The cornerstone of Chile’s impressive fiscal performance and strong fiscal system has been its structural balance rule. It has helped to insulate public spending from copper price cycles and improve the government’s net financial position. Chile should adopt a full-fledged medium-term fiscal framework to improve fiscal planning and provide a framework for addressing temporary deviations from the fiscal rule. Publishing additional fiscal indicators in the budget, such as the non-copper structural balance, could provide more comprehensive information on the impact of fiscal policy on the domestic demand.

Abstract

The cornerstone of Chile’s impressive fiscal performance and strong fiscal system has been its structural balance rule. It has helped to insulate public spending from copper price cycles and improve the government’s net financial position. Chile should adopt a full-fledged medium-term fiscal framework to improve fiscal planning and provide a framework for addressing temporary deviations from the fiscal rule. Publishing additional fiscal indicators in the budget, such as the non-copper structural balance, could provide more comprehensive information on the impact of fiscal policy on the domestic demand.

IV. Revisiting the Estimation of the Chilean Output Gap1

A. Introduction

1. In this paper, various methods are applied to estimate Chile’s output gap. Since each econometric or statistical technique has its advantages and disadvantages, we aim not to have “a” measure of potential GDP growth and the output gap. Instead, having several estimations provides a reasonable range for these values. Most of the note is based on Fuentes et al (2007), updating it with more recent data.

B. Models

Univariate Methods

2. Four univariate methods have been used. These include a piece-wise linear trend, Hodrick-Prescott filter, Baxter and King bandpass filter, and the methods in Christiano-Fitzgerald (2003).

  • Piece-wise linear trend (LT). A linear trend is fitted through the log of GDP. The series is tested for structural breaks using the Chow and the Quandt-Andrews tests. A break is detected in the last quarter of 1998, in line with the Asian crisis. The Chilean economy was growing at an average 7.8 percent between the first quarter of 1986 and the last quarter of 1998. After the crisis, average real GDP growth lowered to 4 percent.

  • Hodrick-Prescott (HP) filter. As is standard, the HP-filter smoothes the deviations of a time series from its trend. Following standard practices we adopt a smoothness parameter equal to 1,600, as we are using quarterly data. This method computes an average growth rate of 7.2 percent for the period 1986:Q1-1998:Q4, while the post-crisis average growth rate is 3.6 percent.

  • Baxter-King (1999, BK) and Christiano-Fitzgerald (2003, CF) band-pass filter. These methods adjust business cycles using a range of business cycles frequencies to compute the cyclical component. The estimation is consistent with the LT and the HP filters. Average growth rate before the structural brake was 7.2 percent with BK and 7.4 percent with CF, decreasing to 3.6 percent and 3.6 percent, respectively after the structural brake.

Figure 1.
Figure 1.

Piece-Wise Linear Trend

Citation: IMF Staff Country Reports 2010, 299; 10.5089/9781455208388.002.A004

Source: Staff calculations.
Figure 2.
Figure 2.

Hodrick-Prescott Filter

Citation: IMF Staff Country Reports 2010, 299; 10.5089/9781455208388.002.A004

Source: Staff calculations.
Figure 3.
Figure 3.

Baxter-King Filter

Citation: IMF Staff Country Reports 2010, 299; 10.5089/9781455208388.002.A004

Source: Staff calculations.

In the figure below we depict the output gaps implied by each method. In general, all of them seem to depict a similar path for the output gap. These linear methods suggest that the output gap was close to 2 percent toward the end of 2009, increasing to the neighborhood of 3 percent in Q1-2010 due to the February earthquake.

Figure 4.
Figure 4.

Output Gap: Univariate Filters

Citation: IMF Staff Country Reports 2010, 299; 10.5089/9781455208388.002.A004

Source: Staff calculations.

Multivariate Methods

3. Among the multivariate procedures, we use statistical filters and econometric methods. Three different versions of the Kalman filter have been estimated. The econometric approaches include a production function method, a structural vector auto- regression, and the IMF’s Global Projection Model.

  • Kalman filter. Univariate filter estimations improve if macroeconomic information is added. Here we follow Fuentes et al (2007) and consider three alternative macro relations: a Phillips curve, an IS curve, and Okun’s law.2 The first restriction considers a backward-looking Phillips curve such that inflation deviations are positively linked to the output gap. The (backward-looking) IS curve model incorporates a relationship between the output gap and the central bank’s monetary policy rate. Okun’s law approach adds a relation between the output gap and deviations of the unemployment rate from the NAIRU. The plot below shows the output gap using each of these filters. We observe in Table 1 that before the Asian crisis real GDP was growing at a 7.2 percent in model 1, 7.2 percent in model 2, and 6.5 percent in model 3. After the crisis, average growth rates were 3.5, -3.5, and 3.6, respectively.

