The Unassuming Economist

From a paper by Nigel Meade and Ciaran Driver:

“Policy makers are concerned with the accuracy of GDP forecasts and want to understand the reasons for the revision of forecasts. We study these issues by examining forecasts of annual UK GDP growth by a panel of agents, published monthly by HM Treasury. We focus on two main issues: the developing accuracy of the group-mean forecast as horizons shorten and the identification of information categories underlying agents’ forecast revisions. The accuracy of the group-mean forecast is poor; there is evidence of information rigidity in forecasts within the target year, and accuracy only improves in May of the target year when contemporary information flows lead to increased accuracy. We find a pessimism bias; the median errors of group-mean forecasts are increasingly positive for horizons shorter than 17months. We seek to explain revisions to both long- and short-horizon group-mean forecasts and individual agent forecasts. Modeling individual agents’ forecast revisions using a moving window, we note a consistent tendency by agents to revise their forecast towards the group-mean. Although their importance varied over time, the main information categories explaining revisions were, over longer horizons, the cost of finance, production, and a business confidence indicator. FX rates and inflation were influential over shorter horizons.”

From a paper by Tim Köhler:

“This study presents an examination of the predictive power of narrative reports from German economic institutes beyond traditional quantitative forecasts in anticipating economic recessions and directional changes in the business cycle. I transform qualitative narratives into quantitative sentiment scores using four different dictionaries and methods and use fixed-effect logistic regression to analyse their impact. To evaluate model performance, I use the Area under the Receiver Operating Characteristic Curve (AUROC) to compare models with versus without sentiment scores. Additionally, I employ DeLong’s test and bootstrapping to test the significance of AUROC improvements. Furthermore, I explore the potential of combining multiple sentiment scores to enhance forecasting accuracy. The results show that sentiment scores significantly enhance forecasting accuracy. This suggests that narrative information provides valuable insights beyond quantitative forecasts alone.”

From a paper by Lin Li, and Guoping Li:

“Using data on analysts’ individual forecasts from all listed companies in China’s A-share market for the period 2007–2022, we document two stylized facts. First, the average forecast error can be predicted by forecast revision. Second, individual forecasts appear to over-react to own revisions and salient public signals, we show that the first fact is inconsistent with standard models of full information rational expectations. The second fact suggests that individuals may be irrational with respect to their use of information. Expectation formation theory suitable for China’s capital market may need to combine information friction and irrational belief.”

From a paper by Abhishek Halder and M. Kannadhasan:

“The growing concern over energy security has raised critical questions about the role of energy uncertainty in shaping firm-level outcomes. While prior research underscores the importance of energy uncertainty, there is limited understanding of its impact on corporate bankruptcy risk, particularly in a cross-country context. This paper presents the first empirical evidence of the relationship between energy uncertainty and corporate bankruptcy risk using a sample of listed firms from 28 countries. The findings reveal that energy uncertainty escalates bankruptcy risk, which is consistent with resource dependency, agency and pecking order theories. Contracting profit margins and surging cost of debt are two intervening mechanisms through which energy uncertainty adversely impacts bankruptcy risk, indicating that heightened costs associated with operating and financing activities inflates financial distress. We also unveil that a conservative working capital policy and superior working capital efficiency diminish the detrimental impact of energy uncertainty. Our sub-sample analyses divulges that this detrimental impact is stronger in firms operating in high energy-consuming and cyclical sectors, and in those based in energy exporting and high energy-intensity countries. Furthermore, at low levels of energy uncertainty, firms effectively curtail bankruptcy risk by executing risk management measures whereas such measures are ineffective when energy uncertainty surpasses a threshold at ∼25th percentile. Our baseline result remains unchanged on employing several robustness checks. Overall, this study yields crucial insights and suggestions for corporate managers, regulators and policymakers to navigate energy shocks and to enhance firm resilience through strategic planning and decision-making.”

From a paper by Jeffrey A. Levy, Gabriel Mathy, and Xuguang Simon Sheng:

“Uncertain times are often bad times, and separating uncertainty shocks from large negative
level shocks is difficult. We use the Base Realignment and Closure (BRAC) process in the
United States in 2005 to investigate this issue, where the level shock may be positive, negative,
or neutral, and is finalized only after a well defined period of significant uncertainty. When
combined with an even greater period of time before the level shock is implemented, we can
clearly separate first-moment from second-moment shocks. When using attention to BRAC
as an instrument, we find that the effect of uncertainty on employment and the labor force is
small but significant, with a 1% decrease in response to a one standard deviation increase in
uncertainty. While similar, the peak effect is smaller and comes after a shorter lag than the
effect found in the existing literature that relies on dynamic models that fall short of making
causal claims.”