The Unassuming Economist

From a paper by Karan Bhasin, Kajal Lahiri and Prakash Loungani:

“This paper estimates uncertainty shocks using density forecasts from the Reserve Bank of India’s Survey of Professional Forecasters (2008–2023). These forecasts enable a direct measurement of unobservable uncertainty in real-time, as the first difference in the second moment of the densities. In addition, we propose a forecast calibration test based on the predictive sequential principle. We report five key findings: (i) macroeconomic uncertainty in India has been on a decline since 2008; (ii) shocks to uncertainty derived from density forecasts compare favorably with other popular measures, viz. Economic Policy Uncertainty and VIX; (iii) prequential tests indicate forecasts to be calibrated; (iv) uncertainty is affected primarily by negative news and is variance rational, and (v) it captures demand shocks even after controlling for global uncertainty shocks, in contrast to EPU and VIX, which are primarily driven by supply shocks. Distinguishing these shocks is crucial for optimal monetary policy.”

From a paper by Ebrahim Merza, Mohammad Alawin, and Muna Husain:

“Inflation volatility remains one of the most important challenges for policymakers, households, and businesses alike. When prices fluctuate unpredictably, people lose confidence in their ability to plan ahead. Households struggle to budget and save, firms hesitate to invest and hire, and policymakers face higher pressure to act without clear guidance. Recent global crises—whether energy shocks, food price surges, or supply chain disruptions—have shown how quickly instability spreads across borders. This raises a central question: why are some countries more vulnerable to inflation volatility than others? Following Aisen and Veiga (2006), this study addresses that question by examining the determinants of inflation volatility across three income-based groups: lower-middle-income, upper-middle-income, and high-income economies, using panel data covering the period 1996-2024. Using both fixed and random-effects models, we find that inflation persistence and high inflation levels are the strongest drivers of volatility, while higher income levels and stronger governance support price stability. External shocks—such as trade openness, oil price fluctuations, and exchange-rate misalignments—show varied effects across income groups, emphasizing the importance of context-specific responses. The findings suggest that when countries invest in credible institutions and reliable policies, they can transform external shocks from being destabilizing forces into manageable challenges.”

From a paper by Thomas Chalaux, Dave Turner and Steven Cassimon:

“Recent research by authors from the IMF, ECB and the Bank of England has identified Random Forests as the most effective method for predicting crisis episodes and superior to the more traditional approach of probit/logit modelling. We challenge that finding when predicting recessions for 20 OECD countries. A customised algorithm that selects probit models, matches the performance of Random Forests in out-of-sample quarterly predictions using real-time data over a two-year horizon, including the period of the Global Financial Crisis. This enhanced performance is attributed to a “wisdom of crowds” feature whereby predictions are averaged from many well-fitting probit equations, comparable to Random Forests averaging many trees. Both country-specific and pooled Random Forests are estimated, and, although the latter has a superior out-of-sample performance, a disadvantage of the pooled approach is that recession risks are highly correlated across countries and rarely very elevated, so that it is unlikely that a recession is ever ‘more likely than not’. The estimation framework is also separately applied to 8 consecutive quarterly horizons and demonstrates that different explanatory variables matter at different horizons; activity variables are more important for immediate quarters, but financial cycle variables (especially credit and house prices) dominate at horizons beyond that.”

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 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.”