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Despite extensive research on the value of collaborative forecasting among supply chain partners, there has been limited attention to the actual mechanisms of forecast sharing. We propose using cost-function-based prediction markets as means of sharing demand forecasts. We consider a decentralized two-stage supply chain with a supplier who sells to a retailer, in which both partners receive demand forecast updates. In the first stage, the supplier and the retailer agree on a collaborative forecasting scheme to form a consensus forecast. We consider two schemes: collaborative forecasting using cost-function-based prediction markets in which partners pay a set-up cost to operate a prediction market, and direct collaborative forecasting in which partners directly share their forecasts with each other and resolve their forecast differences through costly actions. In the second stage, we analyze the game between the supplier and the retailer to determine the retailer’s optimal order quantity as well as the supplier’s optimal wholesale price. We show that demand forecast sharing using cost-function-based prediction markets results in a Pareto improvement of the partners’ profit over the alternative of directly sharing forecasts. Furthermore, we show that this Pareto improvement is monotone with respect to supply chain partners’ forecast accuracy. We conduct an extensive numerical study to provide further insights into the effect of the return on forecast sharing investment, forecast accuracy, and level of demand uncertainty on the benefits gained by implementing cost-function-based prediction markets.

Chapter 10 discusses the rational investor, behavioral finance, neurofinance, consensus opinions, and their impacts on stock and other markets. The chapter begins with a discussion of rational investor paradigms, describing the St. Petersburg Paradox and von Neuman-Morgenstern axioms of choice. Essential findings of Prospect Theory are described, including the framing and anchoring problems, myopia, overreaction, overconfidence, and other fundamental problems in pursuing sound rational solutions to investing, and trading problems. Connections between all of these barriers to rational decision-making to finance are explored. Psychological and physiological underpinnings to such problems are offered. Relationships between irrational investors and markets, either efficient or inefficient are discussed. In this discussion, game shows, betting markets, analyst estimates, and consensus forecasts are offered as evidence that cognitively impaired investors can comprise rational markets, while information cascades and herding can lead to irrational markets.

We analyze an unbalanced panel monthly predictions of nonfarm payroll (NFP) changes between January 2008 and December 2020 sourced from Bloomberg. Unsurprisingly, we find that prediction quality varies across economists and we reject the hypothesis of equal predictive ability. In an error decomposition, we find evidence of significantly biased forecasts. Participation rate in the survey is affecting this bias. We find that survey participants under-predict job losses in times of market turmoil while also under-predicting the recovery thereafter, especially during the COVID19 labor shock. For prediction of NFP changes, autoregressive models are outperformed by a deep learning long short-term memory network. However, the consensus forecast yields better forecasts than model-based approaches and are further improved by combining the forecasts of the best performing economists. The COVID19 labor shock is shown to have adverse effects on the prediction performance of economists. However, not all economists are affected equally.

This paper examines whether analysts affected by lunar eclipses issue more pessimistic earnings forecasts. After controlling for the characteristics of analysts, climates, and lunar phases, we find that analyst forecasts made after lunar eclipses are more pessimistic than consensus forecasts. This effect is more pronounced when a lunar eclipse is stronger. Further, we find that nighttime cloud coverage reduces analyst pessimism, while network attention, analyst superstition, natural disasters, and pollution increases pessimism after eclipses. Our paper provides new evidence that lunar eclipse-induced mood leads to pessimistic forecasts made by sell-side analysts.

Professional forecasters can rely on econometric models, on their personal expertise or on both. To accommodate for adjustments to model forecasts, this paper proposes to use two stage least squares (TSLS) (and not ordinary least squares [OLS]) for the familiar Mincer–Zarnowitz regression when examining bias in professional forecasts, where the instrumental variable is the consensus forecast. An illustration for 15 professional forecasters with their quotes for real gross domestic product (GDP) growth, inflation and unemployment for the United States documents the usefulness of this new estimation method. It also shows that TSLS suggests less bias than OLS does.

