Seasonal Adjustment Methods And Real Time Trend-cycle Estimation (statistics For Social And Behavioral Sciences)
by Estela Bee Dagum /
2016 / English / PDF
4.5 MB Download
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.
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.
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.
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.
