A simple calculation of r-star
In this paper, Bofinger and Haas propose an approach to estimating the neutral rate of interest by utilizing a simple New Keynesian macro model where the central bank sets its interest rate in a way that minimizes the output gap y and the deviation of the inflation rate π from its target value. The central bank thus follows a loss function that includes the inflation gap and the output gap. Both terms are squared since the objectives are symmetric, and a preference parameter weights the relative importance of the two terms. This approach enables the authors to observe neutral periods i.e. periods when the actual real policy rate is close to the neutral rate as there are no exogenous shocks. From these neutral periods, the authors compute a moving average to smooth the adjustments between different manifestations (neutral and non-neutral). Applied to the U.S. and the Euro area, their estimated neutral rate of interest shows the existence of :
[A] prolonged period of loose monetary policy in the years leading up to the financial crisis, followed by an abrupt tightening just before the onset of the crisis. Our measure of the neutral rate is also insensitive to the extreme but brief COVID-19 recession, suggesting that it is mainly a temporary shock for the time being.
R-star: An alternative approach to estimating the polar star of monetary policy
Authors: Peter Bofinger, Thomas Haas
From: University of Wuerzburg
A different approach to estimating the natural rate
In this paper, Bäcker-Peral et al. take a different approach to measuring the natural rate of return by using natural experiments and microdata instead. They argue that this approach is less sensitive to misspecification issues and standard errors are of smaller magnitude. More specifically, they measure the market’s expectation of the natural rate of return on capital, or the long run discount rate on capital, which is a function of three elements: a) the natural rate of interest, b) the natural risk premium of on capital, and c) the long run expected capital gain. Their methodology follows three steps:
- They develop a dynamic real-time estimate of the natural rate of return on capital for housing using a natural experiment approach in the UK from 2003 to the present. Most apartments in the U.K. are sold as “leaseholds”—long-duration leases starting at ninety years or more, issued by the ultimate owner of the property, or “freeholder”. The leaseholder can buy or sell the lease, giving each lease a series of market prices. Moreover, leaseholders have the right to extend their lease conditional on paying freeholders the value of the lease extension.
- They assemble a new data set on leasehold extensions and transactions from 2003 to the present. The authors estimate the increase in the market value of a leasehold due to duration extension, by comparing its price before and after lease extension with an otherwise similar control group of leaseholds that do not get extended at the same time.
- They embed this difference-in-differences estimate of the market value of lease duration extension into a simple discounted cash-flow pricing equation. The equation allows the estimation of the market’s expected natural rate of return on capital via non-linear least squares, using over 130,000 lease extension experiments from 2003 onward.
They find that the natural rate of return on capital in the UK fell from an average of 4.8% between 2003 to 2006 to 2.3% in 2022.
Natural rates are valuable because they “look through” the short-term factors affecting asset prices in real time. There has been a growing gap between natural rates and real forward yields in 2022-23. This gap will narrow either by a fall in spot real yields, as the “secular stagnation view” suggest, or a rise in natural rates if post-covid era reflects a structural regime shift. The consequences of these two scenarios for asset prices and the real economy could of course be very different.
Measuring The Natural Rate Using Natural Experiments
Authors: Verónica Bäcker-Peral, Jonathon Hazell, Atif Mian
From: Princeton University, LSE
