This book explores widely used seasonal adjustment methods and recent
developments in real time trend-cycle estimation. It discusses in detail
the properties and limitations of X12ARIMA, TRAMO-SEATS and STAMP - the
main seasonal adjustment methods used by statistical agencies. Several
real-world cases illustrate each method and real data examples can be
followed throughout the text. The trend-cycle estimation is presented
using nonparametric techniques based on moving averages, linear filters
and reproducing kernel Hilbert spaces, taking recent advances into
account. The book provides a systematical treatment of results that to
date have been scattered throughout the literature.
Seasonal adjustment and real time trend-cycle prediction play an
essential part at all levels of activity in modern economies. They are
used by governments to counteract cyclical recessions, by central banks
to control inflation, by decision makers for better modeling and
planning and by hospitals, manufacturers, builders, transportation, and
consumers in general to decide on appropriate action.
This book appeals to practitioners in government institutions, finance
and business, macroeconomists, and other professionals who use economic
data as well as academic researchers in time series analysis, seasonal
adjustment methods, filtering and signal extraction. It is also useful
for graduate and final-year undergraduate courses in econometrics and
time series with a good understanding of linear regression and matrix
algebra, as well as ARIMA modelling.
