Chapter

II. Inflation Process in Uruguay

Author(s):
R. Gelos, Alejandro Lopez Mejia, and Marco Piñón-Farah
Published Date:
July 2008
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Author(s)
Gaston Gelos and Fernanda Rossi Iriondo 

Following chronic high inflation, Uruguay reduced inflation gradually through a disinflation program in the 1990s, reaching single digits by 1998. Inflation has since remained moderate, except for an outburst during the 2002 financial crisis and devaluation. In that year, the country moved to a flexible exchange rate regime, and inflation began declining quickly to low single digits by 2005. It has since increased somewhat to about 8 percent.

With relatively high inflation vis-à-vis the region and other emerging markets, a relevant question is how costly it would be to reduce it further. Various aspects of the factors driving inflation in Uruguay remain to be understood better. First, what is the degree of inflation persistence, and to what extent is inflation driven by backward-looking behavior and expectations? Understanding this is important for the conduct of monetary policy: with much backward-looking behavior, temporary inflationary shocks tend to persist and the output costs of reducing inflation quickly are higher. Second, what are the determinants of inflation expectations? This question is relevant given the monetary authorities’ increased focus on the inflation objective, de-emphasizing monetary targets. In such a context, anchoring expectations is a precondition for a successful monetary policy.

This chapter explores the dynamics of inflation in Uruguay, making use of survey data, focusing on the role of inflation expectations and their determinants. First, it assesses the role of inflation expectations in shaping the inflation process by estimating a New Keynesian Phillips curve. Second, it explores the determinants of survey expectations. Third, it discusses the rationality of survey expectations.

The chapter finds that expectations are more important than lagged inflation in shaping the inflation process, suggesting that inertia is relatively small. The importance of lagged inflation decreased with the adoption of a flexible exchange rate and has remained low since then. The results also suggest that, in addition to marginal cost proxies, fiscal variables are important in explaining expectations. There is some evidence that the central bank has gained credibility, with announced inflation targets playing an increasing role in forming expectations. Survey expectations over a 12-month horizon are neither unbiased nor efficient, but 1-month-ahead inflation expectations appear to be rational.

Inflation Dynamics and Inflation Expectations

To assess the importance of inflation expectations, this chapter uses a structural price-setting model nesting two types of price-setting behavior (see Galí and Gertler, 1999;Celasun, Gelos, and Prati, 2004a and 2004b; and Celasun and McGettigan, 2005). Backward-looking price setters update the average new price based on the most recently observed inflation rate. Forward-looking price setters set prices based on their expectations, which may or may not be rational. The resulting inflation rate in period t equals

where π is the 12-month (or monthly) consumer price index (CPI) inflation rate, Etπt+1 is the expected inflation rate in 12 (or 1) month(s), πt1 refers to inflation between 24 and 12 months ago (or between 2 and 1 months ago), and mc are marginal costs.

In the empirical work, annual inflation expectations from consensus forecasts are used. For comparison purposes, a shorter sample of monthly inflation expectations from a survey conducted since 2004 by the Uruguayan central bank is also used (Figure 2.1.)1,2 Because a typical firm in a small open economy is likely to use imported intermediate goods and domestic labor as inputs in production, a combination of the real effective exchange rate and domestic real unit labor costs (both in deviations from a linear trend) is used as a proxy of real marginal costs. Data for the period January 1998–September 2006 are used. To address potential endogeneity problems, the equation is estimated by the generalized method of moments (GMM). The equation is also estimated on a monthly forecast horizon for inflation forecasts for January 2004–September 2006 from a Central Bank of Uruguay survey.3

Figure 2.1Actual and 12-Month-Ahead Expected Inflation

(In percent)

Source: IMF staff estimates.

