The Unassuming Economist

From a paper by Pradyumna Dash, Ankit Kumar, and Chetan Subramanian:

“This study investigates the effects of U.S. monetary policy on income inequality in open economies from 1970 to 2016. We find that a 100-basis-point increase in the federal funds rate leads to a cumulative reduction of about 0.15% in income inequality over three years. Interestingly, we show that the effect of US monetary policy on inequality varies over time. The impact also varies by exchange rate regime: in flexible regimes, the reduction can reach nearly 0.3%, while in pegged regimes, it diminishes to around 0.13%. This impact in pegged regimes is influenced by wage rigidity and labor market regulations in the economy. To explain these results, we develop a two-agent small open economy model that incorporates rigid wages, highlighting the link between monetary policy and inequality dynamics.”

From a paper by Makram El-Shagi, and Steven J. Yamarik:

“This paper examines the impact of Federal Reserve policy on income inequality across US states. We use the local projections method of Jordà to estimate impulse response functions for each state. We find that a restrictive monetary policy increases income inequality in almost all states, but of differing magnitudes. We also use panel analysis to examine the possible transmission mechanisms that account for these differences. Our empirical results confirm the theoretical predictions – inequality is increased by higher inflation, home ownership, and earnings in the finance, insurance and real estate (FIRE) sector; but decreased by higher housing prices, unionization rates, educational attainment and minimum wage.”

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 Martin T. Bohl, Niklas Humann, and Pierre L. Siklos:

“This survey synthesizes evidence on the bidirectional links between commodity markets and monetary policy. On the commodities-to-policy side, we review how shocks to energy, food, and metals pass through to inflation, inflation expectations, economic activity, and financial stability in state-dependent ways that vary by shock type, exposure, and policy regime. We complement the literature with an analysis of central-bank speeches, showing how officials classify commodity shocks and how these framings map into policy stances. On the policy-to-commodities side, we organize evidence on the transmission of monetary policy to commodity markets via financial, real-economy, and expectations channels, highlighting heterogeneity across policy instruments, commodities, and central banks. We emphasize how financialization tightens cross-asset linkages, raises leverage and margin sensitivity, and amplifies discount-rate and risk-taking mechanisms. Overall, commodities are best treated as policy sensitive state variables, not exogenous disturbances, with implications for policy design, central bank communication, and international monetary spillovers.”

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.”