New methods for seasonal adjustment

Seasonal adjustment aims to remove predictable seasonal fluctuations and other calendar-related variations from a time series. In the context of economics Hylleberg (1992) defines seasonality as ``[...] the systematic, although not necessarily regular, intra-year movement caused by the changes of the weather, the calendar, and timing of decisions, directly or indirectly through the production and consumption decisions made by the agents of the economy''. The removal of seasonal and calendar effects allows analysts, economists, and statisticians to more clearly identify the business cycle and other non-seasonal effects, such as strikes or crises. By adjusting for seasonality, it becomes easier to compare data across different time periods and to make more timely economic assessments and policy decisions. The usual alternative, year-on-year growth-rates, show underlying developments with a time lag of about half a year (cf. Dagum and Mazzi 2018). Moreover, these growth rates may be skewed by differences in the calendar constellation, for instance a discrepancy in the number of working days between the periods under comparison. To address these issues, seasonal adjustment techniques are employed to provide a more immediate and undistorted view of economic developments. This dissertation adds to the literature on seasonal adjustment and time series econometrics by introducing contributions in two primary areas. Historically, the development and application of seasonal adjustment methods have predominantly focused on time series data with lower frequencies, such as monthly and quarterly observations. This work, however, develops and analyses methods to seasonally adjust higher frequency time series. Most prominently, a procedure is developed to adjust daily time series. The second area of contribution lies in the analysis and enhancement of diagnostic tools that are instrumental in the seasonal adjustment process for both lower and higher frequency time series. This includes the examination and refinement of seasonality tests, which are pivotal in detecting seasonal patterns within the data, as well as unit root tests, which are relevant in many areas of econometrics, and essential for the RegARIMA model based estimation of calendar effects. Collectively, these methodological advancements and diagnostic improvements facilitate more accurate seasonal adjustments.