This paper assesses the performance of Peru’s alternative fiscal rules in supporting medium-term fiscal policy objectives. Peru has shown steady fiscal surpluses and declining debt vulnerabilities. Subnational government constrains to borrow internationally have been relaxed, which could become a concern in the future. Current debt levels are already low, which may allow the authorities to move quicker toward a structural balance rule, or even propose a small structural deficit, without jeopardizing debt sustainability.

Abstract

This paper assesses the performance of Peru’s alternative fiscal rules in supporting medium-term fiscal policy objectives. Peru has shown steady fiscal surpluses and declining debt vulnerabilities. Subnational government constrains to borrow internationally have been relaxed, which could become a concern in the future. Current debt levels are already low, which may allow the authorities to move quicker toward a structural balance rule, or even propose a small structural deficit, without jeopardizing debt sustainability.

Potential Growth and Output Gap in Peru

This chapter1 presents a range of estimates for the potential growth rate and the output gap in Peru, based on different statistical and economic methods. Potential growth is estimated to have increased to around 6¼ percent in 2002–2009, during the inflation targeting period, from about 3 percent in 1994–2001. Moreover, the results show a decline in the volatility of the output gap during the inflation targeting regime. The analysis also considers how a deceleration of trend global growth could affect Peru’s trend growth. Estimates show that a fall of trend global growth by 1 percentage point could decrease Peru’s trend growth by 1–1¼ percentage points.

I. Motivation

1. In recent years, Peru’s growth increased substantially associated with a structural change in fundamentals that resulted in higher potential growth. Growth averaged 2.9 percent annually since the 1980s, but rose to 7.4 percent during 2004–08. Growth, which peaked at 9.8 percent in 2008, came to standstill in the first half of 2009 as the economy was affected by the global crisis. Higher growth with low inflation for over nearly a decade suggests that Peru’s potential output has increased more rapidly than in the past. This is likely to reflect strong macroeconomic stability, higher investment, and reduced vulnerabilities. However, other factors such as a benign external environment during most of this period may also have contributed to higher growth. The analysis in this chapter attempts to shed some light on the extent to which the acceleration of trend growth in Peru reflects intrinsic fundamental factors and global conditions during the boom years.

uA04fig01

Peru: Real GDP Growth, 1980–2008

Citation: IMF Staff Country Reports 2010, 099; 10.5089/9781455206674.002.A004

Source: Banco Central de Reserva del Perú.

II. Measuring Potential Output and the Output Gap

2. Several methodologies are used to estimate potential output. These techniques, which decompose output into trend and cyclical components, may be grouped into two broad categories: those using univariate statistical procedures and those based on economic models. The former use time-series analysis to identify the permanent and cyclical components of output. Four sets of univariate techniques are discussed in this chapter: (i) piece-wise linear de-trending; (ii) filters that isolate high-frequency from low-frequency components (HP-filter, the Baxter and King filter, and the Christiano and Fitzgerald filter); and (iii) the Beveridge and Nelson decomposition. The methods based on economic models are: (i) the Kalman filter (with a Phillips curve, an IS curve, and an Okun’s Law equation); (ii) an Aggregate Production Function approach; and (iii) structural vector autoregression.

Statistical (Univariate) Procedures

  • Piece-wise linear de-trending. This approach fits a linear trend through the log GDP data, allowing for different trends in different subsamples. The Chow breakpoint test and the Quandt Andrews breakpoint test identify 2003Q4 as a structural break point in the GDP time series. This breakpoint coincides also with a change in the volatility of macroeconomic variables. Since 2003, Peru’s adherence to a strong macroeconomic policy framework, together with favorable external conditions, resulted in reduced volatility and higher growth until the recent global financial crisis.

  • Hodrick Prescott (HP) filter. This filter provides a more flexible approach to discerning potential output. It calculates potential output as the series that minimizes the deviation of actual output and potential output, subject to a penalty on the maximum allowable change in potential growth between two periods.2 The higher the penalty, the smoother the series. Trend growth is estimated at 6.27 percent, and the results show that the economy was overheated at end-2008, with a positive output gap of about 4 percent. The global financial crisis resulted in a rapid deceleration of growth starting in 2008Q4. The negative output gap at end-2009 is estimated at -4 percent.

  • Baxter and King (1999) and Christiano and Fitzgerald (2003) (BK and CF) band pass filters. These filters use a range of business cycle frequencies to compute the cyclical component of output.3 The results are similar to the ones reported by the HP filter, only that there is a smoother computation of the output gap.