The COVID19 pandemic has created a massive shock, unexpectedly increasing mortality levels and generating economic recessions all around the world. In recent years, several efforts have been made to develop models that link the environment, population and the economy which may be used to estimate potential longer term effects of the pandemic. Unfortunately, many of the parameters used in these models lack appropriate empirical identification. In this study, first I estimate the parameters of “Wonderland”, a system dynamics model of the population-economy-environment nexus, and posteriorly, add external GDP and mortality shocks to the model. The estimated parameters are able to closely match world data, while future simulations point, on average and regardless of the COVID19 pandemic, to a world reaching dangerous environmental levels in the following decades, in line with consensus forecasts. On the other hand, the effects of the pandemic on the economy are highly uncertain and may last for several decades.

We construct risks around consensus forecasts of real GDP growth, unemployment, and inflation. We find that risks are time-varying, asymmetric, and partly predictable. Tight financial conditions forecast downside growth risk, upside unemployment risk, and increased uncertainty around the inflation forecast. Growth vulnerability arises as the conditional mean and conditional variance of GDP growth are negatively correlated: downside risks are driven by lower mean and higher variance when financial conditions tighten. Similarly, employment vulnerability arises as the conditional mean and conditional variance of unemployment are positively correlated, with tighter financial conditions corresponding to higher forecasted unemployment and higher variance around the consensus forecast.

Research suggests that earnings-disclosure-related litigation causes managers to reduce subsequent disclosure, perhaps stemming from a belief that even their good faith disclosures will cause them trouble. This paper considers unexplored dimensions of disclosure and alternative channels of disclosure to provide additional evidence that speaks to how litigation shapes managers’ disclosure strategies. Consistent with Skinner (1994)’s classic legal liability hypothesis, we find that, while managers reduce and delay forecasts of positive earnings news following litigation, they increase the frequency and timeliness of their bad news forecasts. Moreover, many managers who were nonguiders prior to facing legal scrutiny begin guiding following litigation. Managers also maintain (if not increase) the information they provide via press releases and during conference calls following litigation. Supporting the notion that managers use disclosure to walk down expectations, additional analyses document an increase in the likelihood that lawsuit firms report earnings that beat consensus forecasts in the post-lawsuit period. Collectively, our evidence suggests that following litigation managers continue to view disclosure as a valuable tool that shapes their firms’ information environments and reduces expected legal costs. In so doing, it supports an important alternative viewpoint of how firms respond to litigation as well as the effectiveness of litigation as a disciplining mechanism.

Rainfall event forecasting is prominently done using climate models (CMs) to produce multiple forecasts for the same rainfall event. The best forecast is complicated to find and hence has not yet been explored in the CMs. Recent advances in deep learning methods have provided an exceptional ability to investigate intricate weather patterns from big climate data. In this article, a hybrid climate learning model (HCLM) is proposed that utilizes both the CM and the deep learning models for improving the rainfall forecast. More specifically, a probabilistic multilayer perceptron (PMLP) network evaluates multiple forecasts from the CM-generated forecasts and selects the best one. The selected forecast is next passed onto a hybrid deep long short-term memory (HD-LSTM) network, which looks back and learns the relationship of the selected forecast with corresponding rainfall and temperature observations to produce the next-day rainfall forecast. The experimental results from various climate zones in Australia show that the HCLM outperforms existing state-of-the-art climate and deep learning models.

Do professional forecasters have an accurate sense of the uncertainties surrounding their own forecasts? This paper examines forecaster overconfidence by comparing ex ante, surveyed forecaster uncertainty with ex post, realised uncertainty based on the dispersion of an individual’s forecast errors. Unlike the literature that focuses on consensus forecasts, our focus is at the level of the individual forecaster. Using microdata from the three major surveys of professional forecasters (Euro Area, US and UK), we examine real GDP growth forecasts over the period 1999–2015. Our findings show that overconfidence dominates among individual forecasters, particularly for longer forecast horizons, and that individual forecasters appear to have little understanding of their own uncertainty.

