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Monitoring business cycles faces two potentially conflicting objectives: accuracy and timeliness. To strike a balance between these dual objectives, we propose a Bayesian sequential quickest detection method to identify turning points in real time with a sequential stopping time as a solution. Using four monthly indexes of real economic activity in the United States, we evaluated the method’s real-time ability to date the past five recessions. The proposed method identified similar turning-point dates as the National Bureau of Economic Research (NBER), with no false alarms, but on average, it dated peaks four months faster and troughs 10 months faster relative to the NBER announcement. The timeliness of our method is also notable compared to the dynamic factor Markov-switching model: the average lead time was about five months when dating peaks and two months when dating troughs.

We construct measures of individual forecasters' subjective uncertainty at horizons ranging from one to five years, incorporating a rich information set from the European Central Bank's Survey of Professional Forecasters. We find that the uncertainty curve is more linear than the disagreement curve --- uncertainty at the one-year and two-year horizons can almost perfectly predict uncertainty at the five-year horizon, but not so for disagreement. We document substantial heterogeneity across forecasters in both the level and the term structure of uncertainty, and show that the difference between long-run and short-run uncertainty is procyclical. We develop a signal extraction model that features (i) Kalman filter updating, (ii) time-varying uncertainty and (iii) multi-step ahead forecasting. Our model implies that the heterogeneous patterns of uncertainty over different time horizons depend on forecaster's perceived persistence and volatility of the signal and the noise.

The COVID-induced recession began in March 2020 for the United States. We identify this turning point by applying a Bayesian sequential quickest detection method to a real-time index of economic ac...

By matching a large database of individual forecaster data with the universe of sizable natural disasters across 54 countries, we identify a set of new stylized facts: (i) forecasters are persistently heterogeneous in how often they issue or revise a forecast; (ii) information rigidity declines significantly following large, unexpected natural disaster shocks; (iii) the response of forecast disagreement displays interesting patterns: attentive forecasters tend to move away from the previous consensus following a disaster while the opposite is true for inattentive forecasters. We develop a learning model that captures the two channels through which natural disaster shocks affect expectation formation: attention effect { the visibly large shocks induce immediate and synchronized updating of information for inattentive agents, and uncertainty effect { the occurrence of those shocks generates increased uncertainty among attentive agents.

We document and evaluate how businesses are reacting to the COVID-19 crisis through August 2020. First, on net, firms see the shock (thus far) largely as a demand rather than supply shock. A greater share of firms report significant or severe disruptions to sales activity than to supply chains. We compare these measures of disruption to their expected changes in selling prices and find that, even for firms that report supply chain disruptions, they expect to lower near-term selling prices on average. We also show that firms are engaging in wage cuts and expect to trim wages further before the end of 2020. These cuts stem from firms that have been disproportionally negatively impacted by the pandemic. Second, firms (like professional forecasters) have responded to the COVID-19 pandemic by lowering their one-year-ahead inflation expectations. These responses stand in stark contrast to that of household inflation expectations (as measured by the University of Michigan or the New York Fed). Indeed, firms’ one-year-ahead inflation expectations fell precipitously (to a series low) following the onset of the pandemic, while household measures of inflation expectations jumped markedly. Third, despite the dramatic decline in firms’ near-term inflation expectations, their longer-run inflation expectations have remained relatively stable.

Why was economic forecasting so difficult during COVID-19? To answer this question, we organized an online workshop in July 2020, sponsored by the International Institute of Forecasters (IIF) and hosted by American University.1 Below you will find some of the lessons that can be drawn from the special issue we edited.

We propose a new measure of macroeconomic uncertainty that incorporates a rich information set from U.S. SPF density forecasts. Our measure has two key advantages over traditional measures: (i) it reflects the subjective perceptions of market participants; and (ii) it is an ex ante measure that does not require a knowledge of realized outcomes. We study the features of this measure of macroeconomic uncertainty and explore its impact on real economic activities within the U.S., as well as its spillover effects for BRIC countries.

Stock prices and workplace mobility trace out striking clockwise paths in daily data from mid-February to late May 2020. Global stock prices fell 30% from 17 February to 12 March, before mobility declined. Over the next 11 days, stocks fell another 10 percentage points as mobility dropped 40%. From 23 March to 9 April, stocks recovered half their losses and mobility fell further. From 9 April to late May, both stocks and mobility rose modestly. This dynamic plays out across the 35 countries in our sample, with notable departures in China, South Korea and Taiwan. The size of the global stock market crash in reaction to the pandemic is many times larger than a standard asset-pricing model implies. Looking more closely at the world’s two largest economies, the pandemic had greater effects on stock market levels and volatilities in the USA than in China even before it became evident that early US containment efforts would flounder. Newspaper-based narrative evidence confirms the dominant—and historically unprecedented—role of pandemic-related developments in the stock market behavior of both countries.

