Publications by Ekaterina Krymova

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The time-varying parameters vector autoregression models with exogenous variables (TVP-VARX) have become an indispensable tool for modeling time-varying relationships between macroeconomic indicators. At the same time, TVP-VARX models are often outperforme ...

Since the beginning of the COVID-19 pandemic, many dashboards have emerged as useful tools to monitor its evolution, inform the public, and assist governments in decision-making. Here, we present a globally applicable method, integrated in a daily updated ...

In today's rapidly evolving society, the sources of atmospheric particulate matter (PM) emissions are shifting significantly. Stringent regulations on vehicle tailpipe emissions, in combination with a lack of control of non-exhaust vehicular emissions, hav ...

Background During the COVID-19 pandemic there has been a strong interest in forecasts of the short-term development of epidemiological indicators to inform decision makers. In this study we evaluate probabilistic real-time predictions of confirmed cases an ...

The European Green Deal is prescribing a very ambitious rise in the annual production of renewable energy sources, and grid regulation services are increasingly important in the course of a massive integration of intermittent renewable energy sources. Alth ...

As the global shift towards renewable energy accelerates, achieving stability in power systems is crucial. Hydropower accounts for approximately 17% of energy produced worldwide, and with its capacity for active and reactive power regulation, is well-suite ...

Ekaterina Krymova

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Bayesian TVP-VARX models with time invariant long-run multipliers

Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide

High-resolution modelling of particulate matter chemical composition over Europe: brake wear pollution

National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021

On the modelling of the fatigue-induced damage in Francis turbines start-up sequences

Fatigue damage reduction in hydropower startups with machine learning