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This paper presents a new approach to combining the information in point and density forecasts from the Survey of Professional Forecasters (SPF) and assesses the incremental value of the density forecasts. Our starting point is a model, developed in companion work, that constructs quarterly term structures of expectations and uncertainty from SPF point forecasts for quarterly fixed horizons and annual fixed events. We then employ entropic tilting to bring the density forecast information contained in the SPF’s probability bins to bear on the model estimates. In a novel application of entropic tilting, we let the resulting predictive densities exactly replicate the SPF’s probability bins. Our empirical analysis of SPF forecasts of GDP growth and inflation shows that tilting to the SPF’s probability bins can visibly affect our model-based predictive distributions. Yet in historical evaluations, tilting does not offer consistent benefits to forecast accuracy relative to the model-based densities that are centered on the SPF’s point forecasts and reflect the historical behavior of SPF forecast errors. That said, there can be periods in which tilting to the bin information helps forecast accuracy. Replication files are available at https://github.com/elmarmertens/ClarkGanicsMertensSPFfancharts

We develop a model that permits the estimation of a term structure of both expectations and forecast uncertainty for application to professional forecasts such as the Survey of Professional Forecasters (SPF). Our approach exactly replicates a given data set of predictions from the SPF (or a similar forecast source) without measurement error. Our model captures fixed horizon and fixed-event forecasts, and can accommodate changes in the maximal forecast horizon available from the SPF. The model casts a decomposition of multi-period forecast errors into a sequence of forecast updates that may be partially unobserved, resulting in a multivariate unobserved components model. In our empirical analysis, we provide quarterly term structures of expectations and uncertainty bands. Our preferred specification features stochastic volatility in forecast updates, which improves forecast performance and yields model estimates of forecast uncertainty that vary over time. We conclude by constructing SPF-based fan charts for calendar-year forecasts like those published by the Federal Reserve. Replication files are available at https://github.com/elmarmertens/ClarkGanicsMertensSPFfancharts.

We study equilibrium determination in an environment where two kinds of agents have different information sets: The fully informed agents know the structure of the model and observe histories of all exogenous and endogenous variables. The less in-formed agents observe only a strict subset of the full information set. All types of agents form expectations rationally, but agents with limited information need to solve a dynamic signal extraction problem to gather information about the variables they do not observe. We show that for parameters values that imply a unique equilibrium under full information, the limited information rational expectations equilibrium can be indeterminate. In a simple application of our framework to a monetary policy problem we show that limited information on part of the central bank implies indeterminate outcomes even when the Taylor Principle holds.

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 simple variance approaches, our stochastic volatility model improves the accuracy of uncertainty measures for survey forecasts.

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.

Incoming data in 2020 posed sizable challenges for the use of VARs in economic analysis: Enormous movements in a number of series have had strong effects on parameters and forecasts constructed with standard VAR methods. We propose the use of VAR models with time-varying volatility that include a treatment of the COVID extremes as outlier observations. Typical VARs with time-varying volatility assume changes in uncertainty to be highly persistent. Instead, we adopt an outlier-adjusted stochastic volatility (SV) model for VAR residuals that combines transitory and persistent changes in volatility. In addition, we consider the treatment of outliers as missing data. Evaluating forecast performance over the last few decades in quasi-real time, we find that the outlier-augmented SV scheme does at least as well as a conventional SV model, while both outperform standard homoskedastic VARs. Point forecasts made in 2020 from heteroskedastic VARs are much less sensitive to outliers in the data, and the outlier-adjusted SV model generates more reasonable gauges of forecast uncertainty than a standard SV model. At least pre-COVID, a close alternative to the outlier-adjusted model is an SV model with t-distributed shocks. Treating outliers as missing data also generates better-behaved forecasts than the conventional SV model. However, since uncertainty about the incidence of outliers is ignored in that approach, it leads to strikingly tight predictive densities.

