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Keywords:survey forecasts 

Report
The FRBNY staff underlying inflation gauge: UIG

Monetary policymakers and long-term investors would benefit greatly from a measure of underlying inflation that uses all relevant information, is available in real time, and forecasts inflation better than traditional underlying inflation measures such as core inflation measures. This paper presents the ?FRBNY Staff Underlying Inflation Gauge (UIG)? for CPI and PCE. Using a dynamic factor model approach, the UIG is derived from a broad data set that extends beyond price series to include a wide range of nominal, real, and financial variables. It also considers the specific and time-varying ...
Staff Reports , Paper 672

Report
The term structure of expectations and bond yields

Bond yields can be decomposed into expected short rates and term premiums. We directly measure the former using all available U.S. professional forecasts and obtain the latter as the difference between bond yields and survey-based expected short rates. While the behavior of nominal and real short rate expectations is consistent with standard macroeconomic theory, term premiums account for the bulk of the cross-sectional and time series variation in yields. They also largely explain the yield curve's reaction to a host of structural economic shocks. This dramatic failure of the expectations ...
Staff Reports , Paper 775

Report
Fundamental Disagreement about Monetary Policy and the Term Structure of Interest Rates

Using a unique data set of individual professional forecasts, we document disagreement about the future path of monetary policy, particularly at longer horizons. The stark differences in short rate forecasts imply strong disagreement about the risk-return trade-off of longer-term bonds. Longer-horizon short rate disagreement co-moves with term premiums. We estimate an affine term structure model in which investors hold heterogeneous beliefs about the long-run level of rates. Our model fits U.S. Treasury yields and the short rate paths predicted by different groups of professional forecasters ...
Staff Reports , Paper 934

Discussion Paper
What Drives Forecaster Disagreement about Monetary Policy?

What can disagreement teach us about how private forecasters perceive the conduct of monetary policy? In a previous post, we showed that private forecasters disagree about both the short-term and the long-term evolution of key macroeconomic variables but that the shape of this disagreement differs across variables. In contrast to their views on other macroeconomic variables, private forecasters disagree substantially about the level of the federal funds rate that will prevail in the medium to long term but very little on the rate at shorter horizons. In this post, we explore the possible ...
Liberty Street Economics , Paper 20160815

Working Paper
Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors

We develop uncertainty measures for point forecasts from surveys such as the Survey of Professional Forecasters, Blue Chip, or the Federal Open Market Committee's Summary of Economic Projections. At a given point of time, these surveys provide forecasts for macroeconomic variables at multiple horizons. To track time-varying uncertainty in the associated forecast errors, we derive a multiple-horizon specification of stochastic volatility. Compared to constant-variance approaches, our stochastic-volatility model improves the accuracy of uncertainty measures for survey forecasts.
Working Papers , Paper 2017-026

Working Paper
News-driven uncertainty fluctuations

We embed a news shock, a noisy indicator of the future state, in a two-state Markov-switching growth model. Our framework, combined with parameter learning, features rich history-dependent uncertainty dynamics. We show that bad news that arrives during a prolonged economic boom can trigger a ?Minsky moment??a sudden collapse in asset values. The effect is greatly amplified when agents have a preference for early resolution of uncertainty. We leverage survey recession probability forecasts to solve a sequential learning problem and estimate the full posterior distribution of model primitives. ...
Working Papers , Paper 18-3

Working Paper
Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors

We develop uncertainty measures for point forecasts from surveys such as the Survey of Professional Forecasters, Blue Chip, or the Federal Open Market Committee?s Summary of Economic Projections. At a given point of time, these surveys provide forecasts for macroeconomic variables at multiple horizons. To track time-varying uncertainty in the associated forecast errors, we derive a multiple-horizon specification of stochastic volatility. Compared to constant-variance approaches, our stochastic-volatility model improves the accuracy of uncertainty measures for survey forecasts.
Working Papers (Old Series) , Paper 1715

Working Paper
Combining Survey Long-Run Forecasts and Nowcasts with BVAR Forecasts Using Relative Entropy

This paper constructs hybrid forecasts that combine both short- and long-term conditioning information from external surveys with forecasts from a standard fixed-coefficient vector autoregression (VAR) model. Specifically, we use relative entropy to tilt one-step ahead and long-horizon VAR forecasts to match the nowcast and long-horizon forecast from the Survey of Professional Forecasters. The results indicate meaningful gains in multi-horizon forecast accuracy relative to model forecasts that do not incorporate long-term survey conditions. The accuracy gains are achieved for a range of ...
Working Papers (Old Series) , Paper 1809

Working Paper
Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors

We estimate uncertainty measures for point forecasts obtained from survey data, pooling information embedded in observed forecast errors for different forecast horizons. To track time-varying uncertainty in the associated forecast errors, we derive a multiple-horizon specification of stochastic volatility. We apply our method to forecasts for various macroeconomic variables from the Survey of Professional Forecasters. Compared to constant variance approaches, our stochastic volatility model improves the accuracy of uncertainty measures for survey forecasts. Our method can also be applied to ...
Working Papers , Paper 201715R

Working Paper
Macroeconomic Uncertainty Through the Lens of Professional Forecasters

We analyze the evolution of macroeconomic uncertainty in the United States, based on the forecast errors of consensus survey forecasts of various economic indicators. Comprehensive information contained in the survey forecasts enables us to capture a real-time subjective measure of uncertainty in a simple framework. We jointly model and estimate macroeconomic (common) and indicator-specific uncertainties of four indicators, using a factor stochastic volatility model. Our macroeconomic uncertainty has three major spikes aligned with the 1973?75, 1980, and 2007?09 recessions, while other ...
Working Papers , Paper 1702

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