The main results of the analysis of the Northern Hemisphere large-scale atmospheric circulation features are presented for the 2020 summer. Skill scores of the consensus forecast for the 2020 Northern Eurasia summer are discussed in the context of analyzing the large-scale atmospheric circulation. The prognostic potential of the trend component in forecasting seasonal anomalies of air temperature and precipitation is noted. Keywords: air temperature, precipitation, forecast skill, trends, large-scale atmospheric circulation, sea surface temperature, NEACOF, circulation indices, Arctic ice

We examine the properties of firms’ forecasting records and whether the accuracy of their prior earnings forecasts affects investor response to their subsequent forecasts. Within the context of a Bayesian model of investor learning, we find that the stock price response to management forecast news is increasing in prior forecast accuracy and also in the length of a firm’s forecasting record. Further, we document that investors are more responsive to extreme good and bad news forecasts when a firm has an established forecasting record. Overall, these results suggest that a firm’s prior forecasting behavior allows it to establish a forecasting reputation. JEL Descriptors G19, G39, D89, M40 * Corresponding Author: ahutton@bc.edu; 617 552-1951 phone; 617 552-6345 fax; Fulton Hall 520, 140 Commonwealth Ave. Chestnut Hill, MA 02467-3808 *We thank Ray Ball, Steve Baginski, Linda Bamber, Ken French, Charles Hsu, Michael Kimbrough, S.P. Kothari, Rafael La Porta, Franco Wong, Eric Yeung, Valentina Zamora, an anonymous referee, and workshop participants at Boston College, Boston University, Carnegie Mellon University, Emory University, INSEAD, University of Georgia, MIT Sloan School of Management, Tuck School of Business, George Washington University, FEA Conference at the University of North Carolina, Chapel Hill and the 2006 American Accounting Association annual meetings for helpful comments. Finally, we thank Robert Burnham for excellent research assistance.

In the study, we confirm that reporting entities increase discretionary revenue when their pre-managed operating income and net earnings are slightly lower than the consensus forecast by analysts for the fourth quarter. The novelty of this article is that introducing IFRS 15 significantly mitigates the increase of discretionary revenue when pre-managed operating income is slightly lower than the fourth quarter consensus on operating income. We do not find the implementation of IFRS 15 to have an impact on discretionary revenue if reporting entities are unable to meet the fourth quarter revenue and net earnings consensus by a small amount.

This paper introduces the concept of Temporal Hierarchies for time series forecasting. A temporal hierarchy can be constructed for any time series by means of non-overlapping temporal aggregation. Predictions constructed at all aggregation levels are combined with the proposed framework to result in temporally reconciled, accurate and robust forecasts. The implied combination mitigates modelling uncertainty, while the reconciled nature of the forecasts results in a unified prediction that supports aligned decisions at different planning horizons: from short-term operational up to long-term strategic planning. The proposed methodology is independent of forecasting models. It can embed high level managerial forecasts that incorporate complex and unstructured information with lower level statistical forecasts. Our results show that forecasting with temporal hierarchies increases accuracy over conventional forecasting, particularly under increased modelling uncertainty. We discuss organisational implications of the temporally reconciled forecasts using a case study of Accident & Emergency departments.

We evaluate the performances of various methods for forecasting tourism data. The data used include 366 monthly series, 427 quarterly series and 518 annual series, all supplied to us by either tourism bodies or academics who had used them in previous tourism forecasting studies. The forecasting methods implemented in the competition are univariate and multivariate time series approaches, and econometric models. This forecasting competition differs from previous competitions in several ways: (i) we concentrate on tourism data only; (ii) we include approaches with explanatory variables; (iii) we evaluate the forecast interval coverage as well as the point forecast accuracy; (iv) we observe the effect of temporal aggregation on the forecasting accuracy; and (v) we consider the mean absolute scaled error as an alternative forecasting accuracy measure. We find that pure time series approaches provide more accurate forecasts for tourism data than models with explanatory variables. For seasonal data we implement three fully automated pure time series algorithms that generate accurate point forecasts, and two of these also produce forecast coverage probabilities which are satisfactorily close to the nominal rates. For annual data we find that Naive forecasts are hard to beat.