The results indicate that inflation expectations drive the inflation process (Table 2.1.) In the case of annual inflation, the coefficient on expected future inflation is close to 1, whereas the coefficient on lagged inflation is insignificant in all but one case, where it is slightly negative. The real effective exchange rate enters the estimation with a statistically significant positive coefficient, in line with the notion that foreign prices are important in determining prices in Uruguay. Using monthly data, the role of expectations is even stronger. The coefficient on expected future inflation is larger than 1, whereas the coefficient on lagged inflation is negative and smaller. Proxy marginal costs enter the estimation significantly with positive coefficients, but a much smaller fraction of the monthly variation in inflation can be explained compared with the case of annual data. Table 2.1 shows estimations with the coefficients on lagged and expected inflation constrained to sum 1, as suggested by theory. The key results do not change when estimating the equations without restrictions.

Table 2.1Consumer Price Index Inflation Regressions with Survey Data
Forecast HorizonOne YearOne Month
Estimate(1)(2)(3)(4)(5)(6)(7)(8)
Constant0.0030.0030.002–0.0310.001–0.002–0.001–0.004
(0.003)(0.004)(0.007)(0.075)(0.001)(0.002)(0.001)(0.008)
Lagged inflation–0.17–0.08*–0.032–0.168–0.18–0.30*–0.28*–0.28
(0.106)(0.045)(0.134)(0.227)(0.161)(0.162)(0.143)(0.168)
Inflation forecast1.17***1.08***1.032**1.168***1.18***1.30***1.28***1.28***
(0.106)(0.045)(0.134)(0.227)(0.161)(0.162)(0.143)(0.168)
Real effective exchange rate gap13.9***0.8796.12***
(2.905)(7.934)(2.936)
Real wage gap–0.002*00.001***
(0.001)00
Output gap–0.116**–0.013
(0.051)(0.008)
Unemployment rate0.2620.028
(0.552)(0.07)
Adjusted R20.920.920.860.850.270.450.40.4
Sample1998:01–2006:092004:01–2006:09
Number of observations10533
Source of inflationary expectationsconsensus forecastsCentral Bank of Uruguay
Source: IMF staff estimates.Notes: Generalized method of moments (GMM) estimates. Andrews bandwidth standard errors are given in parentheses. For 12-month forecast horizon the instruments for lagged inflation were inflation (–2) in equations 1 and 4; 12-month exchange rate change (–2) in equation 3; and lagged inflation in equation 2. Oil prices inflation (–1) was used as instrument for inflation forecast. The other regressors were used as instruments in the remaining equations. For 1-month forecast horizon, equation 1 includes inflation forecast (–1) as instrument for inflation forecast. The other regressors were used as instruments in this and the remaining equations.

indicate significance at the 10, 5, and 1 percent level, respectively.—signifies a variable not included in the estimation.

Source: IMF staff estimates.Notes: Generalized method of moments (GMM) estimates. Andrews bandwidth standard errors are given in parentheses. For 12-month forecast horizon the instruments for lagged inflation were inflation (–2) in equations 1 and 4; 12-month exchange rate change (–2) in equation 3; and lagged inflation in equation 2. Oil prices inflation (–1) was used as instrument for inflation forecast. The other regressors were used as instruments in the remaining equations. For 1-month forecast horizon, equation 1 includes inflation forecast (–1) as instrument for inflation forecast. The other regressors were used as instruments in this and the remaining equations.

indicate significance at the 10, 5, and 1 percent level, respectively.—signifies a variable not included in the estimation.

Estimations allowing for the possibility of nonstationarity confirm the key results. With relatively short time series, the issue of possible nonstationarity is difficult to address given the low power of standard tests. Tests following Clemente, Montañȳs, and Reyes (1998) suggest the presence of a unit root even after allowing for two structural breaks (not shown). Thus, we also estimate a system using the FIML (full-information maximum-likelihood) method by Johansen and Swensen (1999), confirming a stationary relationship between inflation, expected inflation, and proxy variables of marginal costs. In line with the GMM results, the estimated coefficient on expected inflation was greater than 1 and significant. The proxies for marginal costs were positive but not statistically significant (not shown).

Recursive Estimates

A recursive estimation shows that the importance of lagged inflation has declined. Indeed, it falls sharply after the floating of the exchange rate, stabilizing after mid-2003 (Figure 2.2.)

Figure 2.2Recursive Coefficient Equation (1) (Recursive ordinary least squares estimates)

(Coefficient of lagged inflation and two standard error bands in equation 1)

Source: IMF staff estimates.