  • Production function. We follow two approaches here. One approach follows the implementation of Menashe and Yakhin (2004) in Fuentes et al. (2007), taking a Cobb-Douglas production function in terms of capital and labor. Using logs, deviations of real GDP from potential output are a function of deviations of the capital stock utilization from its steady state (since the total capital stock is potentially available) and deviations of labor from full employment. Menashe and Yakhin (2004) elaborate on the irrelevance of deviations of TFP from its potential level in these estimations. Using quarterly data, results show that average growth rate of potential output after the structural brake was 3.8 percent. Another approach follows Estevao and Tsounta (2010). The latter computes total factor productivity (TFP) given real GDP, labor, and capital. An HP filter is applied to each series to compute potential values of capital, labor, and TFP. The latter are then used to compute potential output. The estimation was carried over in two different versions and for different data frequencies. The first one uses labor, whereas the second one corrects labor for years of schooling. Using annual data, the first approach gives an average growth rate of potential GDP equal to 6.3 prior to 1999 and 3.9 thereafter. When labor is corrected for years of schooling, the average growth rates are 7.3 and 4.5 percent instead. Using quarterly data, these figures are 7.2 and 3.5, respectively.

  • Blanchard-Quah (SVAR, 1989). This method requires imposing structural restrictions to an otherwise standard Vector Auto-Regression (VAR). Blanchard-Quah decomposes demand and supply shocks. According to theory demand shocks should be considered temporary while supply shocks should be characterized as permanent. Following Fuentes et al. (2007), variables are de-meaned to account for the structural brake in late 1998. In this case, potential output average growth rate was estimated to be 7.5 percent before the Asian crisis and 3.3 after it.

  • IMF’s Global Projection Model (GPM). This is a Bayesian model in five stochastic behavioral equations. It estimates an output gap equation, an inflation equation, an interest rate equation, an expected real exchange rate equation, and a dynamic Okun’s law equation.3 The model estimates an average growth rate for potential output of 3.7 percent for the period 2001–09.

Figure 5.
Figure 5.

Output Gap: Kalman Filters

Citation: IMF Staff Country Reports 2010, 299; 10.5089/9781455208388.002.A004

Source: Staff calculations.
Figure 6.
Figure 6.

Output Gap: Production Function, Structural VAR, and GPM

Citation: IMF Staff Country Reports 2010, 299; 10.5089/9781455208388.002.A004

Source: Staff calculations.

C. Comparison of Results

4. Table 1 summarizes the results from all of the above approaches. Prior to the Asian crisis, most of the estimations point to an average growth rate for potential output between 6 and 7 percent. Post crisis, however, the average growth rate decreased to a 3–4 percent range. Only the production function approach, corrected for years of schooling, gives average growth above 4 percent.

5. The results for the output gap estimated with the different methods are quite similar. Most suggest that the output gap just prior to 2010’s earthquake was in the 2–3 percent range. The SVAR results are somewhat different, possibly due to the assumed restrictions. Demand shocks are assumed to be temporary. But the interaction of supply shocks (through the price of copper) with some persistence in demand might be explaining these differences. Further research needs to be done to explore the details.

Table 1.

Average Growth Rates

article image

Annual.

Corrected for education.

2001-2009

Source: Staff calculations.

References

  • Canales Kriljenko, J.I., C. Freedman, R. Garcia-Saltos, M. Johnson, and D. Laxton, 2009, “Adding Latin America to the Global Projection Model,” IMF Working Paper No. 09/85 (Washington: International Monetary Fund).

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

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  • Fuentes, R., F. Gredig, and M. Larrain, 2007, “Estimating the Output Gap for Chile,” Central Bank of Chile Working Paper No. 455 (Santiago: Central Bank of Chile).

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  • Medina, L., 2010, “Potential Growth and Output Gap in Peru,” in Peru: Staff Report for the 2010 Article IV Consultation Selected Issues, IMF Country Report No. 10/99, pp. 57 (Washington: International Monetary Fund).

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1

Prepared by Nicolas Magud and Leandro Medina (all WHD).

2

See Fuentes et al. (2007) and Medina (2010) for details on these restrictions.

Chile: Selected Issues Paper
Author: International Monetary Fund