Have interest rates been held “too low†in relation to the natural rate of interest? Economists have lately begun to worry that the cost of capital may have fallen below the worldwide expected return on capital, thereby causing excessive borrowing and allowing financial imbalances to build up. On the basis of a dynamic optimizing business cycle model satisfying the natural rate hypothesis, this paper provides an evaluation of natural interest rate estimates under alternative hypotheses and model specifications. To do so, particle filtering methods are employed. The idea underlying this approach is to represent the distribution of interest by a large number of random samples, or particles, evolving over time on the basis of a simulation-based updating scheme, so that new observations are incorporated in the filter as they become available. Unlike Kalman filters, particle filters do not require linearity and Gaussianity assumptions. We show that by lessening the influence of extreme noise observations via heavy-tailed innovations, the uncertainty around time-varying estimates is trimmed down even over small samples