Inflation Persistence

The role of lagged inflation is limited, even after accounting for its impact on expectations and wage formation. If inflation expectations were largely driven by past inflation, the coefficient on lagged inflation would underestimate the overall role of past inflation in shaping the inflation process. To explore this issue, we re-estimated equation (1), replacing expected inflation with the residual of a regression of expected inflation on lagged inflation, finding that the coefficient does not change substantially. Similarly, backward-looking wage indexation may induce inertia through the marginal cost variable, thus leading to underestimation of the importance of lagged inflation. Therefore, the residuals of a regression of unit labor costs on lagged inflation as marginal cost proxy are also used, without altering the qualitative results. All these factors point to low inflation inertia.

Determinants of Inflation Expectations

What factors explain inflation expectations? Because inflation expectations appear to be a key driving force of Uruguayan inflation dynamics, anchoring expectations is important for monetary policy. Following Celasun, Gelos, and Prati (2004a and 2004b), it is key to investigate the role of past inflation, fiscal outcomes, the exchange rate, monetary policy variables, and real wages on inflation expectations by estimating variants of the following general model:

where pbt–1 is the ratio of the consolidated primary fiscal balance to GDP, and the monetary variables include year-on-year M1 growth or, alternatively, exchange rate change (see Licandro and Vicente (2006) for a discussion of the link between fiscal policy and inflation objectives in Uruguay). It is also important to examine whether there has been a structural break in the postcrisis expectations formation using a dummy variable. Equation (2) is estimated by a GMM to address potential endogeneity problems, using both 12-month- and 1-month-ahead horizons.

The results suggest that lagged inflation, the primary balance, and marginal costs proxies explain expected inflation (Table 2.2). Monetary aggregates become important only after the adoption of a flexible exchange rate regime and do not play a role in shorter horizon forecasts. Changes in the nominal exchange rate affect expectations, but the effect is modest and not significant for one-month-ahead expectations. Lagged inflation contributes 50–60 percent to explaining expected inflation, although for one-month expectations, the contribution is significantly smaller. For the 12-month-ahead estimation, recursive coefficient estimates on lagged inflation stabilize around 0.6 after the adoption of a flexible exchange rate in 2002 (Figure 2.3).

Table 2.2.Determinants of Inflation Expectations
Generalized Method of Moments EstimatesCointegration Equation Estimates
Estimate(1)(2)(3)(4)(5)(6)(7)(8)
Constant0.039***0.042***0.001–0. 11***0.010***0.03
(0.004)(0.004)(0.026)(0.039)(0.003)
Inflation (–1)0.699***0.662***0.76***0.76***−0.0640.240**0.243**0.8
(0.03)(0.029)(0.075)(0.067)(0.133)(0.108)(0.0943)(0.086)
Inflation target (−1)1.44***
(0.489)
Primary balance/GDP (−1)–0.005***–0.005***–0.003**–0.003**0.018***–0.001**0.001 ***–0.012
(0.001)(0.001)(0.002)(0.0015)(0.005)(0.001)0(0.003)
Real effective exchange rate gap (–1)1296***32.69***1.510**1.550**60.02
(3.32)(7.532)(0.659)(0.649)(16.72)
Real wage gap (–1)–0.002***0.005
0(0.002)
Unemployment rate (–1)0.190.20***
(0.203)(0.039)
year-on-year M1 growth (–1)0.050.05
(0.038)(0.034)
year-on-year exchange rate change (–1)–0.04*–0.04*
(0.022)(0.02)
DC0.02*0.02**
(0.015)(0.011)
DP* Inflation (–1)–0.243***–0.216***–0.18**–0.18***
(0.035)(0.026)(0.078)(0.037)
Adjusted R20.930.940.880.920.840.260.160.24
Forecasts horizon12 months ahead12 months ahead1 month ahead12 months ahead
Sample1998:01–2006:102004:01–2006:102004:01–2006:101998:01–2006:10
Number of observations1063434106
Source of inflationary expectationsconsensus forecastsCentral Bank of UruguayCentral Bank of Uruguayconsensus forecasts
Source: IMF staff estimates.Notes for generalized method of moments estimates: Andrews bandwidth standard errors are given in parentheses. Instruments include inflation (–2), inflation (–3), primary balance/GDP (–3), real wage gap (–2), real wage gap (–3), money growth rate (–2), money growth rate (–3), DC, and D P. Estimates (5) and (6) exclude DC and DP as instruments, where DC and DP are dummy variables capturing two different effects of 2002 crisis. DC=1 in 2002:07–2003:2 period, thus it captures a transitory increase of inflationary expectations due to the crisis. DP=1 from 2003:3 onward, thereby it captures some structural changes after the crisis. The estimation results showed that only lagged inflation coefficients have experienced a structural change due to the 2002 crisis.

