Survey Of Professional Forecasters Research Articles

Disciplining growth‐at‐risk models with survey of professional forecasters and Bayesian quantile regression

AbstractThis study presents a novel and fully probabilistic approach for combining model‐based forecasts with surveys or other judgmental forecasts. In our method, survey forecasts are integrated as penalty terms for the model parameters, facilitating a probabilistic exploration of additional insights obtained from surveys. We apply this approach to estimate a growth‐at‐risk model for real GDP growth in the United States. The results reveal that this additional shrinkage significantly improves prediction performance, with the information from surveys even exerting an influence on the lower tails of the distribution.

Expectation-driven boom-bust cycles

Using data from the Survey of Professional Forecasters, we observe that a large fraction of analysts’ expectations about future economic growth is not due to technology or other shocks to fundamentals measured by the business cycle literature. We find that these unexplained changes in forecast revisions predict significant boom-bust dynamics in the key macroeconomic aggregates. We offer a novel theory where boom-bust dynamics stem from expectation shocks orthogonal to fundamentals.

Overprecision in the Survey of Professional Forecasters

Every decision depends on a forecast of its consequences. We examine the calibration of the single longest and most complete forecasting project. The Survey of Professional Forecasters has, since 1968, collected predictions of key economic indicators such as unemployment, inflation, and economic growth. Here, we test the accuracy of those forecasts (n = 16,559) and measure the degree to which they fall victim to overconfidence, both overoptimism and overprecision. We find forecasts are overly precise; forecasters report 53% confidence in the accuracy of their forecasts, but are correct only 23% of the time. By contrast, forecasts show little evidence of optimistic bias. These results have important implications for how organizations ought to make use of forecasts. Moreover, we employ novel methodology in analyzing archival data: we split our dataset into exploration and validation halves. We submitted results from the exploration half to Collabra:Psychology. Following editorial input, we updated our analysis plan for the validation dataset, preregistering only analyses that were consistent across different economic indicators and analytic specifications. This manuscript presents results from the full dataset, prioritizing results that were consistent in both halves of the data.

Using deep (machine) learning to forecast US inflation in the COVID‐19 era

AbstractThe 2021–2022 surge in US inflation was unanticipated by the Survey of Professional Forecasters (SPF) and other macroeconomists and institutions. This study assesses whether nascent deep learning frameworks and methods more accurately project recent core personal consumption expenditures inflation. We create a recurrent neural network (RNN) to forecast long‐term inflation, and after training on 60 years of quarterly data, the model outperforms the SPF and projects a spike in inflation similar to that of recent years. We compare the model's performance with and without COVID‐19–specific data and discuss some implications of our findings for economic forecasting in global crises.

A structural approach to combining external and DSGE model forecasts

This note shows that combining external forecasts such as the Survey of Professional Forecasters can significantly increase DSGE forecast accuracy while preserving the interpretability in terms of structural shocks. Applied to pseudo real-time from 1997q2 onward, the canonical Smets and Wouters (2007) model has significantly smaller forecast errors when giving a high weight to the SPF forecasts. Incorporating the SPF forecast gives a larger role to risk premium shocks during the global financial crisis. A model with financial frictions favors a larger weight on the DSGE model forecast.

Conditional macroeconomic survey forecasts: Revisions and errors

Using data from the European Central Bank's Survey of Professional Forecasters and ECB/Eurosystem staff projections, we analyze the role of ex-ante conditioning variables for macroeconomic forecasts. In particular, we test to which extent the updating and ex-post performance of predictions for inflation, real GDP growth and unemployment are related to beliefs about future oil prices, exchange rates, interest rates and wage growth. While oil price and exchange rate predictions are updated more frequently than macroeconomic forecasts, the opposite is true for interest rate and wage growth expectations. Beliefs about future inflation are closely associated with oil price expectations, whereas expected interest rates are related to predictions of output growth and unemployment. Exchange rate predictions also matter for macroeconomic forecasts, albeit less so than the other variables. With regard to forecast errors, wage growth and GDP growth closely comove, but only during the period when interest rates are at the effective zero lower bound.

Inflation and Valuation Practice: German Evidence

The objective of our paper is to analyze, how valuation practice deals with inflation especially for the terminal value, and how company value is influenced by assumptions set by practitioners. For that reason, we examine how vulnerable companies could be regarding struggles to pass on inflationary effects to their customers. We analyze the inflation rates assumed for the steady-state (terminal value) by comparing them to different estimators for the inflation rate expected at the valuation date (Survey of Professional Forecasters, inflation rates derived by comparing real and nominal rate of returns, inflation swaps). We quantify the implications of using different inflation rates for future cash flow development, terminal value and the company value at the valuation date, and compare nominal reported values with company values in a (hypothetical) world without inflation. Our sample consists of 263 valuation reports written by German auditors with valuation dates between 2000 to 2021. Most of the reports aim at determining the price per share to compensate minority shareholders during a squeeze-out. Our results question inter alia the preference for a constant company specific inflation rate of around 1% on average, and we quantify a number of value effects.

