Professional Forecasters Research Articles

Do monetary condition news at the zero lower bound influence households’ expectations and readiness to spend?

We investigate how households update their interest rate and inflation expectations upon hearing monetary condition news, and to what extent this changes their readiness to spend. Using data from the Michigan Survey of Consumers from December 2008 to December 2015, we find (1) the likelihood of higher expected interest rates significantly increases upon hearing news of tighter monetary condition; (2) monetary condition news is irrelevant for both short- and medium-run inflation expectations; (3) the main information content of this perceived news is most likely from forward guidance and professional forecasts; and (4) households update their readiness to spend on houses, cars and durable goods upon perceiving monetary condition news. Our evidence points towards interest rate expectations as the most likely mediator through which monetary condition news drives households’ readiness to spend.

Asset prices in a labor search model with confidence shocks

This paper studies a labor search model with agents who are averse to ambiguity (Knightian uncertainty). Shocks to confidence about future productivity are modeled as changes in ambiguity. Using the Survey of Professional Forecasters data, I find that confidence shocks help explain the equity premium and the stock market volatility, including their term structure. Ambiguity amplifies the response of the economy to productivity shocks, helping the model produce more realistic dynamics of unemployment, vacancies, labor market tightness, stock prices, and returns. Returns in the model are predictable with price-dividend ratios; both returns and dividends are predictable with unemployment, like in the data. Two extensions consider shocks to bargaining power and to separation rate and find similar implications of ambiguity about these shocks.

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.

Cross-country uncertainty spillovers: Evidence from international survey data

Using a large international survey of professional forecasters, we construct measures of economic uncertainty surrounding output growth, inflation, the interest rate, exchange rate and current account. We then analyze uncertainty spillovers across major advanced and emerging economies using large multi-country Bayesian Panel VARs. We consider how our results change if our uncertainty measures reflect: disagreement among forecasters (idiosyncratic uncertainty); the variance of their mean forecast errors (common uncertainty); or both types of uncertainty. We show that the US is an important but not dominant source of uncertainty, affecting other economies through interest rate and exchange rate uncertainty. This reflects the major role played by US monetary policy and the dollar in the global financial system. Crucially, though, the Eurozone followed by the UK and China are also important sources of uncertainty. We also find that, on average, foreign interest rate and exchange rate uncertainty are more important than foreign output growth uncertainty. While spillovers in idiosyncratic uncertainty are more frequently observed, failing to account for common uncertainty can lead us to overestimate the role played by smaller economies.

PROBLEMS OF TRANSPORT AND LOGISTICS COMPANIES AT THE PRESENT STAGE OF ECONOMIC DEVELOPMENT

This article discusses the development of the transport and logistics policy of the state and the development of logistics in general. Despite the fact that the state is making every effort to develop this area, there are a lot of unresolved problems. Kazakhstan is a transit state, but you should not limit yourself to this direction and consider not only mega projects, but also «real» investments. The economy of any state depends on the «circulatory system», which is transport logistics. Drastic changes in supply chain management are needed, specialists and personnel are needed, it is necessary to increase the level of development of transport science and education. In the state transport programs, it is necessary to create conditions for the development of export-import transportation, reducing transport costs, increasing efficiency and improving the quality of intra-republican logistics.
 In the article, the authors share their vision of improving the indicators of the LPI logistics efficiency index in Kazakhstan, which is an integrated indicator of the quality and efficiency of the transport and logistics complex (TLC) in a particular country. In 2018, according to this assessment system, Kazakhstan ranked 71st in the world. First of all, it is proposed to improve and structure approaches to the development of transport infrastructure. It is recommended to make any decisions based on a scientific approach, professional forecast, careful analysis, avoiding a situation where administrative decisions are made first, and then feasibility studies are «adjusted» to them. Currently, various forms of attracting and using capital from outside are used for the reconstruction of the transport and communication complex, since for effective integration into the world space, Kazakhstan first of all needs to create conditions that provide access to the sea and ocean coast.

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.

Out of Bounds: Do SPF Respondents Have Anchored Inflation Expectations?