  • Beveridge and Nelson (1981). This approach tackles the calculation of the trend output and output gap by decomposing a non-stationary time series (real GDP) into a non-stationary trend and a cyclical component by applying the Box-Jenkins (1976) methodology that fits an ARIMA (p, d, q)4 model to the GDP series.

uA04fig02

Peru: Linear Detrending, 1994Q1–2009Q3

Citation: IMF Staff Country Reports 2010, 099; 10.5089/9781455206674.002.A004

Source: IMF staff calculations.
uA04fig03

Peru: Hodrick-Prescott Filter, 1994Q1–2009Q3

Citation: IMF Staff Country Reports 2010, 099; 10.5089/9781455206674.002.A004

Source: IMF staff calculations.
uA04fig04

Peru: Baxter and King Filter, 1996Q1–2009Q3

Citation: IMF Staff Country Reports 2010, 099; 10.5089/9781455206674.002.A004

Source: IMF staff calculations.
uA04fig05

Peru: Christiano and Fitzgerald Filter, 1996Q1–2009Q3

Citation: IMF Staff Country Reports 2010, 099; 10.5089/9781455206674.002.A004

Source: IMF staff calculations.
uA04fig06

Peru: Beveridge and Nelson Decomposition, 1994Q1–2009Q3

Citation: IMF Staff Country Reports 2010, 099; 10.5089/9781455206674.002.A004

Source: IMF staff calculations.

3. Overall, the statistical procedures show an increase in potential output growth to about 6¼ percent during the inflation targeting period from less than 3 percent in the period before. Estimates of potential growth are reported in two periods, taking into account the implementation of the inflation-targeting framework in 2002. On average, potential growth increased by more than 4 percentage points. The estimates of the output gap show that the positive gap (excess demand) peaked in the first half of 2008 (3–4 percent), but turned negative in the first half of 2009.

Table 1:

Peru. Estimates of Potential Growth according to Univariate Techniques

article image
Source: IMF staff calculations.

Economic (Multivariate) Procedures

4. This section estimates Peru’s potential output using methodologies based on economic models. First, the Kalman filter is used exploring three variants based on: (i) the Phillips’ curve; (ii) the IS curve; (iii) and the Okun’s law. Second, an aggregate production function approach is used. Finally, the Blanchard and Quah (1989) methodology is applied.

  • Multivariate Kalman filter. This filter improves univariate methods by adding economic information such as the Phillips’ curve, Okun’s law or the IS curve. The use of macroeconomic relations can reduce the end-of-sample bias of most univariate filters as well as increase the theoretical foundation of purely statistical models.5 This section follows Fuentes, Gredig and Larrain (2008) and applies three models. Model 1 adds the Phillips curve as a second signal equation to the system. The assumption is that deviations in core inflation relate directly to the output gap and therefore provide important information to determine the evolution of the GDP trend. Model 2 incorporates the policy rate, while Model 3 adds Okun’s Law to capture information from the labor market (using a transition equation for the NAIRU6).

  • Aggregate production function (APF). This approach uses a functional relationship between output and factor inputs, typically a Cobb-Douglas representation. The parametrization of Peru’s production function follows Gollin (2002), Fuentes (2008), and Seminario and Beltran (1998). As factor inputs, the analysis considers active population and capital stock (computed according to the perpetual inventory approach with an estimated depreciation rate of 5 percent.

  • Blanchard and Quah (1989). This structural vector autoregression (SVAR) approach allows for the identification of shocks to real GDP, assuming that demand shocks have no permanent effect on output, while supply shocks may have. It uses the unemployment rate to identify demand shocks.7

uA04fig07

Multivariate Kalman Filter, 1996Q1–2009Q3

Citation: IMF Staff Country Reports 2010, 099; 10.5089/9781455206674.002.A004

Source: IMF staff calculations.
uA04fig08

Aggregate Production Function, 2001Q2–2009Q3

Citation: IMF Staff Country Reports 2010, 099; 10.5089/9781455206674.002.A004

Source: IMF staff calculations.
uA04fig09

Blanchard and Quah SVAR, 1997Q2–2009Q3

Citation: IMF Staff Country Reports 2010, 099; 10.5089/9781455206674.002.A004

Source: IMF staff calculations.

5. Multivariate estimates confirm that Peru has doubled its potential growth rate during the inflation targeting period. The potential growth using multivariate methodologies is estimated at around 6¼ percent during 2002–2009, compared to less than 2¼ percent in the period before.

Table 2:

Peru. Estimates of Potential Growth according to Economic Methods 1/

article image
Source: IMF staff calculations.

Due to data availability, estimation starts in 1996Q1.

Change in Output Gap Volatility

6. Using univariate and multivariate estimations, there is evidence of reduced volatility of the output gap during the inflation targeting framework. The average estimated standard deviation of the output gap fell to 1.6 percent during the IT period from over 2 percent in the period before. This reduction in output gap volatility was found far all estimation methodologies.

Table 3.

Estimates of Output Gap Volatility 1/

article image
Source: IMF staff calculations.

Measured as output gap’s standard deviation.