indicate significance at the 10, 5, and 1 percent level, respectively.—signifies a variable not included in the estimation.

Notes for cointegration equation estimates: standard errors are given in parentheses.
Source: IMF staff estimates.Notes for generalized method of moments estimates: Andrews bandwidth standard errors are given in parentheses. Instruments include inflation (–2), inflation (–3), primary balance/GDP (–3), real wage gap (–2), real wage gap (–3), money growth rate (–2), money growth rate (–3), DC, and D P. Estimates (5) and (6) exclude DC and DP as instruments, where DC and DP are dummy variables capturing two different effects of 2002 crisis. DC=1 in 2002:07–2003:2 period, thus it captures a transitory increase of inflationary expectations due to the crisis. DP=1 from 2003:3 onward, thereby it captures some structural changes after the crisis. The estimation results showed that only lagged inflation coefficients have experienced a structural change due to the 2002 crisis.

indicate significance at the 10, 5, and 1 percent level, respectively.—signifies a variable not included in the estimation.

Notes for cointegration equation estimates: standard errors are given in parentheses.

Figure 2.3Determinants of Inflationary Expectations: Recursive Coefficients

(Recursive ordinary least squares (OLS) estimates on an annual horizon)

Source: IMF staff estimates.

The primary balance plays a role in determining expected inflation. It enters with a negative coefficient in almost all the estimated equations (see Celasun, Gelos, and Prati, 2004a and 2004b; and Cerisola and Gelos, forthcoming) and, for estimations with a 12-month-ahead horizon, the coefficient increased slightly since 2003. The quantitative significance of this factor is moderate, however: a 1 percent increase in the primary balance as a percentage of GDP is estimated to yield a fall in inflation expectations by about 0.5 percentage point. For a shorter forecast horizon, it is also significant but less important. The model tracks inflation expectations quite well, including during the crisis period (Figure 2.4).

Figure 2.4Twelve-Month-Ahead Expected Inflation: Actual Versus Fitted (Generalized method of moments–based model)

(In percent)

Source: Authors’ calculations.

There is some evidence that announced inflation targets have gained credibility. Inference is limited given that this study works with data starting January 2004, using the midpoint of the 12-month-ahead inflation target range. The coefficient on the inflation target is significant; on lagged inflation, it is insignificant. Recursive ordinary least squares (OLS) estimates show that the coefficient was approaching 1 during 2006 (Figure 2.5). These results suggest that forecasters are forward-looking and increasingly anchoring expectations around the central bank targets.4 The notion of a recent increase in the central bank’s credibility is consistent with the evolution of the dispersion of inflation forecasts. In particular, the coefficient of variation for survey expectations shows no clear trend until early 2006 and a decline since then (Figure 2.6). However, this result needs to be taken with caution. As discussed in the chapter by López–Mejía, Rebucci, and Saizar, before the switch to interest rate targeting in September 2007, there appeared to be some perceptions of ambiguity about the objectives and instruments of monetary policy that could have undermined monetary policy credibility.

Figure 2.5Recursive Estimates of Coefficient on Inflation Target (Recursive ordinary least squares estimates)

(Coefficient of inflation target and two standard error bands)

Source: IMF staff estimates.

Figure 2.6Dispersion of Inflation Forecasts

(Coefficient of variation)

Source: IMF staff estimates.

