Case studies

This document aims to introduce users to the main features of JDemetra+ enabling them to take advantage of this program and understand the output from the sequential steps in the analysis. The document includes step-by-step descriptions of how to perform a typical analysis and useful tips that facilitate replication of the results with the user’s own data and working instructions. The Case Studies section does not cover all available JDemetra+ functionalities. These are included in the Reference Manual and the user should refer to it for additional information.

It is assumed that the reader is familiar with concepts, such as time series, trend-cycle, seasonality, descriptive statistics, confidence levels, mean square error, estimate, estimator, linear regression, stationarity, ARIMA process and so on. Readers with insufficient background to follow this document are encouraged to refer to an appropriate textbook, e.g. Chatfield (2004).

JDemetra+ uses the notation “X12”, “X13”,”Arima”, “RegArima” and “TramoSeats” instead of “X-12-ARIMA”, “X-13ARIMA-SEATS”, “ARIMA”, “RegARIMA” and “TRAMO-SEATS” respectively. This notation is also used in the Case Studies when references to the user interface are made.

Who should use this document?

With JDemetra+, which is an extremely user-friendly software, pre-adjustment and decomposition of a time series can be performed easily by users who have absolutely no knowledge of seasonal adjustment theory. Although the output is easy to produce, its analysis, interpretation, readjustment and validation requires certain knowledge and experience. Therefore the JDemetra+ User Guide is designed for two types of users: beginners, who have only a basic knowledge of seasonality and its estimation in time series, and advanced users, who already perform seasonal adjustment and are able to interpret the outcomes, at least at a basic level.

Case Studies section neither describes in detail how the seasonal adjustment methods work, nor the underlying mathematics. For readers interested in seasonal adjustment methods a brief sketch of the X-13ARIMA-SEATS and TRAMO-SEATS algorithms and concepts are included in the Reference Manual. For those interested in a more detailed discussion of seasonal adjustment you are encouraged to refer to the References section.

How the document is organized

The Case Studies section includes a set of scenarios. They focus on various topics, such as:

How to use this document

With JDemetra+ seasonal adjustment can be performed in several ways. Additional functionalities designed for time series analysis, not necessarily strictly related to seasonal adjustment, are also provided.

The Case Studies section offers several typical paths that can be followed to perform analyses in an efficient way. The scenarios are designed to account for different user aims as well as common constraints, such as the time allocated to perform the task, the size of the dataset and the user’s experience and skill in seasonal adjustment. Each scenario includes sequential phases of the analysis, e.g. preparing the source data, analysis of the results, readjustment of the parameters and regular data production. Therefore, it is recommended to study each scenario from the beginning to the end.

The scenarios on seasonal adjustment consist of two simple scenarios for beginners and users with limited time for performing seasonal adjustment and several scenarios of more detailed seasonal adjustment for experienced users. The simple and detailed scenarios include examples of single time series and multiple time series analysis.

There are two time series modelling scenarios, one basic and one advanced that provide examples of the analysis time characteristics. The scenario for advanced users provides a more detailed analysis that includes identification and estimation of outliers, calendar effects, interpolation of missing values and forecasting. The basic scenario is a limited version of the advanced analysis, focused mostly on automatic detection of outliers and calendar effects.

A scenario on seasonality tests is for all types of users and explains how to test for the presence of seasonal movements in time series. The presence of seasonality should be checked for each time series in a dataset. The tests for seasonality are integral to the seasonal adjustment procedures available in JDemetra+. However, the tests can also be run independently of seasonal adjustment. The scenario on seasonality tests serves this purpose. As it is designed for analysis of a single time series, it is usually run as part of the detailed analysis of the most important series. For example, it can be used for checking the presence of seasonal movements to decide if a series should be seasonally adjusted or for regular monitoring of seasonality in time series.

The spectral graphs scenario is for advanced users and introduces an in-depth analysis of a time series in the frequency domain.

The calendars scenarios explain how to define country-specific holidays and include them into a national calendar. They also deal with more sophisticated types of calendars and explain how to import them.