Glenn D. Rudebusch

Term structure evidence on interest rate smoothing and monetary policy inertia

Numerous studies have used quarterly data to estimate monetary policy rules or reaction functions that appear to exhibit a very slow partial adjustment of the policy interest rate. The conventional wisdom asserts that this gradual adjustment reflects a policy inertia or interest rate smoothing behavior by central banks. However, such quarterly monetary policy inertia would imply a large amount of forecastable variation in interest rates at horizons of more than three months, which is contradicted by evidence from the term structure of interest rates. The illusion of monetary policy inertia evident in the estimated policy rules likely reflects the persistent shocks that central banks face.

The macroeconomy and the yield curve: a dynamic latent factor approach

We estimate a model that summarizes the yield curve using latent factors (specifically, level, slope, and curvature) and also includes observable macroeconomic variables (specifically, real activity, inflation, and the monetary policy instrument). Our goal is to provide a characterization of the dynamic interactions between the macroeconomy and the yield curve. We find strong evidence of the effects of macro variables on future movements in the yield curve and evidence for a reverse influence as well. We also relate our results to the expectations hypothesis.

Do Measures of Monetary Policy in a VAR Make Sense

No. In many VARs, monetary policy shocks are identified with the least squares residuals from a regression of the federal funds rate on an assortment of variables. Such regressions appear to be structurally fragile and are at odds with other evidence on the nature of the Feds reaction function; furthermore, the residuals from these regressions have little correlation with funds rate shocks that are derived from forward-looking financial markets.

Federal Reserve interest rate targeting, rational expectations, and the term structure

The amount of information in the yield curve for forecasting future changes in short rates varies with the maturity of the rates involved. Indeed, spreads between certain long and short rates appear unrelated to future changes in the short rate--contrary to the rational expectations hypothesis of the term structure. This paper estimates a daily model of Federal Reserve interest rate targeting behavior, which, accompanied by the maintained hypothesis of rational expectations, explains the varying predictive ability of the yield curve and elucidates the link between Fed policy and the term structure.

A macro-finance model of the term structure, monetary policy, and the economy

This paper develops and estimates a macro-finance model that combines a canonical affine no-arbitrage finance specification of the term structure with standard macroeconomic aggregate relationships for output and inflation. From this new empirical formulation, we obtain several important results: (1) the latent term structure factors from finance no-arbitrage models appear to have important macroeconomic and monetary policy underpinnings, (2) there is no evidence of monetary policy inertia or a slow partial adjustment of the policy interest rate by the Federal Reserve, and (3) both forward-looking and backward-looking elements play important roles in macroeconomic dynamics.

On the power of Dickey-Fuller tests against fractional alternatives

Abstract We examine the properties of Dickey-Fuller unit root tests under fractionally-integrated alternatives and find that these tests have quite low power.

Assessing nominal income rules for monetary policy with model and data uncertainty

Nominal income rules for monetary policy have long been debated, but two issues are of particular recent interest. First, there are questions about the performance of such rules over a range of plausible empirical models---especially models with and without rational inflation expectations. Second, there are questions about the performance of these rules in real time using the type of data that is actually available contemporaneously to policymakers rather than final revised data. This paper determines optimal monetary policy rules in the presence of such model uncertainty and real-time data uncertainty and finds only a limited role for nominal output growth.

Scoring the Leading Indicators

The authors evaluate the ability of the composite index of leading indicators to predict business cycle turning points. Formal probability-assessment scoring rules are applied to turning-point probabilities generated from the leading index via a Bayesian sequential probability recursion. These scoring rules enable rigorous and systematic evaluation of leading indicator forecasts. The results are used to assess the merits of forecasting with the composite leading index and to suggest possible improvements in its construction. Copyright 1989 by the University of Chicago.(This abstract was borrowed from another version of this item.)

Is the Fed too timid? Monetary policy in an uncertain world

Estimates of the Taylor rule using historical data from the past decade or two suggest that monetary policy in the U.S. can be characterized as having reacted in a moderate fashion to output and inflation gaps. In contrast, the parameters of optimal Taylor rules derived using empirical models of the economy often recommend much more vigorous policy responses. This paper attempts to match the historical policy rule with an optimal policy rule by incorporating uncertainty into the derivation of the optimal rule and by examining plausible variations in the policymakers model and preferences.

Forecasting Output with the Composite Leading Index: A Real-Time Analysis

Abstract We examine the ability of the composite index of leading economic indicators to predict future movements in aggregate economic activity. Previous examinations of predictive performance have evaluated either the in-sample residual errors from a forecasting equation fitted to the entire sample of data or the out-of-sample forecast errors from an equation fitted to a subsample of the data. Unlike previous evaluations, we perform a real-time analysis, which uses the provisional and partially revised data for the leading index that were actually available historically, along with recursive out-of-sample forecasts. We find a substantial deterioration of forecasting performance in the real-time framework.