This paper investigates the technical efficiency of cereal production in European Union (EU) countries from 2008 to 2018. The primary purpose is to estimate technical efficiency scores by country and crop and explain their variation using macroeconomic and agricultural policy variables using the stochastic frontier production function. The results indicate that the United Kingdom has the highest efficiency in barley production, while Finland has the lowest. France exhibits the highest efficiency in common wheat, and Romania has the lowest efficiency in grain maize production. This study also explores the impact of various factors on technical efficiency, finding, for example, the positive effects of female wages and foreign direct investment on barley production efficiency and the negative effects of forest area and subsidies. Similar analyses were conducted for wheat and maize production. The results indicate a variation in technical efficiency scores across crops and countries. This diversity in performance not only reflects the inherent complexities within each crop but also emphasizes the crucial role played by macroeconomic variables and agricultural policies in shaping efficiency outcomes on a country-wide scale.

This paper investigates the forecasting accuracy of alternative time series models when augmented with partial least-squares (PLS) components extracted from economic data, such as Federal Reserve Economic Data, as well as Monthly Database (FRED-MD). Our results indicate that PLS components extracted from FRED-MD data reduce the forecasting error of linear models, such as ARIMA and SARIMA, but produce poor forecasts during high-volatility periods. In contrast, conditional variance models, such as ARCH and GARCH, produce more accurate forecasts regardless of whether or not the PLS components extracted from FRED-MD data are used.

Gasoline and crude oil price movements have been the focus of many studies in the last decade. We use the asymmetric error correction model (ERM) to examine the hypothesis of asymmetric pricing for both regular and premium gasoline markets at the US national level and in the four states with the highest gasoline consumption. Using weekly crude oil and retail gasoline prices from June 2000 to February 2023, the results show an asymmetric response in the gasoline market for all four states and at the national level. However, the adjustment speed tends to differ for the types of gasoline and across states. The implications of these results for policy and welfare are discussed in this study.

Introduction: Understanding the demand for imported green coffee in Saudi Arabia is crucial for stakeholders in the coffee trade. This study examines how demand varies by country of origin, focusing on Ethiopia, Brazil, India, and other exporting nations such as Colombia, Vietnam, and Kenya. Given the growing coffee consumption in Saudi Arabia, analyzing consumer preferences and market dynamics can provide insights for importers, exporters, and policymakers.Methods: This study employs the Almost Ideal Demand System (AIDS) model to estimate demand elasticities for green coffee imports. The analysis considers income and price elasticities to determine the classification of coffee imports as necessities or luxury goods, as well as cross-price elasticities to assess substitution and complementarity relationships between different origins.Results: Findings indicate that Ethiopian coffee dominates the Saudi market and is considered a luxury good with high income elasticity. In contrast, Brazilian and other coffees exhibit characteristics of necessities, with relatively stable demand. Indian coffee is highly price-elastic but maintains a smaller market share. Cross-price elasticity estimates reveal that Ethiopian and Indian coffees act as substitutes, whereas Ethiopian coffee complements imports from other sources.Discussion and conclusion: Projected trends suggest continued growth in Ethiopian, Brazilian, and Indian coffee imports, while imports from other origins may decline. These findings have practical implications for importers and marketers, who can refine sourcing and promotion strategies, and for exporters, particularly from Ethiopia and Brazil, who can strengthen their market positioning. Policymakers may leverage these insights to ensure stable coffee import flows through targeted trade policies. This study contributes to the coffee demand literature by emphasizing the role of origin differentiation in shaping market dynamics in Saudi Arabia's green coffee sector.

This study explores the factors that impact residential energy usage and spending in the United States. Using data from the 2020 Residential Energy Consumption Survey (RECS), we investigate the significance of different energy consumption determinants at various analysis levels. Our analysis covers residential energy usage, electricity, natural gas, propane, and fuel oil consumption. We also examine energy usage for space heating, cooling, and water heating. To leverage the extensive RECS data, which includes over 180 variables, we utilized machine learning (ML) techniques for feature selection and determined their Shapley contribution for different target outcomes. Our results indicate that the CatBoost algorithm outperforms other ML techniques on the 2020 Residential Energy Consumption Survey sample. Our findings demonstrate that it is not appropriate to aggregate consumption and expenditure, as each level has distinct important features. JEL Classification: D12, Q41, R21