From 1960-1983, the standard deviation of annual growth rates in real GDP in the United States was 2.7%. From 1984-2001, the corresponding standard deviation was 1.6%. This paper investigates this large drop in the cyclical volatility OF real economic.activity. The paper has two objectives. The first is to provide a comprehensive characterization of the decline in volatility using a large number of U.S. economic time series and a variety of methods designed to describe time-varying time series processes. In so doing, the paper reviews the literature on the moderation and attempts to resolve some of its disagreements and discrepancies. The second objective is to provide new evidence on the quantitative importance of various explanations for this "great moderation". Taken together, we estimate that the moderation in volatility is attributable to a combination of improved policy (20-30%), identifiable good luck in the form of productivity and commodity price shocks (20-30%), and other unknown forms of good luck that manifest themselves as smaller reduced-form forecast errors (40-60%).

This paper provides a novel approach to forecasting time series subject to discrete structural breaks. We propose a Bayesian estimation and prediction procedure that allows for the possibility of new breaks over the forecast horizon, taking account of the size and duration of past breaks (if any) by means of a hierarchical hidden Markov chain model. Predictions are formed by integrating over the hyper parameters from the meta distributions that characterize the stochastic break point process. In an application to US Treasury bill rates, we find that the method leads to better out-of-sample forecasts than alternative methods that ignore breaks, particularly at long horizons.

Abstract not found

are preliminary materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the research staff or the Board of Governors. References in publications to the Finance and Economics Discussion Series (other than acknowledgement) should be cleared with the author(s) to protect the tentative character of these papers.

This paper provides an explanation for the run-up of US inflation in the 1960s and 1970s and the sharp disinflation in the early 1980s, which standard macroeconomic models have difficulties in addressing. I present a model in which rational policymakers learn about the behavior of the economy in real time and set stabilization policy optimally, conditional on their current beliefs. The steady state associated with the model’s self-confirming equilibrium is characterized by low inflation. However, prolonged and asymmetric episodes of high inflation can occur when policymakers underestimate both the non-accelerating inflation rate of unemployment and the persistence of inflation in the Phillips curve. I estimate the model using likelihood methods. The estimation results show that the model accounts remarkably well for the evolution of policymakers’ beliefs, stabilization policy and the postwar behavior of inflation and unemployment in the United States.

Abstract not found

Abstract not found

Models of the term structure based on only observable variables have had limited success in explaining movements in longer-term interest rates. A key assumption in much of this literature is that agents know all the parameters describing the model of the economy and that these parameters are fixed for all time. In this paper, we relax both of these assumptions and assume that agents regularly re-estimate the parameters of their models–both those determining the point forecasts and those describing economic volatility–based on incoming data. In this way, we allow for the real-time problem of pricing assets based on the information set available at the time. In addition, we allow for discounting of past data reflecting a concern on the part of agents for structural change in the economy. We find that the learning model with discounting does a much better job at explaining longer-term yields than an equivalent model with constant coefficients estimated over the full sample; in particular, the deviations from the expectations

The views expressed are those of the individual authors and do not necessarily reflect official positions of the Federal Reserve Bank of St. Louis, the Federal Reserve System, or the Board of Governors. Federal Reserve Bank of St. Louis Working Papers are preliminary materials circulated to stimulate discussion and critical comment. References in publications to Federal Reserve Bank of St. Louis Working Papers (other than an acknowledgment that the writer has had access to unpublished material) should be cleared with the author or authors.

The views expressed are those of the individual authors and do not necessarily reflect official positions of the Federal Reserve Bank of St. Louis, the Federal Reserve System, or the Board of Governors. Federal Reserve Bank of St. Louis Working Papers are preliminary materials circulated to stimulate discussion and critical comment. References in publications to Federal Reserve Bank of St. Louis Working Papers (other than an acknowledgment that the writer has had access to unpublished material) should be cleared with the author or authors.