Search Results
Working Paper
The Role of Learning for Asset Prices and Business Cycles
I examine the implications of learning-based asset pricing in a model in which firms face credit constraints that depend partly on their market value. Agents learn about stock prices, but have conditionally model-consistent expectations otherwise. The model jointly matches key asset price and business cycle statistics, while the combination of financial frictions and learning produces powerful feedback between asset prices and real activity, adding substantial amplification. The model reproduces many patterns of forecast error predictability in survey data that are inconsistent with rational ...
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 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.
Report
The Term Structure of Expectations
Economic theory predicts that intertemporal decisions depend critically on expectations about future outcomes. Using the universe of professional survey forecasts for the United States, we document the behavior of the entire term structure of expectations for output growth, inflation, and the policy rate. We show that a simple unobserved components model of the trend and cycle explains the joint behavior of both consensus measures of expectations and the observed disagreement among individual forecasters. Importantly, univariate models of each variable are outperformed by a multivariate model ...
Report
Fundamental disagreement
We use the term structure of disagreement of professional forecasters to document a novel set of facts: (1) forecasters disagree at all horizons, including the long run; (2) the term structure of disagreement differs markedly across variables: it is downward sloping for real output growth, relatively flat for inflation, and upward sloping for the federal funds rate; (3) disagreement is time-varying at all horizons, including the long run. These new facts present a challenge to benchmark models of expectation formation based on informational frictions. We show that these models require two ...
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 ...
Discussion Paper
Fundamental Disagreement: How Much and Why?
Everyone disagrees, even professional forecasters, especially about big economic questions. Has potential output growth changed since the financial crisis? Are we bound for a period of “secular stagnation”? Will the European economy rebound? When is inflation getting back to mandate-consistent level? In this post, we document to what degree professional forecasters disagree and discuss potential reasons why. In a recent working paper, we document a set of novel facts about disagreement among professional forecasters over the last thirty years. We focus on the “trinity” of U.S. output ...
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.
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 ...
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 ...