We document and evaluate how businesses are reacting to the COVID-19 crisis through August 2020. First, on net, firms see the shock (thus far) largely as a demand rather than supply shock. A greater share of firms reports significant or severe disruption to sales activity than to supply chains. We compare these measures of disruption to their expected changes in selling prices and find that, even for firms that report supply chain disruption, they expect to lower near-term selling prices on average. We also show that firms are engaging in wage cuts and expect to trim wages further before the end of 2020. These cuts stem from firms that have been disproportionally negatively affected by the pandemic. Second, firms (like professional forecasters) have responded to the COVID-19 pandemic by lowering their one-year-ahead inflation expectations. These responses stand in stark contrast to that of household inflation expectations (as measured by the University of Michigan or the New York Fed). Indeed, firms' one-year-ahead inflation expectations fell precipitously (to a series low) following the onset of the pandemic, while household measures of inflation expectations jumped markedly. Third, despite the dramatic decline in firms’ near-term inflation expectations, their longer-run inflation expectations remain reasonably well anchored.

Stock prices and workplace mobility trace out striking clockwise paths in daily data from mid-February to late May 2020. Global stock prices fell 30 percent from 17 February to 12 March, before mobility declined. Over the next 11 days, stocks fell another 10 percentage points as mobility dropped 40 percent. From 23 March to 9 April, stocks recovered half their losses and mobility fell further. From 9 April to late May, both stocks and mobility rose modestly. This dynamic plays out across the 35 countries in our sample, with notable departures in China, South Korea, and Taiwan. The size of the global stock market crash in reaction to the pandemic is many times larger than a standard asset-pricing model implies. Looking more closely at the world’s two largest economies, the pandemic had greater effects on stock market levels and volatilities in the U.S. than in China even before it became evident that early U.S. containment efforts would flounder. Newspaper-based narrative evidence confirms the dominant – and historically unprecedented – role of pandemic-related developments in the stock market behavior of both countries.

This paper presents a dynamic general equilibrium model with a health shock in a multi-sector model The health shock leads to a reduction in (i) labor supply i

The COVID-induced recession began in March 2020 for the United States. We identify this turning point by applying a Bayesian sequential quickest detection method to a real-time index of economic activity. Supporting evidence is also found from macroeconomic data releases and stock markets.

Using a unique loan-level dataset from the World Bank’s Debtor Reporting System, we construct new measures of external debt shocks for 120 low- and middle-income countries during the 1975–2018 period. We identify the shock in two steps by first calculating the difference between actual and predicted net disbursement on external debt obligation for each loan and then taking aggregation at the country-year level. During expansionary times, external debt shocks lead to persistent decreases in the external debt to GDP ratio, possibly due to the availability of other sources of financing. During recessionary episodes, however, we see heavy reliance on external debt financing for most of developing countries. This reliance is more substantial for countries with higher levels of external debt stock, raising serious concerns for debt distress in these countries and in their road to building resilience.

We assess how well measures of disagreement in qualitative survey expectations reflect disagreement in corresponding quantitative expectations. We consider a variety of measures, belonging to two categories: measures of dispersion in nominal and ordinal variables and measures based on the probability approach of Carlson and Parkin (Economica, 1975; 42, 123–138). Using data from two household surveys that collect both qualitative and quantitative inflation expectations, we find that the probability approaches with time-varying categorization thresholds and either a piecewise uniform or t distribution perform best and the resulting disagreement estimates are highly correlated with the benchmark. Copyright © 2015 John Wiley & Sons, Ltd.

Motivated by the literature on the capital asset pricing model, we decompose the uncertainty of a typical forecaster into common and idiosyncratic uncertainty. Using individual survey data from the Consensus Forecasts over the period of 1989–2014, we develop monthly measures of macroeconomic uncertainty covering 45 countries and construct a measure of global uncertainty as the weighted average of country-specific uncertainties. Our measure captures perceived uncertainty of market participants and derives from two components that are shown to exhibit strikingly different behavior. Common uncertainty shocks produce the large and persistent negative response in real economic activity, whereas the contributions of idiosyncratic uncertainty shocks are negligible.

Relying on the well-established theoretical result that uncertainty has a common and an idiosyncratic component, we propose a new measure of earnings forecast uncertainty as the sum of dispersion among analysts and the variance of mean forecast errors estimated by a GARCH model. The new measure is based on both common and private information available to analysts at the time they make their forecasts. Hence, it alleviates some of the limitations of other commonly used proxies for forecast uncertainty in the literature. Using analysts' earnings forecasts, we find direct evidence of the new measure's superior performance.

Using a standard decomposition of forecast errors into common and idiosyncratic shocks, we show that aggregate forecast uncertainty can be expressed as the disagreement among the forecasters plus the perceived variability of future aggregate shocks. Thus the reliability of disagreement as a proxy for uncertainty will be determined by the stability of the forecasting environment and the length of the forecast horizon. Using density forecasts from the Survey of Professional Forecasters, we find direct evidence in support of our hypothesis. Our results support the use of GARCH-type models, rather than the ex post squared errors in consensus forecasts, to estimate the ex ante variability of aggregate shocks as a component of aggregate uncertainty. Copyright © 2010 John Wiley & Sons, Ltd.

