U.S. estimates of the natural rate of interest -the real short-term interest rate that would prevail absent transitory disturbances -have declined dramatically since the start of the global financial crisis. For example, estimates using the Laubach-Williams (2003) model indicate the natural rate in the United States fell to close to zero during the crisis and has remained there through the end of 2015. Explanations for this decline include shifts in demographics, a slowdown in trend productivity growth, and global factors affecting real interest rates. This paper applies the Laubach-Williams methodology to the United States and three other advanced economies -Canada, the Euro Area, and the United Kingdom. We find that large declines in trend GDP growth and natural rates of interest have occurred over the past 25 years in all four economies. These country-by-country estimates are found to display a substantial amount of comovement over time, suggesting an important role for global factors in shaping trend growth and natural rates of interest.
At the center of the financial market crisis of 2007-2008 was a highly unusual jump in spreads between the overnight inter-bank lending rate and term London inter-bank offer rates (Libor). Because many private loans are linked to Libor rates, the sharp increase in these spreads raised the cost of borrowing and interfered with monetary policy. The widening spreads became a major focus of the Federal Reserve, which took several actions-including the introduction of a new term auction facility (TAF)-to reduce them. This paper documents these developments and, using a no-arbitrage model of the term structure, tests various explanations, including increased risk and greater liquidity demands, while controlling for expectations of future interest rates. We show that increased counterparty risk between banks contributed to the rise in spreads and find no empirical evidence that the TAF has reduced spreads. The results have implications for monetary policy and financial economics.
Shifts in the long-run rate of productivity growth-such as those experienced by the U.S. economy in the 1970s and 1990s-are difficult, in real time, to distinguish from transitory fluctuations. In this paper, we analyze the evolution of forecasts of longrun productivity growth during the 1970s and 1990s and examine in the context of a dynamic general equilibrium model the consequences of gradual real-time learning on the responses to shifts in the long-run productivity growth rate. We find that a simple updating rule based on an estimated Kalman filter model using real-time data describes economists' long-run productivity growth forecasts during these periods extremely well. We then show that incorporating this process of learning has profound implications for the effects of shifts in trend productivity growth and can dramatically improve the model's ability to generate responses that resemble historical experience. If immediately recognized, an increase in the long-run growth rate causes long-term interest rates to rise and produces a sharp decline in employment and investment, contrary to the experiences of the 1970s and 1990s. In contrast, with learning, a rise in the long-run rate of productivity growth sets off a sustained boom in employment and investment, with long-term interest rates rising only gradually. We find the characterization of learning to be crucial regardless of whether shifts in long-run productivity growth owe to movements in TFP growth concentrated in the investment goods sector or economy-wide TFP.
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