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David Rose is a Research Advisor at the Bank of Canada. This paper has benefitted from discussions with Bill English, Tiff Macklem, Guy Meredith, John Roberts, Steven Symansky, Chris Towe, and Simon van Norden. The views expressed in this paper are those of the authors and do not necessarily reflect those of the International Monetary Fund or the Bank of Canada.
Five countries have officially announced explicit inflation targets: Canada, Finland, New Zealand, Sweden, and the United Kingdom. For an extensive discussion of the experience of three of these countries (Canada, New Zealand, and the United Kingdom), see Ammer and Freeman (1995).
As shown in the estimation section below, this filter maximizes the fit of our basic inflation equation.
The output gap is defined as the percentage difference between actual output and the estimated value of potential output. We refer to a “positive” output gap when actual output exceeds potential, and a “negative” gap when the reverse holds. Inflation is measured as by quarter-to-quarter change expressed at an annual rate.
For example, Perry (1971, p. 560), projected that growth in potential output would average 4.3 percent annually during the 1970s. Subsequently, Adams and Coe (1990) estimated that potential output in the non-farm U.S. business sector grew at an 2¾ percent average annual rate during the 1970s, which is only slightly higher than the average growth rate of real GDP of 2.6 percent during 1970-80.
Testimony of Chairman Greenspan before the Committee on Banking, Housing, and Urban Affairs, U.S. Senate, February 22, 1995, as reprinted in the Federal Reserve Bulletin, April 1995, pp. 384.
Ibid, pp. 343.
The quadratic functional form is chosen only for analytical convenience; it does not give rise to a sensible Phillips curve because at some point large negative gaps will result in an increase in inflationary pressures.
The implications of convexity for economic stabilization policies have been pointed out by Mankiw (1988, p. 483) in his comments on De Long and Summers (1988): “Because of capacity constraints, increases in aggregate demand raise prices more quickly than decreases in aggregate demand lower them. This aggregate supply curve, or indeed any convex aggregate supply curve, will imply that stabilization increases mean output.” Subsequently, however, Ball and Mankiw (1994) presented a model where macroeconomic stabilization does not raise the mean level of output in the presence of asymmetric price adjustment at the firm level.
In other words, we think that econometricians should be as concerned about Type I and Type II policy errors as they are about Type I and Type II statistical errors. Here, the Type 1 policy error is rejecting the hypothesis of nonlinearity when it is true. The implication of the analysis in this paper is that if monetary policy were based on the assumption of a linear Phillips curve, when in fact the relationship between inflation and capacity is nonlinear, macroeconomic performance would be adversely affected because the tendency for the economy to overheat would require significant periods of slack to reduce inflation back to the target level.
A more formal justification for the presence of lagged inflation is given by Taylor (1980) in a model of overlapping wage contracts expressed in growth rates.
The estimation results in this paper were estimated with the nonlinear least squares routine in RATS.
Initial estimates of the inflation equation showed some evidence of third-order autocorrelation in the residuals. We then re-estimated the equations with the ROBUST ERRORS option in RATS. The t-statistics and Wald tests reported in Table 1 are based on the corrected variance-covariance matrix from this estimation procedure.
This finding is similar to that of Turner (1995), who found in the case of three out of the seven major industrial countries that in allowing for an asymmetric impact of the output gap, the inflationary effect of positive gaps is up to four times the deflationary effect of negative gaps.
As discussed below, the value of α depends on the monetary policy reaction function. As noted above, there is evidence that the Fed’s reaction function shifted over the sample period considered here. This implies that our estimate of α may be some average value reflecting the effects of different monetary policy response functions. In principle, it would be desirable to test for different values of α under different policy regimes. However, a strong test for such differences would appear to be difficult on account of the small sample of observations. In Section V below we use stochastic simulations to show the sensitivity of α to the nature of the reaction function.
Our approach is similar to that of Roberts (1994), who used reduced form regressions linking the quarterly percent change in real GDP to lagged changes in Federal funds rate.
For a description of the MPS model and simulation results, see Mauskoff (1990).
For an extensive discussion of reaction functions that rely on economic forecasts, see Anderson and Enzler (1987). See also Brunner (1994) and Mehra (1994) for more recent estimates of the monetary policy reaction function of the Federal Reserve.
It is useful to compare the estimated value of α over the historical sample period of -1.26, shown in Table 1, with the computed values of -0.43 and -1.11 in Table 6. The fact that the sample estimate is close to the higher figures suggests that a myopic policy response function dominated the sample period.