Robust forecast superiority testing with an application to assessing pools of expert forecasters

SummaryWe develop forecast superiority tests that are robust to the choice of loss function by following Jin, Corradi and Swanson (JCS: 2017), and relying on a mapping between generic loss forecast evaluation and stochastic dominance principles. However, unlike JCS tests, which are not uniformly valid and are correctly sized only under the least favorable case, our tests are uniformly asymptotically valid and non‐conservative. To show this, we establish uniform convergence of HAC variance estimators. Monte Carlo experiments indicate good finite sample performance of our tests, and an empirical illustration suggests that prior forecast accuracy matters in the Survey of Professional Forecasters.

The role of stickiness, extrapolation and past consensus forecasts in macroeconomic expectations

We estimate a simple reduced-form model of expectation formation with three distinct deviations from full-information rational expectations that combines underreaction with overreaction to new information. In particular, forecasts are sticky, extrapolate the most recent news about the current period, and depend on the lagged consensus forecast about the period being forecast. We find that all three biases are present in the Survey of Professional Forecasters as well as in the Livingston Survey, and that their magnitudes depend on the forecasting horizon. We also stress the point that using the past consensus forecast to form expectations is a reasonable thing to do if a forecaster is not able to come up with full-information rational expectations all by herself. Finally, we show that forecasters that have more sticky expectations generally tend to rely less on the lagged consensus forecast but extrapolate news about the current period more.

How to deal with missing observations in surveys of professional forecasters

ABSTRACT Survey forecasts are prone to entry and exit of forecasters as well as forecasters not contributing every period leading to gaps. These gaps make it difficult to compare individual forecasters to each other and raises the question of how to deal with the missing observations. This is addressed for the variables GDP, CPI inflation, and unemployment for the US. The theoretically optimal method of filling in missing observations is derived and compared to several competing methods. It is found that not filling in missing observations and taking the previous value do not perform particularly well. For the other methods assessed, there is no clear superior approach for all use cases, but the theoretically optimal one usually performs quite well.

A real-time assessment of inflation nowcasting at the Cleveland Fed

Using a model based on staff research, the Cleveland Fed's website provides daily nowcasts—or near-term predictions—of multiple US inflation measures for public use. In this Commentary, we compare the historical predictive accuracy of the model behind those inflation nowcasts with the accuracy of inflation nowcasts coming from competing sources: surveys of professional forecasters and alternative statistical models. We find that our inflation nowcasts have performed relatively well in these comparisons, both over a long sample and a short sample that focuses on the period since the start of the COVID-19 pandemic.

An Alternative Explanation for the “Fed Information Effect”

Regressions of private-sector macroeconomic forecast revisions on monetary policy surprises often produce coefficients with signs opposite to standard macroeconomic models. The “Fed information effect” argues these puzzling results are due to monetary policy surprises revealing Fed private information. We show they are also consistent with a “Fed response to news” channel, where both the Fed and professional forecasters respond to incoming economic news. We present new evidence challenging the Fed information effect and supporting the Fed response to news channel, including: regressions that control for economic news, our own survey of professional forecasters, and financial market responses to FOMC announcements. (JEL D82, E23, E27, E43, E44, E52, E58)

How to Deal with Missing Observations in Surveys of Professional Forecasters

How to Deal with Missing Observations in Surveys of Professional Forecasters

Does the Survey of Professional Forecasters Help Predict the Shape of Recessions in Real Time?

Does the Survey of Professional Forecasters Help Predict the Shape of Recessions in Real Time?

Monetary policy and the term structure of inflation expectations with information frictions

I estimate how the term structure of inflation expectations in the United States responds to monetary policy shocks from 1982 to 2019. Studying the entire term structure uses all available data and allows me to examine whether forecasts at different horizons respond differently to the same shock, which may influence bond pricing, investment decisions, and the term structures of real and nominal interest rates. Survey forecasts of inflation (taken from the Survey of Professional Forecasters) are available at many horizons, but combining these in a consistent way is challenging because of information frictions and the number of available forecasts. I extend the unobserved components model to include additional covariates and allow for information frictions, a combination of features that allows me to identify the effects of monetary policy shocks, which I do using an instrumental variable. I find that a contractionary monetary policy shock twists the term structure of inflation expectations, raising short-run expectations while lowering long-run expectations, which suggests that long-run expectations are not well anchored. I also show that monetary policy actions taken throughout the recession beginning in 2008 propped up long-run inflation expectations from 2009 to 2014.