AbstractSince the Federal Open Market Committee announced a 2% inflation target in 2012, the median long‐run inflation forecast of professional forecasters has been near target. We show, however, that most individual forecasters' long‐run inflation expectations fluctuate substantially, with sizeable departures from target. We propose a new “bounds anchoring” indicator based on deviations of individuals' long‐run forecasts from target. This indicator sheds new light on gradual changes in expectations since the 2012 announcement. We find that bounds anchoring gradually strengthened in the years following the target announcement, but that this trend has recently started to reverse.

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.

Reaction of the Philippine Stock Market to Domestic Monetary Policy Surprises: An Event Study Approach

This paper uses an event study analysis to assess how stock prices in the Philippines have reacted to domestic monetary-policy changes using data at a daily frequency from 2017 to 2022. A major contribution of this paper is the construction of a monetary-policy surprise measure for the Philippines, as the difference between the actual change in the monetary policy rate and the change anticipated by professional forecasters. My results are consistent with the literature, suggesting that unanticipated monetary policy changes exert a significant influence on stock prices in the Philippines. Overall, I find that an unexpected increase of 25 basis points in the monetary policy rate increases stock prices by about 1.09% on average. These results are robust to the inclusion of additional control variables in the baseline regression model, such as the implementation of restrictions to economic activity to curb the spread of the COVID-19 outbreak or revisions to macroeconomic forecasts released concomitantly with the monetary-policy rate announcement.

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, elicited predictions of key economic indicators such as unemployment, inflation, and economic growth. Here, we test the accuracy of those forecasts (n = 12,359) 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 28% of the time. By contrast, forecasts show little evidence of optimistic bias. These results have important implications for how policy makers ought to interpret forecasts. Moreover, we employ novel methodology in analyzing archival data: we split our dataset into exploration and validation halves. The current manuscript presents results from the exploration half. The final manuscript will include results from the hold-out sample, as guided by editorial input.

News-Driven Uncertainty Fluctuations

We investigate the channels through which news influences the subjective beliefs of economic agents, with a particular focus on their subjective uncertainty. The main insight of the article is that news that is more at odds with agents’ prior beliefs generates an increase in uncertainty; news that is more consistent with their prior beliefs generates a decrease in uncertainty. We illustrate this insight theoretically and then estimate the model empirically using data on U.S. output and professional forecasts to provide novel measures of news shocks and uncertainty. We then estimate impulse responses from the identified shocks to show that news shocks can affect macroeconomic variables in ways that resemble the effects of uncertainty shocks. Our results suggest that controlling for news can potentially diminish the estimated effects of uncertainty shocks on real variables, particularly at longer horizons.

Belief Distortions and Macroeconomic Fluctuations

This paper combines a data-rich environment with a machine learning algorithm to provide new estimates of time-varying systematic expectational errors (“belief distortions”) embedded in survey responses. We find sizable distortions even for professional forecasters, with all respondent-types overweighting the implicit judgmental component of their forecasts relative to what can be learned from publicly available information. Forecasts of inflation and GDP growth oscillate between optimism and pessimism by large margins, with belief distortions evolving dynamically in response to cyclical shocks. The results suggest that artificial intelligence algorithms can be productively deployed to correct errors in human judgment and improve predictive accuracy. (JEL C45, D83, E23, E27, E31, E32, E37)

Economic policy uncertainty and government spending multipliers

We examine the impact of Economic Policy Uncertainty (EPU) on the effectiveness of government spending using a local projection method with an endogenously determined threshold parameter. The empirical analysis reveals that government spending multipliers are larger during low EPU periods compared to those in high EPU periods. This result is robust when government spending shocks are identified using government spending news constructed based on survey of professional forecasters data. Our study calls for policy expectation management as a company effort together with fiscal stimulus.

Forecast disagreement about long-run macroeconomic relationships

Using survey forecast data, this paper studies whether professional forecasters utilize long-run cointegration relationships among macroeconomic variables to forecast the future, as postulated in stochastic growth models. Significant heterogeneity exists among forecasters. The majority of the forecasters do not use these long-run relationships. The results are robust across different groups, to addressing the multiple testing problem and to allowing for structural break.