We describe some fast algorithms for reconciling large collections of time series forecasts with aggregation constraints. The constraints arise due to the need for forecasts of collections of time series with hierarchical or grouped structures to add up in the same manner as the observed time series. We show that the least squares approach to reconciling hierarchical forecasts can be extended to more general non-hierarchical groups of time series, and that the computations can be handled efficiently by exploiting the structure of the associated design matrix. Our algorithms will reconcile hierarchical forecasts with hierarchies of unlimited size, making forecast reconciliation feasible in business applications involving very large numbers of time series.

We study the rationality of individual and consensus professional forecasts of macroeconomic and financial variables using the methodology of Coibion and Gorodnichenko (2015), which examines predictability of forecast errors from forecast revisions. We report two key findings: forecasters typically over-react to their individual news, while consensus forecasts under-react to average forecaster news. To reconcile these findings, we combine the diagnostic expectations model of belief formation from Bordalo, Gennaioli, and Shleifer (2018) with Woodford’s (2003) noisy information model of belief dispersion. The forward looking nature of diagnostic expectations yields additional implications, which we also test and confirm. A structural estimation exercise indicates that our model captures important variation in the data, yielding a value for the belief distortion parameter similar to estimates obtained in other settings

Многофакторный системный кризис в результате пандемии коронавируса COVID-19 обуславливает наступление критических изменений в целом ряде отраслей мировой экономики. Автор проводит анализ мнений ведущих отечественных и зарубежных экспертов по этой тематике. Результатом является консенсус-прогноз о наиболее вероятных и ожидаемых в «постковидную»эпоху трендах в таких сферах, как: транспорт и логистика, организация труда, цифровые и бесконтактные платежи, мониторинг с использованием Интернета вещей и больших данных, цифровизация потребительского опыта, здравоохранение, закрепление цифровой инфраструктуры, образование, бесконтактные интерфейсы и взаимодействия, автоматизация и роботизация, общественное устройство и государственное управление, занятость, развитие е-спорта, онлайн-эвенты и др.

We study risk premiums in the U.S. Treasury bond market from the perspective of a Bayesian econometrician BLwho learns in real time from disagreement among investors about future bond yields. Notably, disagreement has substantial predictive power for yields, and BL's risk premiums are less volatile than those in the analogous model without learning. BL's forecasts are substantially more accurate than the consensus forecasts of market professionals, particularly following U.S. recessions. The predictive power of disagreement is distinct from the (much weaker) one of inflation and output growth. Rather, it appears to reflect uncertainty about future fiscal policy.

PurposeThis paper contributes to shed light on the quality and performance of US fiscal forecasts.Design/methodology/approachThe first part inspects the causes of official fiscal forecasts revisions by Congressional Budget Office (CBO) between 1984 and 2016 that are due to technical, economic or policy reasons.FindingsBoth individual and cumulative means of forecast errors are relatively close to zero, particularly in the case of expenditures. CBO averages indicate net average downward revenue and expenditure revisions and net average upward deficit revisions. Focusing on the causes of the technical component, the authors uncover that its revisions are quite unpredictable, which cast doubts on inferences about fiscal policy sustainability that rely on point estimates. Comparing official with private-sector (consensus) forecasts, despite the informational advantages CBO might have, one cannot unequivocally say that one or the other is more accurate. Evidence also seems to suggest that CBO forecasts are consistently heavily biased toward optimism while this is less the case for consensus forecasts. Not only is the extent of information rigidity is more prevalent in CBO forecasts but also evidence seems to indicate that consensus forecasts dominate CBO in terms of information content.Originality/valueThe authors provide a detailed analysis on US fiscal forecasts both using revenue and expenditure and decomposing forecast errors into several explanatory components. Moreover, the authors compare official with private-sector (consensus) forecasts and assess which one is better or preferred.