Estimations taking into account possible nonstationarity reinforce the results. A Johansen cointegration test finds a stable relationship between expected inflation, lagged inflation, primary balance, real effective exchange rate, and real wage gap; a vector error correction (VEC) model suggests that the 12-month-ahead inflation forecasts are determined by lagged inflation, primary balance, and the marginal costs proxies. The estimated coefficients have the same signs and only slightly higher magnitudes than the GMM estimates.

Rationality

Are expectations rational? To explore this issue, this chapter first examines unbiasedness, regressing actual inflation on both an intercept and expected inflation:

If expectations are unbiased, c should be zero and β one. This is the joint hypothesis tested in this chapter (Table 2.3).

Table 2.3.Tests of Unbiasedness of Inflation Forecasts
CoefficientsConsensus ForecastsCentral Bank of Uruguay
α–0.031–0.004
β1.351.46
Adjusted R20.890.382948
Coefficient (p-values)consensus forecastsCentral Bank of Uruguay
α = 00.00010.052
β = 10.00010.0816
α = 0 and β = 10.00030.1265
Sample period1998:01–2006:092004:01–2006:09
Source: IMF staff estimates.
Source: IMF staff estimates.

The Central Bank of Uruguay inflation forecasts appear unbiased for 1 month ahead, but biased for 12 months ahead. However, the tests should be interpreted with caution given the potential nonstationarity of the series. Generally, the estimated coefficient of an I(1) variable is not normally distributed, invalidating the usual tests. In any case, however, the paper would reject unbiasedness for the 12-month-ahead inflation expectations.5,6

Allowing for the possibility of nonstationarity, the results confirm that expectations do not use all available information. Johansen tests find a cointegration relation between actual and expected inflation (Appendix Table 2A.1). The speed at which expectations are revised over time in light of new information is estimated through a bivariate VEC model with inflation and expected inflation (Appendix Table 2A.2). If the parameter on the expectation error in this regression is significant, then consumers revise their expectations and adjust them toward the fully rational outcome. The estimated coefficients are negative and significant for both the 12-month-ahead and 1–month–ahead forecasts, implying low persistence in deviation between actual and expected inflation. However, regressions of the forecast errors on different variables indicate that 12-month-ahead forecasts fail to extract all possible information from all the relevant variables,7 except for past information on the real effective exchange rate gap. On the other hand, one-month-ahead forecasts seem efficient in the use of past information, except for the informational content of actual inflation (Appendix Table 2A.3).

Conclusions

This chapter examined the role of inflation expectations in shaping inflation dynamics, the degree of inflation persistence, and the rationality and factors driving inflation expectations. The key results are as follows:

  • Inflation expectations are more important than past inflation in shaping the inflation process. As a result, inflation inertia is not high. However, recent changes in wage agreement mechanisms containing backward-looking elements may lead to an increase in inertia. In the most recent wage negotiations, emphasis has been shifted again to forward-looking adjustments.
  • Moreover, the role of lagged inflation in shaping the inflation process has been declining since 2002.
  • Confirming previous studies’ results, fiscal variables play a role in driving inflation expectations, with a higher primary balance associated with lower expected inflation.
  • The evidence suggests that the central bank has gained credibility since the adoption of a flexible exchange rate and the announcement of inflation targets. Forecasters increasingly take the central bank’s targets into account when forming expectations.
  • Twelve-month-ahead inflation expectations are neither unbiased nor efficient, whereas one-month-ahead inflation surveys are.

For policy purposes, low inertia implies that responding to inflationary shocks entails more limited output losses. The results also underscore the role of consistent macroeconomic policies for anchoring inflation expectations, with fiscal credibility playing an important role. The increase in credibility of the central bank since the move to a flexible exchange rate regime bodes well for a gradual move to a full–fledged inflation–targeting regime. As discussed in the next chapter, further increasing credibility will require increasing the clarity of objectives and procedures of monetary policy. Still, the evidence on the rationality of the forecasts is mixed. This suggests that further efforts at enhancing policy communications may help agents improve their forecasts and assist the authorities in anchoring expectations.

