Trend-Cycle-Seasonal Interactions: Identification and Estimation

Abstract

Economists typically use seasonally adjusted data in which the assumption is imposed that seasonality is uncorrelated with trend and cycle. The importance of this assumption has been highlighted by the Great Recession. The paper examines an unobserved components model that permits non-zero correlations between seasonal and nonseasonal shocks. Identification conditions for estimation of the parameters are discussed from the perspectives of both analytical and simulation results. Applications to UK household consumption expenditures and US employment reject the zero correlation restrictions and also show that the correlation assumptions imposed have important implications about the evolution of the trend and cycle in the post-Great Recession period.

A Review of Some Recent Developments in the Modelling and Seasonal Adjustment of Infra-Monthly Time Series

“…McElroy and Monsell, 2017) may also pave the way for the addition of correlated latent components (e.g. Hindrayanto, Jacobs, Osborn, and Tian, 2019;McElroy and Maravall, 2014) or GARCH-type heteroskedasticity (e.g. Koopman et al, 2007).…”

CAMPLET: Seasonal Adjustment Without Revisions

“…CAMPLET might also be employed to seasonally adjust Chinese economic statistics, which suffer from moving holidays due to the Chinese New Year (Roberts and White, 2015). Other applications are the seasonal adjustment of short (volatile) series or series in which seasonality is correlated with trend and cycle (Hindrayanto et al, 2018).…”