The COVID-19 outbreak in 2020 has fostered in many countries the development of new weekly economic indices for the timely tracking of pandemic-related turmoils and other forms of rapid economic changes. Such indices often utilise information from daily and weekly economic time series that...

This paper examines the use of machine learning methods in modeling and forecasting time series with long memory through GARMA. By employing rigorous model selection criteria through simulation study, we find that the hybrid GARMA-LSTM model outperforms traditional approaches in forecasting...

This paper evaluates the transition from X12-ARIMA to X13-ARIMA-SEATS for the seasonal adjustment of Armenia's quarterly national accounts (QNA). We analyze the methodological advancements and their impact on key economic indicators, focusing on the precision and reliability of seasonally...

Applications of high-frequency data, including energy management, economics, and finance, frequently require time-series forecasting characterized by complex seasonality. Recognizing prevailing seasonal trends continues to be difficult, given that the majority of solutions depend on basic...

The establishment of common guidelines for seasonal adjustment within the European Statistical System is an essential step towards a better harmonisation and comparability of infra-annual statistics. Following the 2009 and 2015 editions which were widely accepted and implemented, the 2024...

Hamilton (2018) argues that one should never use the Hodrick-Prescott (HP) filter to detrend economic time series and proposes an alternative approach. This comment reconsiders Hamilton's case against the HP filter, emphasizing two simple points. First, in the empirical example Hamilton...