Economic Time Series: Modeling and Seasonality is a focused resource on analysis of economic time series as pertains to modeling and seasonality, presenting cutting-edge research that would otherwise be scattered throughout diverse peer-reviewed journals. This compilation of 21 chapters showcases the cross-fertilization between the fields of time s

"Most statistical bureaus do not apply uniform practices for the seasonal adjustment of related series and, consequently, the presence of inconsistencies may occur. This lack of uniformity results mainly from the fact that: (1) the seasonal adjustment method, the X-11-ARIMA, can be used in four different modes (each mode producing different current seasonally adjusted values of the same series); and (2) that most key economic and social indicators are large aggregates and the results differ whether they are seasonally adjusted directly or indirectly (through the aggregation of each of the seasonally adjusted components). Other problems faced currently by Statistics Canada and other statistical bureaus which will also be discussed in this paper are: the smoothing of highly irregular seasonally adjusted series, the estimation of trading day variation and the estimation of Easter effect"--Abstract.

In Time Series Analysis and Adjustment the authors explain how the last four decades have brought dramatic changes in the way researchers analyze economic and financial data on behalf of economic and financial institutions and provide statistics to whomsoever requires them. Such analysis has long involved what is known as econometrics, but time series analysis is a different approach driven more by data than economic theory and focused on modelling. An understanding of time series and the application and understanding of related time series adjustment procedures is essential in areas such as risk management, business cycle analysis, and forecasting. Dealing with economic data involves grappling with things like varying numbers of working and trading days in different months and movable national holidays. Special attention has to be given to such things. However, the main problem in time series analysis is randomness. In real-life, data patterns are usually unclear, and the challenge is to uncover hidden patterns in the data and then to generate accurate forecasts. The case studies in this book demonstrate that time series adjustment methods can be efficaciously applied and utilized, for both analysis and forecasting, but they must be used in the context of reasoned statistical and economic judgment. The authors believe this is the first published study to really deal with this issue of context.