Work in Progress

How Structural Shocks Shift the Term Structure of SPF Expectations

with Todd E. Clark (Johns Hopkins University)

Abstract: Professional forecasts play a crucial role in shaping expectations and guiding policy decisions. Among these, the Survey of Professional Forecasters (SPF) provides a uniquely detailed and long-standing record of macroeconomic expectations. In their recent work, Clark, Ganics, and Mertens (2025, forthcoming REStat; hereafter CGM) developed a state-space framework that reconstructs a rich, SPF-consistent term structure of expectations and forecast uncertainty by integrating the SPF’s fixed-horizon and fixed-event forecasts. This approach allows for the estimation of latent quarterly expectations at long horizons, even beyond the ragged edge of the SPF’s published data, while accounting for potential inefficiencies and time-varying volatility. Importantly, the CGM model also accommodates deviations from full-information rational expectations by allowing for serially correlated forecast updates, in the spirit of Coibion and Gorodnichenko (2015, AER) and Angeletos, Huo, and Sastry (2021, NBER Macro Annual). This feature captures the possibility of imperfect or delayed information processing by forecasters and proves critical for our analysis of how structural shocks are transmitted through expectations. 

Building on the CGM framework, this project investigates the sources of variation in the SPF's term structure of expectations. Specifically, we examine the extent to which updates in SPF-consistent forecasts can be explained by structural shocks related to uncertainty, monetary policy, and geopolitical developments. In doing so, we project the shifts in expectations across the term structure onto exogenous shock series, thereby providing a structural interpretation of the dynamics captured in the SPF forecasts.Our empirical strategy involves two key steps. First, we extract the time series of SPF-consistent term structures of expectations and their updates using the CGM model. These updates, which reflect revisions to forecasters’ beliefs about future macroeconomic outcomes, serve as our dependent variables. Second, we regress these updates onto a set of structural shock proxies. These include widely used measures of economic and policy uncertainty (e.g., the Economic Policy Uncertainty Index), high-frequency monetary policy shocks derived from surprise components around FOMC announcements, and indices capturing geopolitical risk (e.g., Caldara and Iacoviello’s Geopolitical Risk Index; 2022, AER).

Our analysis proceeds across multiple dimensions of the SPF: real GDP growth, inflation (both CPI and GDP deflator), and the unemployment rate. For each variable, we estimate the responsiveness of short-, medium-, and long-term expectations to contemporaneous structural shocks. This decomposition allows us to quantify the horizon-specific impact of each shock, revealing, for instance, whether monetary policy surprises primarily influence near-term forecasts or have persistent effects extending further into the future. Overall, our results contribute to a deeper understanding of how structural shocks are embedded into professional forecasters’ expectations, and how these effects vary across horizons. By marrying the granular SPF term structures constructed by CGM with economically meaningful structural shock measures, this work enhances the interpretability of survey-based forecasts and provides insights into the transmission channels of macroeconomic shocks through expectations.

Analytical Solutions for Entropic Tilting of Model Outputs to SPF Histograms

with Todd E. Clark (Johns Hopkins University)

Abstract: This paper develops and applies analytical solutions for entropic tilting of predictive distributions to match the histogram forecasts provided in the U.S. Survey of Professional Forecasters (SPF). We focus on tilting to histogram probabilities directly---rather than to moments of fitted distributions---and derive analytic solutions to the tilting problem. We evaluate the method using updated SPF data and compare forecast accuracy with and without tilting. Our results suggest that direct tilting to histograms improves model calibration and predictive accuracy, particularly since the Global Financial Crisis.

Policymakers' misperceptions about ``r*''

with Giovanni  Nicolò (Federal Reserve Board)

Abstract: The post-pandemic increase in long-term Treasury yields raises questions about the level of the natural rate of interest, r*. Policymakers typically assess the stance of monetary policy relative to the natural rate of interest. However, r* is unobservable, and policymakers rely on real-time estimates generated from data on output, inflation, and other indicators.  We show that incorporating those estimates into the setting of monetary policy alters the systematic response to endogenous outcomes in ways that can violate the Taylor principle. Even when the policy rule is ``active'' in response to policymakers' real-time estimates of economic conditions, the effective policy rule---which arises from the effects of data on policymakers' real-time estimates---can violate the Taylor principle.  As a result, multiple equilibria may exist, and non-fundamental sunspot shocks can influence macroeconomic outcomes. A policy rule that responds only to observed outcomes, rather than estimates of latent variables like r*, would avoid such multiplicity.  

We consider a simple New Keynesian model in which policymakers observe aggregate output and inflation but not the underlying shocks to potential output and the supply side of the economy.  We model policymakers' estimates of r* as rational expectations that are consistent with equilibrium dynamics. While the true r* is driven only by shocks to potential output, policymakers' estimates of r* depend on observed data, which in turn leads to violations of the Taylor principle and multiple equilibria.  In this case, the resulting equilibrium is not unique. In addition, non-fundamental belief shocks distort policymakers’ ability to perfectly infer structural shocks---leading to imperfect inference about r*---and can contribute significantly to fluctuations in macroeconomic outcomes.  

We provide a unified framework in which policymakers believe that monetary policy is `active,'' while the policy response to actual data (instead of policymakers' real-time estimates) is consistent with multiple equilibrium outcomes. In this case, sunspot shocks emerge as important drivers of macroeconomic dynamics. Our framework integrates empirical results that find an active reaction function of monetary policy in terms of real-time estimates (Orphanides, 2001, AER), while also recognizing a role for non-fundamental sunspot shocks as drivers of inflation and output due to an inactive policy in terms of actual data (Clarida, Galì, and Gertler, 2000, QJE; Lubik and Schorfheide, 2004, AER). Extending earlier work by Lubik, Matthes, and Mertens (2022, RED), we derive analytic solutions for models in which imperfect information about r* faced by policymakers leads to equilibrium outcomes featuring sunspot-driven fluctuations. Our solution framework facilitates the estimation of such models.  

Natural-rate trends and time-varying uncertainty

Abstract: We present trend estimates of the natural rate of interest in the US and Europe that reflect time-varying uncertainty about economic shocks. Time-varying uncertainty about the relative importance of different shocks crucially affects natural-rate estimates. The natural rate of interest is not directly observed and can only be inferred from macroeconomic aggregates. When the role of different shocks changes over time, natural-rate estimates should also react differently to new data. A model that assumes constant uncertainty about the relative importance of different shocks will instead assign a constant signal-to-noise ratio to incoming data, which can lead to substantial revisions in natural-rate estimates. Moreover, while nominal interest rate data is a key input for estimating the natural rate, their dynamics have been notably affected over the last decade by a binding effective lower bound in many economies.

To address these issues, we extend earlier work by Johannsen and Mertens (2021, JMCB), which provided natural-rate estimates for the US that also reflected the effective lower bound. We adapt the model to euro-area data and generalize the treatment of time-varying volatility in shocks to trend and cyclical components. We also introduce a novel treatment of extreme data points, such as those observed during the COVID-19 pandemic, as stochastic outliers with random timing and magnitude. Our model filters the output gap from real GDP instead of relying on external estimates and implements a unified factor for cyclical volatility, which is robust to variable reordering. We highlight the importance of using extended data samples (including cross-country information) for more reliable filtering of natural rates from the data. A joint model of natural rates in the Euro area and the US is the subject of ongoing work.