Little has been documented about the benefits and impacts of the recent growth in climate services, despite a growing call to justify their value and stimulate investment. Regional Climate Outlook Forums (RCOFs), an integral part of the public and private enterprise of climate services, have been implemented over the last 20 years with the objectives of producing and disseminating seasonal climate forecasts to inform improved climate risk management and adaptation. In proposing guidance on how to measure the success of RCOFs, we offer three broad evaluative categories that are based on the primary stated goals of the RCOFs: 1) quality of the climate information used and developed at RCOFs; 2) legitimacy of RCOF processes focused on consensus forecasts, broad user engagement, and capacity building; and 3) usability of the climate information produced at RCOFs. Evaluating the quality of information relies largely on quantitative measures and statistical techniques that are standardized and transferrable, but assessing the RCOF processes and perceived usability of RCOF products will necessitate a combination of quantitative and qualitative social science methods that are sensitive to highly variable regional contexts. As RCOFs have taken up different formats and procedures to adapt to diverse institutional and political settings and varied technical and scientific capacities, objective evaluation methods adopted should align with the goals and intent of the evaluation and be performed in a participatory, coproduction manner where producers and users of climate services together design the evaluation metrics and processes. To fully capture the potential benefits of the RCOFs, it may be necessary to adjust or recalibrate the goals of these forums to better fit the evolving landscape of climate services development, needs, and provision.

When will a large group provide an accurate answer to a question involving quantity estimation? We empirically examine this question on a crowd-based corporate earnings forecast platform (Estimize.com). By tracking user activities, we monitor the amount of public information a user views before making an earnings forecast. We find that the more public information users view, the less weight they put on their own private information. Although this improves the accuracy of individual forecasts, it reduces the accuracy of the group consensus forecast because useful private information is prevented from entering the consensus. To address endogeneity concerns related to a user’s information acquisition choice, we collaborate with Estimize.com to run experiments that restrict the information available to randomly selected stocks and users. The experiments confirm that “independent” forecasts result in a more accurate consensus. Estimize.com was convinced to switch to a “blind” platform from November 2015 on. The findings suggest that the wisdom of crowds can be better harnessed by encouraging independent voices from among group members and that more public information disclosure may not always improve group decision making. This paper was accepted by Renee Adams, finance.

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.

We compare multivariate and univariate approaches to assessing the accuracy of competing density forecasts of a portfolio return in the downside part of the support. We argue that the common practice of performing multivariate forecast comparisons can be problematic in the context of assessing portfolio risk, since better multivariate forecasts do not necessarily correspond to better aggregate portfolio return forecasts. This is illustrated by examples that involve (skew) elliptical distributions and an application to daily returns of a number of US stock prices. In addition, time-varying test statistics and Value-at-Risk forecasts provide empirical evidence of regime changes over the last decades.

We examine a new set of U.S. fiscal forecasts from the FOMC briefing books. These forecasts are precisely those that were presented to monetary policymakers, and include frequently-updated estimates covering six complete business cycles and several fiscal-policy regimes. We detail the performances of forecast federal expenditures, revenues, surpluses, and structural surpluses in terms of their accuracy, bias, and efficiency. We find that forecast errors can be large economically, even at relatively short forecast horizons. While economic activity became less volatile after 1990, fiscal policy became harder to forecast. Finally, cyclically-adjusted deficit forecasts appear to be over-optimistic around both peaks and troughs of the business cycle, suggesting that fiscal policy is counter-cyclical in downturns and pro-cyclical in the early stages of recoveries.

What is the link between stock returns and news about economic growth? Using consensus forecasts from the Philadelphia Fed’s Survey of Professional Forecasters, I find that the univariate association between stock returns and gross domestic product (GDP) growth forecast surprises is indistinguishable from zero. Although consistent with prior macro-finance research, this phenomenon is intriguing if one believes that the stock market should move in sync with the economy. I consider two non–mutually exclusive hypotheses for this puzzling phenomenon. The first hypothesis is that GDP growth forecast surprises are correlated with offsetting cash flow news and discount rate news. The second hypothesis is that GDP growth forecast surprises measure news about economic growth with noise. I extract a measure of market-level discount rate news using accounting data and find evidence consistent with the hypothesis of offsetting value-relevant news. Overall, this article makes an important step toward resolving evidence of a disconnect between stock market returns and news about economic growth. More broadly, this article illustrates how accounting constructs and methods can be applied to inform macro-finance questions.

