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Interest rate data are an important element of macroeconomic forecasting. Projections of future interest rates are not only an important product themselves, but also typically matter for forecasting other macroeconomic and financial variables. A popular class of forecasting models is linear vector autoregressions (VARs) that include shorter- and longer-term interest rates. However, in a number of economies, at least shorter-term interest rates have now been stuck for years at or near their effective lower bound (ELB), with longer-term rates drifting toward the constraint as well. In such an environment, linear forecasting models that ignore the ELB constraint on nominal interest rates appear inept. To handle the ELB on interest rates, we model observed rates as censored observations of a latent shadow-rate process in an otherwise standard VAR setup. The shadow rates are assumed to be equal to observed rates when above the ELB. Point and density forecasts for interest rates (short term and long term) constructed from a shadow-rate VAR for the US since 2009 are superior to predictions from a standard VAR that ignores the ELB. For other indicators of financial conditions and measures of economic activity and inflation, the accuracy of forecasts from our shadow-rate specification is on par with a standard VAR that ignores the ELB.

Modeling nominal interest rates requires their effective lower bound (ELB) to be taken into account. We propose a flexible time series approach that includes a "shadow rate" - a notional rate identical to the actual nominal rate except when the ELB binds. We apply this approach to a trend-cycle decomposition of interest rates and macroeconomic variables that generates competitive interest-rate forecasts. Our estimates of the real-rate trend have edged down somewhat in recent decades, but not significantly so. We identify monetary policy shocks from shadow-rate surprises and find that they were particularly effective at stimulating economic activity during the ELB period.

Stylized facts on U.S. output and interest rates have so far proved hard to match with DSGE models. But model predictions hinge on the joint specification of economic structure and a set of driving processes. In a model, different shocks often induce different comovements, such that the overall pattern depends as much on the specified transmission mechanisms from shocks to outcomes, as well as on the composition of these driving processes. I estimate covariances between output, nominal and real interest rate conditional on several shocks, since such evidence has largely been lacking in previous discussions of the output-interest rate puzzle. Conditional on shocks to neutral technology and monetary policy, the results square with simple models, like the standard RBC model or a textbook version of the New Keynesian model. In addition, news about future productivity help to explain the overall counter-cyclical behavior of the real rate. A sub-sample analysis documents also interesting changes in these pattern. During the Great Inflation (1959-1979), permanent shocks to inflation accounted for the counter-cyclical behavior of the real rate and its inverted leading indicator property. Over the Great Moderation (1982-2006), neutral technology shocks were more dominant in explaining comovements between output and interest rates, and the real rate has been pro-cyclical.

In response to the global financial crisis, the Federal Open Market Committee lowered the target for the federal funds rate to a range of 0 to 25 basis points in December 2008, and maintained that target range until the end of 2015. Over that same period, longer-term interest rates in the United States were at historically low levels.

This paper studies the joint dynamics of U.S. inflation and the average inflation predictions of the Survey of Professional Forecasters (SPF) on a sample running from 1968Q4 to 2014Q2. The joint data generating process (DGP) of these data consists of the unobserved components (UC) model of Stock and Watson (2007, "Why has US inflation become harder to forecast?," Journal of Money, Credit and Banking 39(S1), 3-33) and the sticky information (SI) forecast updating equation of Mankiw and Reis (2002, "Sticky information versus sticky prices: A proposal to replace the New Keynesian Phillips curve," Quarterly Journal of Economics 117, 1295-1328). We introduce timevarying inflation gap persistence into the Stock and Watson (SW)-UC model and a timevarying frequency of forecast updating into the SI forecast updating equating. These models combine to produce a nonlinear state space model. This model is estimated using Bayesian tools grounded in the particle filter, which is an implementation of sequential Monte Carlo methods. The estimates reveal the data prefer the joint DGP of time-varying frequency of SI forecast updating and a SW-UC model with time-varying persistence. The joint DGP produces estimates that indicate the inflation spike of 1974 was explained most by gap inflation, but trend inflation dominates the inflation peak of the early 1980s. We also find the stochastic volatility (SV) of trend inflation exhibits negative co-movement with the time-varying frequency of SI forecast updating while the SV and time-varying persistence of gap inflation often show positive co-movement. Thus, the average SPF respondent is most sensitive to the impact of permanent shocks on the conditional mean of inflation.

Optimal monetary policy becomes tricky when the central bank has better information than the public: Policy does not only affect economic fundamentals, but also people’s beliefs. For a general class of widely studied DSGE models, this paper derives the optimal discretionary policy under hidden information. Illustrated with a simple New Keynesian model, the introduction of hidden information has striking effects on discretionary policies: Policy losses are better under hidden information than under full transparency. Looking at Markov-perfect policies excludes reputational mechanisms via history dependent strategies. Under full transparency, discretion policies are then myopic, since a current policymaker cannot influence future decisions. But imperfect information adds public beliefs as a distinct, endogenous state variable. Managing beliefs connects the actions of policymakers such that they realize the inflationary consequences of expansionary policies. The optimal policy shares similarities with those from commitment models. Additionally, disinflations are pursued more vigorously the larger the credibility problems from hidden information. Optimal policy also responds to belief shocks, which shift public perceptions about fundamentals even when those fundamentals are unchanged.

We derive estimates of trend inflation for fourteen advanced economies from a framework in which trend shocks exhibit stochastic volatility. The estimated specification allows for time-variation in the degree to which longer-term inflation expectations are well anchored in each economy. Our results bring out the effect of changes in monetary regime (such as the adoption of inflation targeting in several countries) on the behavior of trend inflation. Our estimates expand on the previous literature in several dimensions: For each country, we employ a multivariate approach that pools different inflation series in order to identify their common trend. In addition, our estimates of the inflation gap—defined as the difference between trend and observed inflation—are allowed to exhibit considerable persistence. Consequently, the fluctuations in estimates of trend inflation are much lower than those reported in studies that use stochastic volatility models in which inflation gaps are serially uncorrelated. This specification also makes our estimates less sensitive than trend estimates in the literature to the effect of distortions to inflation arising from non-market influences on prices, such as tax changes. A forecast evaluation based on pseudo-real-time estimates documents improvements in inflation forecasts, even though it remains hard to outperform simple random walk forecasts to a statistically significant degree.