Government spending news and surprise shocks: It’s the timing and persistence

We identify government spending news and surprise shocks based on the Survey of Professional Forecasters (SPF) data. After clarifying the nature of the two news measures used in Ramey (2011b), we show that a measure of the news shock for the recent sample periods is still needed. Moreover, we find that news and surprise shocks may differ not only in timing but also in the level of persistence of government spending responses. Given these findings, we construct a present discounted value measure that properly captures government spending news shock of high persistence. The effects of the news shock strikingly differ from those of the surprise shock. A news shock has a significant expansionary effect on GDP, hours worked, real wage, consumption, and investment, whereas a surprise shock has a contractionary effect. These results are in contrast to previous findings (Ramey, 2011b) and inconsistent with the neoclassical view emphasizing the negative wealth effect.

Robust inference under time‐varying volatility: A real‐time evaluation of professional forecasters

SummaryIn many forecast evaluation applications, standard tests as well as tests allowing for time‐variation in relative forecast ability build on heteroskedasticity‐and‐autocorrelation consistent (HAC) covariance estimators. Yet, the finite‐sample performance of these asymptotics is often poor. “Fixed‐ ” asymptotics, used to account for long‐run variance estimation, improve finite‐sample performance under homoskedasticity, but lose asymptotic pivotality under time‐varying volatility. Moreover, loss of pivotality due to time‐varying volatility is found in the standard HAC framework in certain cases as well. We prove a wild bootstrap implementation to restore asymptotically pivotal inference for the above and new CUSUM‐ and Cramér‐von Mises‐based tests in a fairly general setup, allowing for estimation uncertainty from either a rolling window or a recursive approach when fixed‐ asymptotics are adopted to achieve good finite‐sample performance. We then investigate the (time‐varying) performance of professional forecasters relative to naive no‐change and model‐based predictions in real‐time. We exploit the Survey of Professional Forecasters (SPF) database and analyze nowcasts and forecasts at different horizons for output and inflation. We find that not accounting for time‐varying volatility seriously affects outcomes of tests for equal forecast ability: wild bootstrap inference typically yields convincing evidence for advantages of the SPF, while tests using non‐robust critical values provide remarkably less. Moreover, we find significant evidence for time‐variation of relative forecast ability, the advantages of the SPF weakening considerably after the “Great Moderation.”

Information frictions across various types of inflation expectations

Understanding how the degree of information frictions varies among economic agents is of utmost importance for macroeconomic dynamics. We document and compare the frequency of forecast revisions and cross-sectional disagreement in inflation expectations among five categories of agents: households, firms, professional forecasters, policymakers and participants to laboratory experiments. First, we provide evidence of a heterogeneous frequency of forecast revisions across categories of agents, with policymakers revising more frequently their forecasts than firms and professional forecasters. Households revise less frequently. Second, all categories exhibit cross-sectional disagreement. There is however a strong heterogeneity: while policymakers and professional forecasters exhibit low disagreement, firms and households show strong disagreement. Our analysis suggests that the nature of information frictions is closer to noisy information model features. We also explore the external validity of experimental expectations.

Are internally consistent forecasts rational?

AbstractEconomic forecasts, such as the Survey of Professional Forecasters (SPF), are revised multiple times before realization of the target. This paper studies the sources of forecast revisions. By decomposing the fixed‐event forecast error into a rational component due to unanticipated future shocks and an irrational component due to measurement error in acquired information or forecasters' reactions, we derive the conditions under which fixed‐event forecasts that contain an irrational forecast error can still possess the second moment properties of rational forecasts. We show that internal consistency of fixed‐event forecasts depends on the magnitude of the rational and irrational components in the revisions. As such, fixed‐event forecasts subject to irrational error may still be internally consistent, although they are not rational, as evidence in many empirical studies. We illustrate our methodology with the SPF inflation forecasts data. Our results show evidence of a sizeable and heterogeneous irrational forecast error component across forecast horizons and a high irrational‐to‐news ratio for most forecasters. This finding also provides insight into why forecast revision effort is not always fully compensated by revision reward.