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Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation

  • Estela Bee Dagum
  • Silvia Bianconcini

Part of the Statistics for Social and Behavioral Sciences book series (SSBS)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Estela Bee Dagum, Silvia Bianconcini
    Pages 1-28
  3. Estela Bee Dagum, Silvia Bianconcini
    Pages 29-57
  4. Seasonal Adjustment Methods

    1. Front Matter
      Pages 59-59
    2. Estela Bee Dagum, Silvia Bianconcini
      Pages 61-78
    3. Estela Bee Dagum, Silvia Bianconcini
      Pages 79-114
    4. Estela Bee Dagum, Silvia Bianconcini
      Pages 115-145
    5. Estela Bee Dagum, Silvia Bianconcini
      Pages 147-164
  5. Trend-Cycle Estimation

    1. Front Matter
      Pages 165-165
    2. Estela Bee Dagum, Silvia Bianconcini
      Pages 167-195
    3. Estela Bee Dagum, Silvia Bianconcini
      Pages 197-223
    4. Estela Bee Dagum, Silvia Bianconcini
      Pages 243-262
    5. Estela Bee Dagum, Silvia Bianconcini
      Pages 263-278
  6. Back Matter
    Pages 279-283

About this book

Introduction

This book explores widely used seasonal adjustment methods and recent developments in real time trend-cycle estimation. It discusses in detail the properties and limitations of X12ARIMA, TRAMO-SEATS and STAMP - the main seasonal adjustment methods used by statistical agencies.  Several real-world cases illustrate each method and real data examples can be followed throughout the text. The trend-cycle estimation is presented using nonparametric techniques based on moving averages, linear filters and reproducing kernel Hilbert spaces, taking recent advances into account. The book provides a systematical treatment of results that to date have been scattered throughout the literature.

Seasonal adjustment and real time trend-cycle prediction play an essential part at all levels of activity in modern economies. They are used by governments to counteract cyclical recessions, by central banks to control inflation, by decision makers for better modeling and planning and by hospitals, manufacturers, builders, transportation, and consumers in general to decide on appropriate action.

This book appeals to practitioners in government institutions, finance and business, macroeconomists, and other professionals who use economic data as well as academic researchers in time series analysis, seasonal adjustment methods, filtering and signal extraction. It is also useful for graduate and final-year undergraduate courses in econometrics and time series with a good understanding of linear regression and matrix algebra, as well as ARIMA modelling.

Keywords

62G08, 62M10, 62P20, 62P25 STAMP TRAMO-SEATS X12ARIMA real time trend-cycle prediction seasonal adjustment signal extraction time series reproducing kernel Hilbert space nonparametric linear filters Census Method II

Authors and affiliations

  • Estela Bee Dagum
    • 1
  • Silvia Bianconcini
    • 2
  1. 1.Department of Statistical SciencesUniversity of BolognaBolognaItaly
  2. 2.Department of Statistical SciencesUniversity of BolognaBolognaItaly

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-31822-6
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-31820-2
  • Online ISBN 978-3-319-31822-6
  • Series Print ISSN 2199-7357
  • Series Online ISSN 2199-7365
  • Buy this book on publisher's site