We measure the effects of the COVID-19 outbreak on macroeconomic and financial uncertainty, and we assess the consequences of the latter for key economic variables. We use a large, heteroskedastic vector autoregression (VAR) in which the error volatilities share two common factors, interpreted as macro and financial uncertainty, in addition to idiosyncratic components. Macro and financial uncertainty are allowed to contemporaneously affect the macroeconomy and financial conditions, with changes in the common component of the volatilities providing contemporaneous identifying information on uncertainty. We also consider an extended version of the model, based on a latent state approach to accommodating outliers in volatility, to reduce the influence of extreme observations from the COVID period. The estimates we obtain yield very large increases in macroeconomic and financial uncertainty over the course of the COVID-19 period. These increases have contributed to the downturn in economic and financial conditions, but with both models, the contributions of uncertainty are small compared to the overall movements in many macroeconomic and financial indicators. That implies that the downturn is driven more by other dimensions of the COVID crisis than shocks to aggregate uncertainty (as measured by our method).

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.

Firmly-anchored inflation expectations are widely viewed as playing a central role in the successful conduct of monetary policy. This paper presents estimates of trend inflation, based on information contained in survey expectations, the term structure of interest rates, and realized inflation rates. My application combines a variety of data sources at the monthly frequency and it can flexibly handle missing data arising from infrequent observations and limited data availability. In order to assess whether inflation expectations are anchored, uncertainty surrounding future changes in trend inflation--measured by a time-varying volatility of trend shocks--is estimated as well. Not surprisingly, the estimates suggest that trend inflation in the U.S. rose and fell again over the 1970s and 1980s, accompanied by increases in uncertainty. Considering the recent crisis, full-sample estimates of trend inflation fell quite a bit, but not too dramatically. In contrast, real-time estimates recorded sizeable increases of trend uncertainty during the crisis of 2007/2008, which have abated since then.

Zusammenfassung Die praoperative Anamnese und korperliche Untersuchung sind anerkannter Standard bei der Risikoevaluation von Patienten vor elektiven chirurgischen Eingriffen. Ob und unter welchen Umstanden technische Voruntersuchungen dazu beitragen konnen, das perioperative Risiko zu reduzieren, ist bislang haufig nur unzureichend untersucht. Auch besteht unter Anasthesisten, Chirurgen und Internisten vielfach erhebliche Unsicherheit im perioperativen Umgang mit der Dauermedikation. Die deutschen wissenschaftlichen Fachgesellschaften fur Anasthesiologie und Intensivmedizin (DGAI), Chirurgie (DGCH) und Innere Medizin (DGIM) haben daher eine gemeinsame Empfehlung zur praoperativen Evaluation erwachsener Patienten vor elektiven, nichtkardiochirurgischen und nichtlungenresezierenden Eingriffen erarbeitet. Zunachst werden die allgemeinen Prinzipien der praoperativen Evaluation dargestellt (Teil A). Das Vorgehen bei Patienten mit bekannten oder vermuteten kardiovaskularen Vorerkrankungen wird gesondert betrachtet (Teil B: „Erweiterte kardiale Diagnostik“). Abschliesend wird der perioperative Umgang mit der Dauermedikation diskutiert (Teil C). Die vorgestellten Konzepte stellen fachubergreifende Empfehlungen dar, die ein strukturiertes und gemeinsames Vorgehen ermoglichen sollen. Ihr Ziel ist es, durch transparente und verbindliche Absprachen eine hohe Patientenorientierung unter Vermeidung unnotiger Voruntersuchungen zu gewahrleisten, praoperative Untersuchungsablaufe zu verkurzen sowie letztlich Kosten zu reduzieren. Die gemeinsamen Empfehlungen von DGAI, DGCH und DGIM spiegeln den gegenwartigen Kenntnisstand, aber auch die Meinungen von Experten wider, da nicht fur jede Fragestellung wissenschaftliche Evidenz besteht. Daher werden eine regelmasige Uberprufung und Aktualisierung der Empfehlungen erfolgen, sobald gesicherte neue Erkenntnisse vorliegen.

