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The authors wish to thank Richard Clarida, Mark Griffiths, Juha Kähkönen, Ratna Sahay, Antonio Spilimbergo, and Carlos Végh for helpful discussions, and the Research Department of the Central Bank of Turkey for useful comments and suggestions.
The literature on stabilization is too large to be discussed here. See Sargent (1982) for a study of the process of ending hyperinflations and Calvo and Vegh (1999) for an extensive discussion of inflation stabilization in developing countries. Fischer, Sahay, and Vegh (2002) provide an overview of modern hyper- and high inflations, including disinflation episodes. Hamann and Prati (2002) study why many inflation stabilizations succeed only temporarily.
See the conference report of the 2001 NBER conference on Turkey under http://www.nber.org/crisis/turkey_report.html.
For an example of the use of survey data in the estimation of the Phillips curve, see Roberts (1995).
Food prices carry the largest weight in the CPI (31 percent), followed by housing (26 percent), clothing (9.8 percent), transportation (9.3 percent), housewares (9 percent), health (2.9 percent), entertainment (2.9 percent), hotels and restaurants (2.8 percent), education (1.6 percent), and miscellaneous (4.5 percent.)
Stock (2001), in a comment on Cogley and Sargent (2001) notes: “There are a variety of ways to measure persistence, none perfect.” He then goes on to use a median-unbiased estimation method similar to the one employed here.
We also repeated the analysis on quarterly data obtaining analogous results. For all these calculations, we modified a program that was originally written by Antonio Spilimbergo.
The bottom panel of Figure 2 does not show the upper bound of the confidence interval for the half-life of a shock to inflation in Brazil because the corresponding median unbiased estimator of the autoregressive parameter was 1, suggesting that the Brazilian CPI inflation series could be nonstationary, as shown in other studies (see, for example, Durevall (1999)).
Galí and Gertler (1999) argue that proxies for marginal costs should be used instead of an excess demand/output gap variable. See also the discussion in Celasun (2001). We will use both approaches below.
Chadha, Masson, and Meredith (1992) show that when δ is greater than 0.5 there is a money growth rate that keeps the output gap at zero along the disinflation path.
All variables are in logarithms, and except for the real wage, in deviation from a linear trend. For the logarithm of the real wage, the quadratic trend was also statistically significant, and therefore was used along with the linear one in detrending.
The presence of the real exchange rate term accounts for the possibility that a share of firms, most likely in the tradables sector, “index” their prices to the current and expected future domestic level of foreign prices (i.e. the exchange rate multiplied by foreign prices). We plan to test in future work whether some price setters index to the exchange rate in a backward-looking manner.
** Denotes significance at the 1 percent level.
The estimated coefficient on the relative price of tradables with respect to nontradables (trntr) is statistically indistinguishable from zero in samples that end after 1999:Q3, but all other variables in the equation remain statistically significant in driving inflation.
We used the mean forecast of 15 different economic forecasters, including 13 Turkish and foreign private financial institutions, Istanbul Bilgi University, and the Turkish Industrialists’ and Businessmen’s Association.
The specification remains constant across the estimations. The equation for, say, n-monthly inflation includes n-months lagged (n-monthly) inflation, expected n-month ahead inflation, and the n-month average of capacity utilization, as well as a constant term. We restrict the sum of the coefficients of lagged and expected future inflation to one, as in the GMM regressions.
Six-month and one-year average inflation expectations are computed using a weighted average of this year’s and next year’s expected inflation rate. In the case of six-month inflation, we assume that expected inflation was equal for the whole 12-month horizon. Given that these data are available only at a bimonthly frequency since May 1998, we interpolate data for the missing months.
The recursive estimates of the coefficient of lagged inflation (not shown) are quite stable since mid-1997.
We did not address the separate question of whether inflation expectations are rational. A quick examination suggests that survey forecasts are unbiased, but not efficient. The fact that lagged inflation helps explain expectations is not inconsistent with the earlier results in which expectations of future inflation drive current inflation. It suggests that a reduced form equation could express current inflation as a function of lagged inflation, an excess demand/marginal cost term, and other variables explaining expectation formation.
Including the exchange rate, as an explanatory variable is problematic, since, at least in periods in which it is floating, the exchange rate is a jump variable that captures inflation expectations. The qualitative results presented below are not affected by the exclusion of the exchange-rate variable.
We use past changes in the debt stock as instruments for the forecast of the fiscal balance.
Several studies have analyzed econometrically the relationship between public sector deficits and inflation in Turkey (see, for example, Metin (1998).) See also Alper and Ucer (1998) and Lim and Papi (1997).
One might wonder whether the lagged inflation term is the regressor that explains most of the variation in inflation expectations. This does not seem to be the case. For example, in specification I, the R2 excluding lagged inflation is 0.55.