Seasonality in economic time series can obscure movements of other components in a series that
are operationally more important for economic and econometric analyses. In practice one often
prefers to work with seasonally adjusted data to assess the current state of the economy and
its future course. This book presents a seasonal adjustment program called CAMPLET an acronym
of its tuning parameters which consists of a simple adaptive procedure to extract the seasonal
and the non-seasonal component from an observed series. Once this process is carried out there
will be no need to revise these components at a later stage when new observations become
available. The authors describe the main features of CAMPLET evaluate the outcomes of CAMPLET
and X-13ARIMA-SEATS in a controlled simulation framework using a variety of data generating
processes and illustrate CAMPLET and X-13ARIMA-SEATS with three time series: US non-farm
payroll employment operational income of Ahold and real GDP in the Netherlands. Furthermore
they show how CAMPLET performs under the COVID-19 crisis and its attractiveness in dealing
with daily data. This book appeals to scholars and students of econometrics and statistics
interested in the application of statistical methods for empirical economic modeling.
