Journal Article

No. 2007-8 | July 11, 2007
Evaluating Inflation Targeting Using a Macroeconometric Model PDF Icon

Abstract

This paper uses a structurally estimated macroeconometric model, denoted the MC model, to evaluate inflation targeting in the United States. Various interest rate rules are tried with differing weights on inflation and output, and various optimal control problems are solved using differing weights on inflation and output targets. Price-level targeting is also considered. The results show that 1) there are output costs to inflation targeting, especially for price shocks, 2) price-level targeting is dominated by inflation targeting, 3) the estimated interest rate rule of the Fed (in Table 4) is consistent with the Fed placing equal weights on inflation and unemployment in a loss function, 4) the estimated interest rate rule does a fairly good job at lowering variability, and 5) considerable economic variability is left after the Fed has done its best. Overall, the results suggest that the Fed should continue to behave as it has in the past.

JEL Classification

E52

Citation

Ray C. Fair (2007). Evaluating Inflation Targeting Using a Macroeconometric Model. Economics: The Open-Access, Open-Assessment E-Journal, 1 (2007-8): 1—52. http://dx.doi.org/10.5018/economics-ejournal.ja.2007-8

Assessment

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Citations (@RePEc): 4
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Comments and Questions


Warren Coats - Fair's paper on Inflation Targeting
August 27, 2007 - 01:47

Fair’s paper attempts to evaluate n to our assessments of inflation forecast targeting (IFT), but suffers from an important limitation. One of IFT’s central claims to superiority is that it should be capable of better aligning the public’s expected inflation with the central banks inflation target. This improves the trade ...[more]

... off between inflation and output. Because Fair’s model uses backward looking adaptive expectations, it is in capable of capturing this effect and is thus deficient as a means of simulating the performance of IFT.

Fair’s justification for using his model rather than NK models is partly out of date. He argues that “NK models are not likely to be good enough approximations of the economy to be trustworthy for evaluating inflation targeting.” His first reason is that these models ignore the government and foreign sectors. However, this is not true of recent models (see, for example, the IMF Working Paper – WP/07/200— “DSGE Modeling at the Fund
Applications and Further Developments.”). He also believes that his more richly detailed model will better track the economy better and it thus better for evaluating IFT. IFT advocates prefer simpler, easier to under stand models because they are easier for the actual policy makers to follow and thus actually use. While Fair is on firmer ground here, which class of models is better for evaluating IFT is an empirical question, which Fair is unable to resolve given the very nature of his tests.


Ray Fair - Reply to Warren Coats
September 17, 2007 - 20:23

I agree with Coats that which class of models is better for evaluating IFT is an empirical question. Simple models that policy makers can understand and use are not good if they are poor approximations of the economy. So the key question is how accurate any given model ...[more]

... is. I do have a way of getting at this question in my paper, which is Table 1, where outside sample RMSEs are compared. The NK model analyzed by Del Negro et al. (2006) is very poor at predicting real output compared to my model, which suggests to me that the NK AD curve is way too simple. I think the challenge for the NK literature in future work is to see if models can be developed that do well in comparisons like those in Table 1. I am doubtful that this will happen because I think the assumption of rational expectations is not a good approximation of the way that expectations are actually formed, but time will tell. In the meantime I don't think it is fair to say (no pun intended) that my model suffers from the limitation that expectations are assumed to be adaptive. Table 1 suggests that this may be the most accurate assumption!