We show that quantile regression is better than ordinary-least-squares (OLS) regression in forecasting profitability for a range of profitability measures following the conventional setup of the accounting literature, including the mean absolute forecast error (MAFE) evaluation criterion. Moreover, we perform both a simulated-data and an archival-data analysis to examine how the forecasting performance of quantile regression against OLS changes with the shape of the profitability distribution. Considering the MAFE and mean squared forecast error (MSFE) criteria together, we see that the quantile regression is more accurate relative to OLS when the profitability to be forecast has a heavier-tailed distribution. In addition, the asymmetry of the profitability distribution has either a U-shape or an inverted-U-shape effect on the forecasting accuracy of quantile regression. An application of the distributional shape analysis framework to cash flow forecasting demonstrates the usefulness of the framework beyond profitability forecasting, providing additional empirical evidence on the positive effect of tail-heaviness and supporting the notion of an inverted-U-shape effect of asymmetry. This paper was accepted by Shiva Rajgopal, accounting.

Financial analysts tend to demonstrate herding behaviour, which sometimes compromises accuracy. A number of explanations spanning rational economic logic, cognitive biases, and social forces have been suggested. Relying on an experimental setting where participants forecast future earnings from a rich information set, we posit and obtain support for individual risk tolerance (or lack thereof) as an explanatory variable for herding behaviours. Specifically, less risk tolerant individuals forecast with less boldness and instead issue forecasts in agreement with the consensus forecast. The results are argued to be at least partially a product of cognitive biases and an intuitive reaction to uncertainty.

This paper argues that order flow can explain exchange rate forecasting errors. A unified theoretical model is developed showing that forecasting errors can be explained by both informational rigidities and portfolio shifts. This is applied to Brazilian data using a unique data set of daily consensus exchange rate forecasts managed by the Banco Central do Brasil along with order flow derived from the FX futures market. The results strongly support the theory.

There is a considerable volume of literature concerned with point forecasting which aims to assess whether producing aggregate forecasts as the sum of the components’ forecasts is better than alternative direct methods, whereas aggregate density forecasting from disaggregate components is still a relatively unexplored field. This paper develops an implementation of the bottom-up approach that is capable of producing well-performing and competitive density forecasts. This is achieved by accounting explicitly for the interaction between components, using Bayesian VARs to estimate the whole multivariate process and produce the aggregate forecasts. An empirical application using CPI and GDP data shows that the method can be used to produce aggregate density forecasts capable of accounting for the events resulting from the crisis. This suggests that it might be particularly useful for forecasting in turbulent times and therefore prove a valuable addition to the forecaster’s toolkit.

Seasonal forecasts of rainfall are considered the priority timescale by many users in the tropics. In East Africa, the primary operational seasonal forecast for the region is produced by the Greater Horn of Africa Climate Outlook Forum (GHACOF), and issued ahead of each rainfall season. This study evaluates and compares the GHACOF consensus forecasts with dynamical model forecasts from the UK Met Office GloSea5 seasonal prediction system for the two rainy seasons. GloSea demonstrates positive skill (r = 0.69) for the short rains at 1 month lead. In contrast, skill is low for the long rains due to lack of predictability of driving factors. For both seasons GHACOF forecasts show generally lower levels of skill than GloSea. Several systematic errors within the GHACOF forecasts are identified; the largest being the tendency to over-estimate the likelihood of near normal rainfall, with over 70% (80%) of forecasts giving this category the highest probability in the short (long) rains. In a more detailed evaluation of GloSea, a large wet bias, increasing with forecast lead time, is identified in the short rains. This bias is attributed to a developing cold SST bias in the eastern Indian Ocean, driving an easterly wind bias across the equatorial Indian Ocean. These biases affect the mean state moisture availability, and could act to reduce the ability of the dynamical model in predicting interannual variability, which may also be relevant to predictions from coupled models on longer timescales.

Existing accounting-based forecasting models of earnings either do not fully consider information that is contained in stock prices or use an ad hoc specification that is not based on rigorous valuation theory. In this paper, we develop an earnings forecasting model built on the theoretical linkages between future earnings and stock prices as well as a number of accounting fundamental variables. We find that our model-based forecasts of earnings are in general less biased and more accurate than both existing model-based forecasts and analysts' consensus forecasts, at both shorter and longer horizons. We also show that the accuracy of both model-based forecasts and financial analysts' forecasts depend on firm-specific characteristics such as firm size and industry membership.

