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2023 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

Seasonal Adjustment Without Revisions

A Real-Time Approach

verfasst von: Barend Abeln, Jan P. A. M. Jacobs

Verlag: Springer International Publishing

Buchreihe : SpringerBriefs in Economics

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
Seasonality, which Hylleberg (1986, p. 23) defines as ‘the systematic, although not necessarily regular or unchanging, intrayear movement that is caused by climatic changes, timing of religious festivals, business practices, and expectations’, is often considered a nuisance in economic modeling. 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 this book, we present a novel, alternative 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. We will show among other things that differences between X13ARIMA-SEATS, the industry standard, and CAMPLET are fairly small. Therefore, the disadvantages associated with having to update seasonal adjustments and seasonal factors when new observations become available might be larger than the benefits of using optimal seasonal adjustments.
Barend Abeln, Jan P. A. M. Jacobs
Chapter 2. CAMPLET: Seasonal Adjustment Without Revisions
Abstract
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 chapter 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 paper describes the main features of CAMPLET. We 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: U.S. non-farm payroll employment, operational income of Ahold, and real GDP in the Netherlands.
Barend Abeln, Jan P. A. M. Jacobs
Chapter 3. Seasonal Adjustment of Economic Tendency Survey Data
Abstract
Economic tendency survey data are not revised when new observations become available and routinely adjusted for seasonal effects. Standard methods like Census X12 produce revisions when new observations become available. In this chapter, we investigate the variables of the KOF Economic Barometer, the majority of which are based on economic tendency surveys. We compare seasonally adjusted values generated by the Census X12 method KOF applies and outcomes of CAMPLET, which do not result in revisions when new observations become available, both for individual variables and in terms of the KOF Barometer.
Barend Abeln, Jan P. A. M. Jacobs
Chapter 4. Residual Seasonality: A Comparison of X13 and CAMPLET
Abstract
We compare residual seasonality properties for the series of U.S. real GDP. We download the raw series and the seasonally adjusted version, which is produced by X13ARIMA-SEATS, from FRED, the database of the St. Louis Federal Reserve bank, and calculate CAMPLET seasonal adjustments. In the second round, we seasonally adjust the X13 and CAMPLET seasonal adjustments again. We show graphs of unadjusted and first-round seasonal adjustments and compare unadjusted and all six seasonally adjusted series on the basis a selection of seasonality measures including our own Measure of Seasonality for the last eight years of the sample. Our empirical analyses confirm the strength of CAMPLET in seasonal adjustment. First-round and second-round seasonal adjustments of X13 and CAMPLET are similar. In addition, we do not find evidence of residual seasonality in U.S. real GDP.
Barend Abeln, Jan P. A. M. Jacobs
Chapter 5. COVID-19 and Seasonal Adjustment
Abstract
In this chapter, we study the impact of COVID-19 on seasonal adjustment. We focus on whether special adjustments are required to treat the COVID-19 crisis as an outlier as suggested by Eurostat in the application of three seasonal adjustment procedures: X-13ARIMA-SEATS, the industry standard, STL (Seasonal-Trend decomposition based on Loess), and a new method CAMPLET, an acronym of its tuning parameters. In addition, we investigate whether revisions occur. We show results of seasonal adjustments for the quarterly series real GDP in the Netherlands and for the weekly series U.S. Initial Claims. Seasonal adjustment with X-13ARIMA-SEATS and CAMPLET requires modifications in the implementation of the standard procedure to treat the COVID-19 crisis as an outlier; STL can be applied straightforwardly. Differences in seasonally adjusted values are generally small around COVID-19. Finally, X-13ARIMA-SEATS and STL seasonal adjustments are subject to revision, which probably will lead to the COVID-19 crisis becoming less deep when new observations become available.
Barend Abeln, Jan P. A. M. Jacobs
Chapter 6. Seasonal Adjustment of Daily Data with CAMPLET
Abstract
In the last decade, large data sets have become available, both in terms of the number of time series and with higher frequencies (weekly, daily, and even higher). All series may suffer from seasonality, which hides other important fluctuations. Therefore, time series are typically seasonally adjusted. However, standard seasonal adjustment methods cannot handle series with higher than monthly frequencies. Recently, Abeln et al. (2019) presented CAMPLET, a new seasonal adjustment method, which does not produce revisions when new observations become available. The aim of this chapter is to show the attractiveness of CAMPLET for seasonal adjustment of daily time series. We apply CAMPLET to daily data on the gas system in the Netherlands.
Barend Abeln, Jan P. A. M. Jacobs
Chapter 7. Conclusion
Abstract
This book presented a new 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 is no need to revise these components at a later stage when new observations become available. We have shown among other things that differences between X-13ARIMASEATS, the industry standard, and CAMPLET are fairly small. Therefore, the disadvantages associated with having to update seasonal adjustments and seasonal factors when new observations become available might be larger than the benefits of using optimal seasonal adjustments.
Barend Abeln, Jan P. A. M. Jacobs
Backmatter
Metadaten
Titel
Seasonal Adjustment Without Revisions
verfasst von
Barend Abeln
Jan P. A. M. Jacobs
Copyright-Jahr
2023
Electronic ISBN
978-3-031-22845-2
Print ISBN
978-3-031-22844-5
DOI
https://doi.org/10.1007/978-3-031-22845-2