Learning, disagreement and inflation forecasting

This paper studies inflation forecasting based on the Bayesian learning algorithm which simultaneously learns about parameters and state variables. The Bayesian learning method updates posterior beliefs with accumulating information from inflation and disagreement about expected inflation from the Survey of Professional Forecasters (SPF). The empirical results show that Bayesian learning helps refine inflation forecasts at all horizons over time. Incorporating a Student’s t innovation improves the accuracy of long-term inflation forecasts. Including disagreement has an effect on refining short-term inflation density forecasts. Furthermore, there is strong evidence supporting a positive correlation between disagreement and trend inflation uncertainty. Our findings are helpful for policymakers when they forecast the future and make forward-looking decisions.

Economic forecasting in a pandemic: some evidence from Singapore.

This paper aims to investigate whether the predictive performance and behaviour of professional forecasters are different during the COVID-19 pandemic as compared with the global financial crisis of 2008 and normal times. To this end, we use a survey of professional forecasters in Singapore collated by the central bank to analyse the forecasting records for GDP growth and CPI inflation for the period 2000Q1–2021Q4. We first examine the point forecasts to document the extent of forecast failure during the two crises and explore various explanations for it, such as leader-following and herding behaviour. Then, using percentile-based summary measures of probability distribution forecasts, we study how the degree of consensus and extent of subjective uncertainty among forecasters were affected by crisis conditions. A trend break is observed in the subjective uncertainty associated with growth projections after the onset of the COVID-19 crisis. In contrast, both subjective uncertainty and the degree of consensus in inflation projections were essentially unchanged in crises, suggesting that the short-term inflation expectations of forecasters were strongly anchored.

Disagreement in Consumer Inflation Expectations

AbstractBy carefully matching the data sets from the Michigan Survey of Consumers with the Survey of Professional Forecasters, we show that there exists substantial heterogeneity in the propensity of U.S. households to learn from experts in forming inflation expectations. Additional results for a group of European economies broadly confirm this observation. We advance an extended version of the sticky‐information model to analyze disagreement in consumer inflation expectations. Besides differences in consumers' propensities to learn, disagreement in our model arises from heterogeneity in consumers' fundamental inflation and past expectations and experts' different views about future inflation.

Supply shocks, demand shocks and yield curve dynamics

This paper presents a framework for extracting the supply and demand shocks using the revisions of expectations obtained from a Survey of Professional Forecasters. The main contribution of this paper is the identification of these shocks and the subsequent assessment of their impact on yield curve dynamics. This allows for the asymmetry in the bond yield movements, where we show that yields react differently to supply and demand shocks. The analysis is performed on the US data ranging from 1969 (q4) to 2022 (q1).

Disagreement in economic forecasts and equity returns: risk or mispricing?

PurposeThis paper investigates the role of economic disagreement in the cross-sectional pricing of individual stocks. Economic disagreement is quantified with ex ante measures of cross-sectional dispersion in economic forecasts from the Survey of Professional Forecasters (SPF), determining the degree of disagreement among professional forecasters over changes in economic fundamentals.Design/methodology/approachThe authors introduce a broad index of economic disagreement based on the innovations in the cross-sectional dispersion of economic forecasts for output, inflation and unemployment so that the index is a shock measure that captures different aspects of disagreement over economic fundamentals and also reflects unexpected news or surprise about the state of the aggregate economy. After building the broad index of economic disagreement, the authors test out-of-sample performance of the index in predicting the cross-sectional variation in future stock returns.FindingsUnivariate portfolio analyses indicate that decile portfolios that are long in stocks with the lowest disagreement beta and short in stocks with the highest disagreement beta yield a risk-adjusted annual return of 7.2%. The results remain robust after controlling for well-known pricing effects. The results are consistent with a preference-based explanation that ambiguity-averse investors demand extra compensation to hold stocks with high disagreement risk and the investors are willing to pay high prices for stocks with large hedging benefits. The results also support the mispricing hypothesis that the high disagreement beta provides an indirect way to measure dispersed opinion and overpricing.Originality/valueMost literature measures disagreement about individual stocks with the standard deviation of earnings forecasts made by financial analysts and examines the cross-sectional relation between this measure and individual stock returns. Unlike prior studies, the authors focus on disagreement about the economy instead of disagreement about earnings growth. The authors' argument is that disagreement about the economy is a major factor that would explain disagreement about stock fundamentals. The authors find that disagreement in economic forecasts does indeed have a significant impact on the cross-sectional pricing of individual stocks.