This study investigates the application of Factor-Augmented Vector Autoregression (FAVAR) and Bayesian Vector Autoregression (BVAR) models for inflation forecasting. FAVAR models deal with high-dimensional data by extracting latent factors from extensive macroeconomic indicators, while BVAR...

This paper addresses the challenge of inflation forecasting by adopting a thick modeling approach that integrates forecasts from time- and frequency-domain models. Frequency-domain models excel at capturing long-term trends while also accounting for short-term fluctuations. Combining these...

This paper presents a novel 5-factor model for agricultural commodity risk premiums, an approach not explored in previous research. The model is applied to the specific cases of corn, soybeans, and wheat. Calibration is achieved using a Kalman filter and maximum likelihood, with data from...

Purpose - The purpose of this research is to develop a predictive model that can estimate the volume of remittances channeled toward Yemen's economic reconstruction efforts. Design/methodology/approach - This study utilized a time-series dataset encompassing remittance inflows into Yemen's...

This study examines the effectiveness of using webscraped data to predict price developments in the Austrian food retail sector. We calculate monthly nowcasts of price changes based on daily price data collected by the OeNB since mid-2020, using Eurostat methodology for price index calculation,...

I use Bayesian VARs with stochastic volatility to forecast global temperatures and sea level and ice cover in the Northerin emisphere until 2010, by exploiting (i) their long-run equilibrium relationship with climate change drivers (CCDs) and (ii) the relationship between world GDP and...

This study examines the out-of-sample predictability of expected skewness of oil price returns, which serves as a metric for global future risks, as we show statistically through the association with crises of different nature, for stock returns of 10 (8 advanced plus two emerging) countries...

In this article, we evaluate the relationship between quality investing combined with Economic Moat, ESG (Environmental, Social and Governance) and analyst opinions over the period 2014-2020 based on a data set comprising 803 US stocks. Performance is evaluated using several metrics (returns and...

Using a novel and unique panel dataset of individual-level professional forecasts at short, medium, and very-long horizons, we provide new stylized facts about survey forecasts. We present direct evidence that forecasters use multivariate models in an environment with imperfect information about...

The farm-level economic impacts of the FAPRI 2025 Baseline on representative crop and livestock operations are projected in this report, assuming provisions of the 2018 Farm Bill are in effect and continue throughout the 2025-2030 projection period. The current representative farm analysis...

Understanding the developments of atmospheric ethane is essential for better identifying the anthropogenic sources of methane, a major greenhouse gas with high global warming potential. While previous studies have focused on analyzing past trends in ethane and modeling the inter-annual...

Word embeddings are vectors of real numbers associated with words, designed to capture semantic and syntactic similarity between the words in a corpus of text. We estimate the word embeddings of the European Central Bank's introductory statements at monetary policy press conferences by using a...