Appendix 2.1. Cointegration Tests, Bivariate Vector Error Correction Model, and Tests of Efficiency of Inflation Forecasts
Table 2A.1.Johansen Cointegration Test Results
Trace Statistic for Alternative Trend Assumptions: Source of Inflationary Expectations
Consensus forecastsCentral Bank of Uruguay
No intercept, no trend12.735.3
(0.043)(0.00)
An intercept, no trend20.349.6
(0.049)(0.00)
An intercept, a linear trend19.949.1
(0.01)(0.00)
An intercept and a trend, a linear trend29.352.3
(0.018)(0)
An intercept, quadratic trend29.351.5
(0.001)(0)
Source: IMF staff estimates.p -values for the null: none; cointegrating equations are reported in parentheses. Two lags were used as indicated by Schwartz criterion.
Source: IMF staff estimates.p -values for the null: none; cointegrating equations are reported in parentheses. Two lags were used as indicated by Schwartz criterion.
Table 2A.2.Adjustment Coefficients in a Bivariate Vector Error Correction on Actual and Expected Inflation
Consensus Forecasts, 12 Months Ahead
Source of Inflationary ExpectationsExpectations regressionActual inflation regression
Adjustment coefficients–0.15*0.13*
t-statistics–0.067–0.061
Sample period1998:01–2006:09
Central Bank of Uruguay, 1 Month Ahead
Expectations regressionActual inflation regression
Adjustment coefficients–1.41*–0.63
t–statistics–0.34–1.05
Sample period2004:04–2006:09
Source: IMF staff estimates.
Source: IMF staff estimates.
Table 2A.3.Tests of Efficiency of Inflation Forecasts
12-month-ahead forecast horizon Sample period: 1993:03–2006:09 Number of observations: 163 Dependent variableRegressorsChi-squarep-value
εt+12Constant, lags 1 to 3 of inflation21.710.0002
εt+12Constant, lags 1 to 3 of primary balance10.420.034
εt+12Constant, lags 1 to 3 of real wage gap19.240840.0007
εt+12Constant, lags 1 to 3 of real effective exchange rate gap8.9331860.0628
εt+12Constant, lags 1 to 3 of year-on-year M1 growth18.590570.0009
εt+12Constant, lags 1 to 3 of unemployment rate25.359120.00
εt+12Constant, lags 1 to 3 of year-on-year exchange rate change70.519590.00
1-month-ahead forecast horizon Sample period: 2004:01–2006:09 Number of observations: 33 Dependent variableRegressorsChi-squarep-value
εt+1Constant, lags 1 to 3 of inflation14.609010.0056
εt+1Constant, lags 1 to 3 of primary balance5.496805***0.24
εt+1Constant, lags 1 to 3 of real wage gap2.5344670.6385
εt+1Constant, lags 1 to 3 of real effective exchange rate gap9.1357440.0578
εt+1Constant, lags 1 to 3 of year-on-year M1 growth4.0181220.4036
εt+1Constant, lags 1 to 3 of unemployment rate2.9073120.5735
εt+1Constant, lags 1 to 3 of year-on-year exchange rate change6.9105370.1407
Source: IMF staff estimates.

indicates significance at the 1 percent level.

Source: IMF staff estimates.

indicates significance at the 1 percent level.

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1One–year–ahead inflation expectations are calculated as a weighted average of this year and next year’s inflation forecast. Data are available on a monthly basis starting in 2001 and bimonthly prior to that. Data are interpolated for the missing months.
2Roberts (1995 and 1997) pioneered the use of survey expectations in estimating Phillips curves and inflation dynamics in the United States. Use in emerging markets is not widespread.
3The monthly surveys conducted by the central bank are available from 2004.
4See Cerisola and Gelos (forthcoming) and Minella and others (2003) for similar results on the relevance of inflation targets for the case of Brazil.
5See similar results for other Latin American countries in de Carvalho and Bugarin (2006).
6The results are not an artifact from the approximation for the 12-month forecasts because unbiasedness is also rejected for much shorter subperiods.
7This result also holds for the shorter 12-month survey available from the central bank.

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