III. Peru’s and Global Growth

7. Peruvian growth is sensitive to global growth conditions. Using a VAR methodology, this section tries to assess the impact of global growth on Peru’s growth, and its potential implication for trend growth. Two samples are considered using quarterly data: (i) one covering the whole period (1996–2009), and (ii) the inflation targeting subsample (2002–09). The estimated impulse response function shows a positive impact on Peru’s GDP of a global growth innovation, with a peak impact in two to four quarters.8

uA04fig10

Peru GDP growth response to a unitary shock in World growth

Citation: IMF Staff Country Reports 2010, 099; 10.5089/9781455206674.002.A004

Source: IMF staff calculations.

8. Global growth fluctuations account for a significant fraction of growth fluctuations in Peru. To quantify the contribution of global growth shocks to fluctuations in Peru’s growth, a variance decomposition analysis was performed for the full sample. The results show that global growth has accounted for nearly a quarter of Peru’s GDP fluctuations at a ten-quarter horizon. The variance decomposition analysis for the IT period shows a higher share at almost 60 percent, reflecting the reduction in idiosyncratic volatility that characterized the period of macroeconomic instability in Peru.

Table 4.

Variance Decomposition of Peru Growth

article image
Source: IMF staff calculations.

9. The impact of a reduction of trend global growth on Peru’s trend growth would be, at least, one to one. A scenario where global trend growth does not recover to its pre-crisis growth rate, for instance a reduction of ½ a percentage point, would reduce Peru’s trend growth by about ½ percentage point, from 6¼ to 5¾ percent over the next few years.

Table 5.

Average Growth Estimations 2010–11

article image
Source: IMF staff calculations.

References

  • Anderson, B., and J. Moore, 1979, “Optimal Filtering,” Engelwoods Cliff, NJ: Prentice Hall.

  • Baxter, M. and R. G. King, 1999, “Measuring Business Cycles: Approximate Band-Pass”. Filters for Economic Time Series,” Review of Economics and Statistics, Vol. 81.

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  • Beveridge, S. and C.R. Nelson, 1981, “A New Approach to the Decomposition of Economic Time Series into Permanent and Transitory Components with Particular Attention to Measurement of the Business Cycle,” Journal of Monetary Economics 7, pp. 151174.

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    • Export Citation
  • Blanchard, O. and D. Quah, 1989, “The Dynamic Effects of Aggregate Demand and Supply Disturbances,” American Economic Review, Vol. 79, No. 4.

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  • Christiano, L. J. and T. J. Fitzgerald, 2003, “The Band Pass Filter,” International Economic Review, Vol. 44, No. 2.

  • Estevão, M. and E. Tsounta, 2010, ” Canada’s Potential Growth: Another Victim of the Crisis?”, IMF Working Paper No. 10/13, International Monetary Fund.

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  • Fuentes, R., F. Gredig and M. Larraín, 2008, “La Brecha del Producto en Chile: Medición y Evaluación,” Economía Chilena, Vol. 11 -No. 2, Banco Central de Chile.

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  • Fuentes, R., Larraín, M. and K. Schmidt-Hebbel, 2006, “Measuring and Explaining Total Factor Productivity in Chile,” Cuadernos de Economía 43.

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  • Gollin, D., 2002, “Getting Income Shares Right,” Journal of Political Economy, 110: pp. 458474.

  • Harvey, A. C., 1991, “Time Series Models,” Deddington, U.K., Phillip Allan.

  • Hodrick, R.J. and E.C. Prescott, 1997, “Postwar U.S. Business Cycles: An Empirical Investigation,” Journal of Money, Credit, and Banking, Vol. 29, February.

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  • Menashe, Y. and Y. Yakhin, 2004, “Mind the Gap: Structural and Nonstructural Approaches to Estimating Israel’s Output GAP,” Israel Economic Review 2 (2): pp. 79106.

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  • Seminario, B. and A. Beltran, 1998, “Crecimiento Económico en el Perú, 1896–1995,” Universidad del Pacifico.

1

Prepared by Leandro Medina (WHD).

2

Following extensive literature, the adjustment of the sensitivity of the trend to short-term fluctuations is achieved by modifying a multiplier λ as standard in the literature λ =1600 for quarterly data.

3

In order to address the end-of-sample bias, data on quarterly projections for 2010–11 were considered when running the filter (8 leads and lags). Excluding projections does not change significantly the estimates of potential growth during the two periods. Default ranges are chosen (6–32 cycle periods).

4

In this case p refers to the number of autoregressive lags, d refers to the order of integration, and q gives the number of moving average lags.

5

For a more detailed explanation see Fuentes et al. (2008), Anderson and Moore (1979) and Harvey (1991).

6

Non-accelerating Inflation Rate of Unemployment, and refers to a level of unemployment below which inflation rises.

7

The SVAR estimation uses 5 lags for the endogenous variables, as indicated by the Akaike Information Criterion (AIC) and the Final Prediction Error (FPE) Criterion.

8

The solid line represents the response to one unit innovation in World GDP growth. Dotted lines reflect two-standard deviation bands.

Peru: Selected Issues
Author: International Monetary Fund