No, not really, since spectral estimators suffer from small sample and misspecification biases just as VARs do. Spectral estimators are no panacea for implementing long-run restrictions. In addition, when combining VAR coefficients with non-parametric estimates of the spectral density, care needs to be taken to consistently account for information embedded in the non-parametric estimates about serial correlation in VAR residuals. This paper uses a spectral factorization to ensure a correct representation of the data's variance. But this cannot overcome the fundamental problems of estimating the long-run dynamics of macroeconomic data in samples of typical length.

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.

There is widespread evidence of excess return predictability in financial markets. For the foreign exchange market a number of studies have documented that the predictability of excess returns is closely related to the predictability of expectational errors of excess returns. In this paper we investigate the link between the predictability of excess returns and expectational errors in a much broader set of financial markets, using data on survey expectations of market participants in the stock market, the foreign exchange market, the bond market and money markets in various countries. The results are striking. First, in markets where there is significant excess return predictability, expectational errors of excess returns are predictable as well, with the same sign and often even with similar magnitude. This is the case for foreign exchange, stock and bond markets. Second, in the only market where excess returns are generally not predictable, the money market, expectational errors are not predictable either. These findings suggest that an explanation for the predictability of excess returns must be closely linked to an explanation for the predictability of expectational errors.

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.

Beaudry and Portier (2006) propose an identification scheme to study the effects of news shocks about future productivity in vector error correction models (VECMs). This comment shows that, when applied to their VECMs with more than two variables, the identification scheme does not have a unique solution. The problem arises from a particular interplay of cointegration assumptions and longrun restrictions. (JEL E32, E44, G12, G14) In a highly influential paper, Beaudry and Portier (2006) estimate vector error correction models (VECMs) on US data and find that shocks generating a stock market boom but no contemporaneous movement in total factor productivity (TFP)—henceforth called TFP news—are closely related to shocks driving long-run variations in TFP. Moreover, these TFP news shocks lead to increases in consumption, investment, output, and hours on impact and constitute an important source of business-cycle fluctuations. These results run counter to basic dynamic stochastic general equilibrium models and have sparked a new literature attempting to generate news-driven positive comovement among macroeconomic aggregates. 1 This comment shows that, in the VECMs with more than two variables estimated by Beaudry and Portier (2006), their identification scheme fails to determine TFP news. Yet these higher-dimension systems are crucial to quantify the business-cycle effects of TFP news. 2 The identification problem arises from the interplay of two assumptions. First, the Beaudry-Portier identification scheme, called BP restrictions from here on, requires that one of the nonnews shocks has no permanent impact on either TFP or consumption. Second, the VECMs estimated by Beaudry and Portier (2006) impose that TFP and consumption are cointegrated. This cointegration means that TFP and consumption have the same permanent component, which makes one

Illustrated in a New Keynesian economy with time-varying output targets of the policymaker, belief management improves outcomes compared to symmetric information. At the margin, the policymaker tries to be intransparent about policy objectives by engineering disturbances which lower public beliefs about the persistence of output targets.

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.

There is widespread evidence of excess return predictability in financial markets. In this paper we examine whether this predictability is related to expectational errors. To consider this issue, we use data on survey expectations of market participants in the stock market, the foreign exchange market, and the bond and money markets in various countries. We find that the predictability of expectational errors coincides with the predictability of excess returns: when a variable predicts expectational errors in a given market, it typically predicts the excess return as well. Understanding expectational errors appears crucial for explaining excess return predictability.

Stylized facts on output and interest rates in the U.S. have so far proved hard to match with business cycle models. But these ¯ndings do not acknowledge that the economy might well be driven by di®erent shocks, and by each in di®erent ways. I estimate covariances of output, nominal and real interest rate conditional on three types of shocks: Technology, monetary policy and sources of in°ation persistence. Conditional and technology and monetary policy, the results square with standard models. However these two shocks explain only about 50% of persistent movements in in°ation which are key for understanding the overall comovements. The puzzle lies in modeling the shocks and transmission channels behind in°ation persistence, not in standard transmission channels for technology and monetary policy errors.

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.