In empirical tests guided by recent theory (e.g., Hughes, Liu and Liu 2007; and Lambert, Leuz and Verrecchia 2011), we examine the joint effects of information precision, information asymmetry and the level of market competition on firms’ cost of equity capital. Consistent with theory, we find that average information precision and the level of market competition reduce the positive effect of information asymmetry but do not eliminate it. Besides examining various aspects of the environment jointly, our study is also unique in that we follow the suggestions of Sheng and Thevenot (2012) for modifying the Barron, Kim, Lim and Stevens (1998) measures of information asymmetry and precision. We find that cost of equity capital varies greatly with the modified measures of information asymmetry and average information precision. For example, our regression estimates suggest that information asymmetry and average information precision are more important than equity beta and firm size in determining firms’ cost of capital, and that such a substantial effect from information asymmetry and information precision is not apparent using unmodified BKLS measures.

We investigate the impact of an uncertain number of false individual null hypotheses on commonly used p value combination methods. Under such uncertainty, these methods perform quite differently and often yield conflicting results. Consequently, we develop a combination of “combinations of p values” (CCP) test aimed at maintaining good power properties across such uncertainty. The CCP test is based on a simple union–intersection principle that exploits the weak correspondence between two underlying p value combination methods. Monte Carlo simulations show that the CCP test controls size and closely tracks the power of the best individual methods. We empirically apply the CCP test to explore the stationarity in real exchange rates and the information rigidity in inflation and output growth forecasts.

By decomposing analysts’ forecast errors into common and idiosyncratic components, we develop a simple model aimed at explaining the relationship between forecast uncertainty and analyst dispersion. Under this framework, we propose a new measure of earnings forecast uncertainty as the sum of dispersion among analysts and the variance of mean forecast errors estimated by a GARCH model. The new measure gives an ex ante estimate of uncertainty arising from both analysts’ common and private information. Hence, it circumvents the limitations of other commonly-used proxies for forecast uncertainty in the literature. Using analysts’ earnings forecasts, we find direct evidence for the superior performance of the new measure.

This paper proposes a simple panel unit root test based on Zaykin et al.’s (2002) truncated product method. The test is powerful in cases where there are only a few large p-values, and is robust to a certain degree of cross-section dependence. Monte Carlo evidence shows good size and power properties relative to existing p-value combination tests. Unlike the previous tests, the new test allows to make stronger claims in the event of rejection of the null hypothesis. The proposed test is applied to a panel of 27 OECD real exchange rate series as well as to a group of inflation density forecasts in the SPF data.

This paper provides a detailed analysis of the forecasts of real GDP, inflation and unemployment made by individual members of the Federal Open Market Committee (FOMC) for the period 1992–2003. Despite a general tendency for the committee members to underpredict real GDP over the sample period, we find evidence suggesting that the FOMC has a considerable amount of information about output growth, beyond what is known by commercial forecasters. We also document a substantial level of variation in the members’ forecasts, which can be explained in part by the differences in economic conditions between Federal Reserve districts. The members’ heterogeneous forecasts for output growth and inflation contain useful information for explaining their preferred policy settings, beyond that in the Greenbook forecasts.

We estimate a Bayesian learning model with heterogeneity aimed at explaining the evolution of expert disagreement in forecasting real GDP growth and inflation over 24 monthly horizons for G7 countries during 1990-2007. Professional forecasters are found to begin and have relatively more success in predicting inflation than real GDP at significantly longer horizons; forecasts for real GDP contain little information beyond 6 quarters, but forecasts for inflation have predictive value beyond 24 months and even 36 months for some countries. Forecast disagreement arises from two primary sources in our model: differences in the initial prior beliefs of experts, and differences in the interpretation of new public information. Estimated model parameters, together with two separate case studies on (i) the dynamics of forecast disagreement in the aftermath of the 9/11 terrorist attack in the U.S. and (ii) the successful inflation targeting experience in Italy after 1997, firmly establish the importance of these two pathways to expert disagreement.

We estimate a Bayesian learning model with heterogeneity aimed at explaining expert forecast disagreement and its evolution over horizons. Disagreement is postulated to have three components due to differences in: (i) the initial prior beliefs, (ii) the weights attached on priors, and (iii) interpreting public information. The fixed-target, multi-horizon, cross-country feature of the panel data allows us to estimate the relative importance of each component precisely. The first component explains nearly all to 30% of forecast disagreement as the horizon decreases from 24 months to 1 month. This finding firmly establishes the role of initial prior beliefs in generating expectation stickiness. We find the second component to have barely any effect on the evolution of forecast disagreement among experts. The importance of the third component increases from almost nothing to 70% as the horizon gets shorter via its interaction with the quality of the incoming news. We propose a new test of forecast efficiency in the context of Bayesian information processing and find significant heterogeneity in the nature of inefficiency across horizons and countries.