Electricity price forecasting is a challenging task for decision-makers in deregulated power markets due to the inherent characteristics of electricity prices, e.g., high frequency and volatility. Accordingly, we propose a novel hybrid Deep Learning model to forecast one-step, two-step, and...

A principal hires an agent to work on a long-term project that culminates in a breakthrough or a breakdown. At each time, the agent privately chooses to work or shirk. Working increases the arrival rate of breakthroughs and decreases the arrival rate of breakdowns. To motivate the agent to work,...

This textbook introduces the mathematical models and algorithms utilised in machine learning, covering supervised and unsupervised learning as well as reinforcement learning: In supervised learning we present ensemble models, artificial neural networks, deep neural networks, recurrent neural...

Traditional stochastic mortality models tend to extrapolate, to focus on identifying trends in mortality without explaining them. Those that do link mortality with other variables usually limit themselves to GDP. This article presents a novel stochastic mortality model that incorporates a wide...

Forecasting CO2 emissions is a crucial topic because of the enormous impacts these emissions have on climate change and global warming and the considerable health risks they pose. This paper's primary goal is to assess the effectiveness of machine learning models as a unique method for...

By virtue of the stochastic nature of economic variables that exists today in any country, most nations are in a continuous attempt to increase their economic growth rates. Accurate foreign direct investment (FDI) inflows forecasting is certainly an inexpensive way to meet the aforementioned...

This study provides a novel attempt to evaluate whether an early warning system (EWS) for systemic banking crises can produce better predictions when political indicators are employed alongside traditional macrofinancial indicators. Based on a dataset covering 32 advanced economies for the...

It has been established in the economics literature that competitive outcomes can be well predicted by the aggregate salaries of the contesting participants. In this paper we use this idea to construct a forecast test to evaluate a novel, and hitherto unevaluated source of wage data. If the...

This study is the first to explore whether the well-known relationship between the clean energy sector, oil prices, and technology stocks can be leveraged to enhance the accuracy of realized volatility forecasts for individual clean energy sub-sectors. Based on intraday data and various...

This paper uses an augmented gravity model framework to investigate the historical impact of infectious diseases on international tourism and develops an out-of-sample prediction model. Using bilateral tourism flows among 38,184 pairs of countries during the period 1995–2017, I compare the...