through the indirect terms-of-trade channel instead of the direct trade exposures.

29. The macroeconomic and policy responses to China’s investment slowdown suggest that inflation remains well-targeted, but higher exchange rate flexibility might be desirable to mitigate the impact on output. Nominal exchange rate depreciates by only 0.1 percent more in the medium term cumulatively in the alternative scenario compared to the baseline scenario. With a more depreciated nominal exchange rate, the output gap might not be widened as much in face of China’s investment slowdown.

Appendix. Data
VariableData and Source
Output gapGross domestic product (millions of 1994 nuevos soles, seasonally adjusted). Gap computed with the HP filter. Source: Central Reserve Bank of Peru.
Terms-of-trade gapExport price index relative to import price index (1994=100, quarterly average, seasonally adjusted). Gap computed with the HP filter. Source: Central Reserve Bank of Peru.
Real effective exchange rate (REER) gapGap computed with the HP filter. Source: Informational Notice System.
Foreign output gapA weighted average of the output gaps of the U.S., euro area, and China using the trade shares between each of the three economies with Peru as the weights. Sources: IMF’s World Economic Outlook databases; and International Financial Statistics.
InflationCPI inflation (Dec. 2001=100, quarterly average, seasonally adjusted). Source: Central Reserve Bank of Peru.
Imported inflationComputed with the import price index (1994=100, quarterly average, seasonally adjusted). Source: Central Reserve Bank of Peru.
Foreign inflationWorld inflation (quarterly average, seasonally adjusted). Source: International Financial Statistics.
World metal price gapWorld metal price index (2005=100, quarterly average, seasonally adjusted). Source: International Financial Statistics.
Nominal interest rate in domestic currencyInterbank interest rate (quarterly average). Source: Central Reserve Bank of Peru.
Nominal interest rate in U.S. dollars3-month U.S. dollar Libor rate (quarterly average). Source: Haver Analytics.
Nominal exchange rateQuarterly average. Increase denotes depreciation. Source: Central Reserve Bank of Peru.
Table 1.Estimation Results: Aggregate Demand Equation

yt=ayyt-1+a1+(βrrr+βr$rt$)+atot[βtot ToTt+(1-βtot)ToTt-1]+aqqt+ay*yt*+εty

Estimated Parameters
CoefficientGMM estimateStd. Error
ay0.71**0.06
ar-0.14*0.08
atot0.05*0.01
aq0.060.07
ay*0.09*0.05
Calibrated Parameters
βr0.3
βr$0.15
βtot0.48
Note: * and ** indicate statistical significance at the 10% and 5% levels, respectively.
Note: * and ** indicate statistical significance at the 10% and 5% levels, respectively.
Table 2.Estimation Results: Aggregate Supply Equation

πt=bππt-1+bπeEt(πt+1)+byyt+bmπtm+εtπ

Estimated Parameters
CoefficientGMM estimateStd. Error
bπ0.11*0.06
bπe0.21*0.02
by0.07*0.04
bm0.06**0.01
Note: * and ** indicate statistical significance at the 10% and 5% levels, respectively.
Note: * and ** indicate statistical significance at the 10% and 5% levels, respectively.
Table 3.Estimation Results: Monetary Policy Rule

it=ciit-1+(1-ci){i¯+cyyt+cπ(πt-π¯)+cπe[Et(πt+1)-π¯]}+εti

Estimated Parameters
CoefficientGMM estimateStd. Error
Ci0.70**0.05
Cy0.90**0.29
Cπ0.61*0.37
Cπe0.050.49
i¯3.38**0.47
Note: * and ** indicate statistical significance at the 10% and 5% levels, respectively.
Note: * and ** indicate statistical significance at the 10% and 5% levels, respectively.
Table 4.Estimation Results: Exchange Rate Expectation Equation

Et(st+1)=γst-1+(1-γ)st+1+εte

Estimated Parameters
CoefficientGMM estimateStd. Error
γ0.26**0.04
Note: * and ** indicate statistical significance at the 10% and 5% levels, respectively.
Note: * and ** indicate statistical significance at the 10% and 5% levels, respectively.
References

Prepared by Fei Han (WHD) and Juan Alonso Peschiera Perez-Salmon (WHD-Lima Office). The authors would like to thank the Central Reserve Bank of Peru (BCRP) for kind data provision and helpful discussions.

See Annex IV of Peru’s Staff Report for the 2013 Article IV Consultation.

The impact of Peru’s terms of trade on real GDP growth and the transmission channels have been analyzed in Annex IV of Peru’s Staff Report for the 2013 Article IV consultation.

One standard deviation is equivalent to 3½ percentage points in quarter-on-quarter, seasonally adjusted, growth rates. The shock is approximately equivalent to a 2.5 percent decline in the investment levels.

China’s investment growth is proxied by China’s fixed asset investment due to the data limitation of China’s total investment.

This result is consistent with the finding in Annex IV of Peru’s Staff Report for the 2013 Article IV Consultation that a one percentage point decline in China’s real GDP growth is likely to reduce Peru’s real GDP growth by 0.2-0.4 percentage points, because investment accounts for about a half of real GDP in China, and China’s consumption seems to have insignificant spillover effects on Peru as mentioned below.

China’s consumption growth is proxied by the growth of retail sales due to the data limitation of China’s private consumption.

Central banks in several partially dollarized economies actively intervene in the foreign exchange market in order to prevent the balance sheet effects stemmed from large exchange rate movements; see Calvo and Reinhart (2002) and Reinhart and Reinhart (2008).

All the gap variables in this paper are computed with the Hodrick-Prescott (HP) filter.

REER is also included here because Peru’s terms of trade and REER are not highly correlated, and thus seem to contain differentiated information. An increase in REER indicates a real appreciation vis-à-vis its trading partners.

This is a one standard deviation negative temporary shock to China’s investment growth in one quarter, equivalent to a 2.5 percent decline in China’s investment levels from the baseline. This shock is the same as the shock to China’s investment growth considered in the IMF’s 2012 Spillover Report for a better comparison.

See Table 2 in Ahuja and Nabar (2012). The estimated impact on world metal prices is then transformed into the impact on the gap of the world metal prices by assuming that the trend of metal prices remains the same as in the baseline scenario.

The estimates suggest that a one-standard-deviation decline in China’s investment growth is likely to reduce China’s real GDP growth by 0.3 percentage points cumulatively after one year. The estimated impact is then transformed into the impact on China’s output gap by assuming the same potential output as in the baseline scenario.

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