Technology forecasting is an important and critical issue that determines the starting point of planning and is considered as a management tool directly related to the future. In the previous research items, the development of renewable energy technologies was of concern. Moreover, due to the...

This research paper explores the use of the Python programming language in analyzing the stock market. The paper explains the various techniques used in the analysis of the stock market and their limitations. It outlines the various features and capabilities of Python that make it suitable for...

An updated version of our Markov-switching model of U.S. real GDP clearly suggests the COVID-19 recession was more U shaped than L shaped. As with linear time series models, it is important to account for extreme outliers during the pandemic, but a simple decay function for volatility from...

Supply Chain Financing (SCF) is increasingly utilised as an effective method for optimising cash flows in supply networks. With increased popularity several financial institutions have begun offering SCF to businesses. However various recent scandals have highlighted inefficiencies in the...

We show that classical insurance models based on some compound distributions can well predict information leakage by cyber incidents with reducing the computational cost thanks to the model’s simplicity. We use the negative binomial distribution, a renewal process and a Hawkes process as the...

Economic uncertainty is a powerful force in the modern economy. Research shows that surges in uncertainty can trigger business cycles, bank runs and asset price fluctuations. But where do sudden surges in uncertainty come from? This paper provides a data-disciplined theory of belief formation...

Interest in regional economic issues coupled with advances in administrative data is driving the creation of new regional economic data. Many of these data series could be useful for nowcasting regional economic activity, but they suffer from a short (albeit constantly expanding) time series...

Forecast combinations, also known as ensemble models, routinely require practitioners to select a model from a massive number of potential candidates. Ten explanatory variables can be grouped into 21078 forecast combinations, and the number of possibilities increases further to 21078+2^1078if we...

Poverty prediction models are used by economists to address missing data issues in a variety of contexts such as poverty profiling, targeting with proxy-means tests, cross-survey imputations such as poverty mapping, or vulnerability analyses. Based on the models used by this literature, this...

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...