The Unassuming Economist

From a paper by Priyanka Banerji and Mohammed B. Shettima:

“The most demanding sector in today’s era is the energy sector. India is now the third highest consumer of crude oil in the world, after the USA and China, with 4.6% share of world total according to Worldometer. Thus, when there is an increase in the oil prices, the Indian economy suffers relentlessly. This paper seeks to carry out a study on the volatility of oil price and the real exchange rate and its effects on Indian rupee and the US dollar. The study will make use of the Garch analysis model to analyse and predict the volatility of the two variables (crude oil prices and exchange rate). Empirical findings points out an asymmetrical relationship between crude oil prices and dollar exchange rate. Recommendations are made to the Indian Government to provide effective energy security source by establishing strategic crude oil storage facilities so as to reduce importation of crude oil.”

From a paper by Alkis Blanz, Ulrich Eydam, Maik Heinemann, Matthias Kalkuhl:

“Since market-based climate policies such as carbon pricing affect the cost of using fossil resources, rule-based climate policy adjustments in response to fossil energy price shocks may promote macroeconomic stabilization. This raises the question of whether climate policy should adapt to short-term fluctuations in fossil energy prices. We examine this question by employing a dynamic stochastic general equilibrium (DSGE) model calibrated for the German economy. Our results indicate that the macroeconomic and welfare impacts of rule-based carbon pricing adjustments depend on the share of recycled revenue. If revenue is fully absorbed, lowering emissions prices can stabilize the economy in response to rising energy prices. Conversely, if revenue is at least partially recycled, maintaining a stable carbon price will improve overall welfare. With a stable carbon price, revenue recycling acts as insurance against fluctuating energy prices. This result remains robust across several robustness checks.”

From a paper by Luciano Vereda, Helder Ferreira de Mendonça, and George Morcerf:

“Our study advances the modeling of forecast revisions by accounting for the nuanced impact of informational shocks across different time horizons. Specifically, we introduce modifications to the error structure of regression models used to detect biases in macroeconomic forecasts. Drawing on consensus forecasts of inflation and output growth from the central banks of Brazil, Chile, and Mexico, our approach offers a nuanced understanding of bias estimation uncertainty, leading to a more robust rejection of the null hypothesis of no biases. By elucidating the differential effects of informational shocks on forecast accuracy across time periods, our findings not only contribute to the refinement of forecasting methodologies but also have implications for policymakers and economic analysts striving for more accurate and reliable predictions in dynamic economic environments.”

From a paper by Boqiang Lin, and Yijie Song:

“Macroeconomic factors such as coal prices affect corporate decision making. With a set of 39,795 observations of firm-year data, encompassing 30 provinces and spanning the years 2005–2022, this paper estimates the impact of coal price shocks to corporate risk-taking of listed companies in China. The empirical results show that coal prices change negatively affect corporate risk-taking due to internal risk aversion. Further analysis shows that this effect is asymmetric between positive and negative shocks, and is more significant among higher coal dependence provinces. External factors such as economic policy uncertainty further reduce risk-taking. This study provides both macro and micro perspective analysis of corporate risk-taking and coal prices influences, contributing to the policy references for corporate strategic choices and coal price adjustments in the process of energy transition of China.”

From a paper by Khoja Akhmet Yassawi, Mukhtar Auezov, and Miras University:

“The investigation delved into the dynamic interplay between oil price fluctuations and their ramifications on the pricing of agricultural products, employing the Vector Autoregression methodology. The dataset spanned 3 months, commencing from January 2010 and concluding in December Upon subjecting the data to an in-depth analysis, it was ascertained that it possesses a unit root, indicating an integrated order of one (I [1]), and achieves stationarity subsequent to the first-order differencing. The findings of the inquiry revealed that the paramount driver influencing agricultural product prices is the inherent volatility within the agricultural sector itself. Contrary to initial expectations, the impact of oil price fluctuations on agricultural prices was discerned to be comparatively modest. Intriguingly, the outcomes underscored that the preeminent factor contributing to fluctuations in agricultural product prices is the influence wielded by oil prices. This implies that alterations in oil prices exert a more pronounced effect on the variability in agricultural product prices as opposed to the overall revenue generated from agricultural endeavors.”