A long line of literature documents that managers are more likely to issue guidance when the analysts’ consensus forecast is optimistic. I extend this literature by providing evidence that there exists a hierarchical order by which managers respond to individual analyst forecasts. First, I find that optimism in both the majority of individual forecasts and the influential analyst’s forecast are important determinants of management guidance in addition to optimism in the consensus forecasts. Second, I find that managers are more likely to issue guidance when the influential analyst forecast is optimistic, even when the consensus forecast is pessimistic. Third, when the influential analyst disagrees with the majority, managers are more willing to respond to the influential analyst. Taken collectively, this study provides new evidence on how individual analysts influence managers’ voluntary disclosure and suggests a hierarchy, whereby the influential analyst’s opinion is the most important factor in determining management guidance, followed by the majority or consensus.

This paper assesses the performance of the IMF’s unemployment forecasts for 84 countries, both advanced and emerging market economies, between 1990 and 2015. The forecasts are reported in the World Economic Outlook, a leading IMF publication. The forecasts display a small amount of bias—they tend to predict lower unemployment outcomes than occur—which arises because the forecasters fail to predict accurately the sharp increase in unemployment during downturns. Forecasts are characterized by inefficiency (errors of the past are repeated in the present) and rigidity (forecast revisions are serially correlated). There is little to choose between IMF and Consensus Forecasts, a source of private sector forecasts, for the small subset of 12 countries for which both sets of forecasts are available.

A popular approach to forecasting macroeconomic variables is to utilize a large number of predictors. Several regularization and shrinkage methods can be used to exploit such high-dimensional datasets, and have been shown to improve forecast accuracy for the US economy. To assess whether similar results hold for economies with different characteristics, an Australian dataset containing observations on 151 aggregate and disaggregate economic series as well as 185 international variables, is introduced. An extensive empirical study is carried out investigating forecasts at different horizons, using a variety of methods and with information sets containing an increasing number of predictors. In contrast to other countries the results show that it is difficult to forecast Australian key macroeconomic variables more accurately than some simple benchmarks. In line with other studies we also find that there is little to no improvement in forecast accuracy when the number of predictors is expanded beyond 20–40 variables and international factors do not seem to help.

We develop a new dynamic multivariate model for the analysis and forecasting of football match results in national league competitions. The proposed dynamic model is based on the score of the predictive observation mass function for a high-dimensional panel of weekly match results. Our main interest is in forecasting whether the match result is a win, a loss or a draw for each team. The dynamic model for delivering such forecasts can be based on three different dependent variables: the pairwise count of the number of goals, the difference between the numbers of goals, or the category of the match result (win, loss, draw). The different dependent variables require different distributional assumptions. Furthermore, different dynamic model specifications can be considered for generating the forecasts. We investigate empirically which dependent variable and which dynamic model specification yield the best forecasting results. We validate the precision of the resulting forecasts and the success of the forecasts in a betting simulation in an extensive forecasting study for match results from six large European football competitions. Finally, we conclude that the dynamic model for pairwise counts delivers the most precise forecasts while the dynamic model for the difference between counts is most successful for betting, but that both outperform benchmark and other competing models.

Using a panel of 54 countries between 1980 and 2013, we find empirical support for the view that changes in the fiscal policy stance (year-on-year change in the cyclically adjusted primary balance) have a significant positive correlation with inflation volatility. An increase in the volatility of discretionary fiscal policies by one standard deviation raises inflation volatility by about 6%. Moreover, results using alternative inflation volatility proxies confirm that an expansionary fiscal stance increases price volatility. Another relevant outcome is that in a context of economic expansions (recessions) the harmful impact of fiscal activism on price volatility is soft (heightened), while the negative impact of fiscal activism on price stability is higher when fiscal policy is expansionary. Finally, fiscal activism fuels inflation volatility much more pronouncedly in emerging market economies vis-à-vis advanced economies.