Gasoline retail markets have traditionally attracted a lot of attention from researchers and policy makers. This paper reviews 2 sets of questions studied by economists related to this market who seek to understand the behavior of their prices. The first is concerned with whether volatility can...

This paper considers the problem of forecasting inflation in the United States, the euro area and the United Kingdom in the presence of possible structural breaks and changing parameters. We examine a range of moving window techniques that have been proposed in the literature. We extend previous...

Gaining highly skilled human capital is one of the key motivations for mergers and acquisitions (M&A), particularly in knowledge-intensive sectors such as the technology industry. However, the inherent cultural differences and organizational misalignments during the integration process can lead...

The retail industry, particularly the grocery sector, generates massive amounts of data daily. Predicting sales accurately is essential for effective inventory management, supply chain optimization, and revenue maximization. In this research paper, we propose a comprehensive study of various...

This paper uses machine learning approaches to predict the association between traffic volume, air pollution, and meteorological conditions. A key focus is on the interaction between these factors. The paper does this using hourly traffic volume, [[EQUATION]], and weather data for Oslo, Norway....

Mixed-frequency data prediction tasks are pertinent in various application domains, in which one leverages progressively available high-frequency data to forecast/nowcast the low-frequency ones. Existing methods in the literature tailored to such tasks are mostly linear in nature; depending on...

The paper aims to enhance the accuracy of realized volatility prediction by introducing a novel Dual Empirical Mode Decomposition (DEMD) method that allows for the extraction of incremental information related to volatility prediction in raw financial data. The empirical results show that using...

Accurate forecasting of Gross Domestic Product (GDP) is crucial for policymakers, businesses, and investors. This research explores the use of SARIMAX, ARIMA, and Random Forest models to forecast GDP in the UK. The study investigates the relationship between GDP and the unemployment rate,...

We present a hierarchical architecture based on recurrent neural networks for predicting disaggregated inflation components of the Consumer Price Index (CPI). While the majority of existing research is focused on predicting headline inflation, many economic and financial institutions are...

Having information about an uncertain event is crucial for informed decision-making. This paper introduces a simple framework in which 1) a principal uses the reported beliefs of multiple agents to make a decision and 2) the agents reporting a belief are affected by the decision. Naturally, the...