tex Response to Seitz and Tödter , “ How the P ¤ Model Rationalises Monetary Targeting — A Comment on Svensson ”

Abstract

Seitz and Tödter argue, counter to Svensson, that the P ¤ model provides a rationale for money-growth targeting. In particular, they argue that " money growth targeting is a special form of in ‡ation forecast targeting based on a 'limited' information set. In contrast to 'full information' in ‡ation forecast targeting, money growth targeting is likely to be more robust under changing conditions of the real world. " However, money-growth targeting is better described as a special case of in ‡ation targeting , namely when money growth is considered to be the only predictor of future in ‡ation. But there is overwhelming empirical evidence that there are not only other, but better, predictors of future in ‡ation than money growth, which makes in ‡ation-forecast targeting superior to money-growth targeting. In ‡ation-forecast targeting is indeed more robust (in the sense of using available information and allowing judgemental adjustments in a ‡exible way) than monetary targeting. In particular, in the P ¤ model, the real money gap is a better predictor of future in ‡ation than money growth, as demonstrated theoretically by Svensson and empirically by Gerlach and Svensson (the empirical …nding is also con…rmed by Trecroci and Vega). Therefore, in ‡ation-forecast targeting is superior also within the P ¤ model. Under " changing conditions of the real world, " for instance, after the formation of a monetary union, money growth is likely to be particularly unreliable as a predictor of future in ‡ation, making monetary targeting especially unsuitable and nonrobust. Seitz and Tödter [4] argue, counter to Svensson [6], that the P ¤ model provides a rationale for money-growth targeting. In particular, they argue that " money growth targeting is a special form of in ‡ation forecast targeting based on a 'limited' information set. In contrast to 'full information' in ‡ation forecast targeting, money growth targeting is likely to be more robust under changing conditions of the real world. "

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