From noise to turning points: a new framework for seasonal adjustment in Armenia

Deskripsi

Timely identification of turning points in key macroeconomic variables like quarterly national accounts requires the removal of seasonal and calendar effects from time series data. This paper looks at the use of autoregressive integrated moving average (ARIMA) approaches. It evaluates Armenia’s transition from the X12-ARIMA to the X13-ARIMA-SEATS framework, which includes the signal extraction in ARIMA time series (SEATS) method. The authors analyze the methodological advancements and their impact, focusing on the precision and reliability of seasonally adjusted data.