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$GS Expects Forward Looking Dovish Statements From the Fed on Rate Hike Threshold

“The extreme experiment of current US monetary policy has evolved (as we noted yesterday), from explicit end-dates, to unlimited end-dates, to threshold-based end-dates. Of course, this ‘threshold’ was no problem for the liquidty whores when unemployment rates were extremely high themselves, but as the world awoke to what we have been pointing out – that it’s all a mirage of collapsing participation rates – the FOMC (and sell-side strategists) realized that the endgame may be ‘too close’. Cue Goldman’s Jan Hatzius, who in today’s note, citing two influential Fed staff economists, shifts the base case and forecasts that the Fed will lower its threshold for rate hikes to 6.0% (and perhaps as low as 5.5%) as early as December (as a dovish forward-guidance balance to an expected Taper announcement).

 

Via Goldman Sachs,

  • The most senior Fed staff economists for monetary policy analysis and domestic macroeconomics, William English and David Wilcox, havepublished separate studies that imply a strong case for a reduction in the 6.5% unemployment threshold for the first funds rate hike. We have proposed such a move for some time, but have been unsure whether it would in fact happen. And while the uncertainty around near-term Fed policy remains very considerable, our baseline view is now that the FOMC will reduce its 6.5% threshold to 6% at the March 2014 FOMC meeting, alongside the first tapering of QE. A move as early as the December 2013 meeting is possible, and if so, this might also increase the probability of an earlier tapering of QE.

It is hard to overstate the importance of two new Fed staff studies that will be presented at the IMF’s annual research conference on November 7-8. The lead author for the first study is William English, who is the director of the Monetary Affairs division and the Secretary and Economist of the FOMC. The lead author for the second study is David Wilcox, who is the director of the Research and Statistics division and the Economist of the FOMC. The fact that the two most senior Board staffers in the areas of monetary policy analysis and domestic macroeconomics have simultaneously published detailed research papers on central issues of the economic and monetary policy outlook is highly unusual and noteworthy in its own right. But the content and implications of these papers are even more striking.

It will take us some time to absorb the sizable amounts of new analysis in the two studies, and we are only able to comment on a few selected aspects at this point. But our initial assessment is that they considerably increase the probability that the FOMC will reduce its 6.5% unemployment threshold for the first hike in the federal funds rate, either coincident with the first tapering of its QE program or before.

The first study, written by William English, David Lopez-Salido, and Robert Tetlow and entitled “The Federal Reserve’s Framework for Monetary Policy–Recent Changes and New Questions,” uses a smaller version of the staff’s large-scale econometric model FRB/US to analyze the optimal path for the federal funds rate. Using “small FRB/US,” a set of assumptions about Fed preferences, and a set of assumptions about the baseline performance of the economy, the authors find that the theoretically optimal policy involves a commitment to hold the federal funds rate near zero until 2017, followed by a series of hikes that push the rate well above neutral by the early 2020s. In this simulation, the unemployment rate falls below the structural rate for a time, and inflation rises modestly above the 2% target. (The optimal policy in the English et al. study is more aggressive than that shown in Vice Chair Yellen’s earlier set of optimal control simulations, which points to the first hike in early 2016; the reasons seem to include a lower assumption for the structural unemployment rate and a later baseline for the first hike in the funds rate.)

However, the authors note that such an optimal policy is possibly infeasible because it is complex and model-dependent….”

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