Household indebtedness in the European Union countries: Going beyond the mainstream interpretation

This paper develops a panel data econometric analysis in order to determine the main macroeconomic causes of household indebtedness in the European Union countries from 1995 to 2019. During that time, household indebtedness reached unprecedented and unsustainable levels, which played a crucial role in the emergence of the last financial and economic crisis. This is not clearly well interpreted by the mainstream economics, which advocates that household indebtedness is just an instrument to smooth consumption in a continuous process of utility maximization over life. This paper estimates a model according to which the household indebtedness depends on seven mainstream and non-mainstream macroeconomic causes, namely housing prices, financial asset prices, personal income inequality, the households’ labour income, welfare state expenditures, the working-age population and interest rates. This paper finds that housing prices, welfare state expenditures and interest rates impact positively on household indebtedness in the European Union countries, whilst the financial asset prices, personal income inequality and households’ labour income impact negatively on household indebtedness in the European Union countries. This paper also finds that the fall of household labour income and the rise of the housing prices have been the main triggers of household indebtedness in the European Union countries since 1995.info:eu-repo/semantics/publishedVersio

Correspondence: The One Health concept applied to dirofilariasis-a zoonotic disease

n/

Agent based demand flexibility management for wind power forecasting error mitigation using the SG-BEMS framework

The integration process of renewable energy sources (RES) and distributed energy resources (DER) into the power system, is characterized by concerns that originate from their stochastic and uncontrollable nature. This means that system operators require reliable forecasting tools, in order to ensure efficient and reliable operation. Accordingly, this paper proposes the use of demand flexibility, to counteract the RES forecasting errors. For this purpose, distributed and decentralized intelligence is used, via the SG-BEMS framework, to invoke demand flexibility in a timely and effective fashion, while taking into account the negative effects on the building occupants comfort. Lastly, numerical results from a simulated case of study are presented, which confirm that demand flexibility can be used to mitigate the magnitude of forecast errors