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2 edition of On the seasonal adjustment of economic time series aggregates found in the catalog.

On the seasonal adjustment of economic time series aggregates

Estela Bee Dagum

On the seasonal adjustment of economic time series aggregates

a case study of the unemployment rate

by Estela Bee Dagum

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Published by National Commission on Employment and Unemployment Statistics : for sale by the Supt. of Docs., U.S. Govt. Print. Off. in Washington .
Written in English

    Subjects:
  • Unemployed -- United States -- Statistical methods,
  • Labor supply -- United States -- Statistical methods,
  • Set functions

  • Edition Notes

    StatementEstela B. Dagum
    SeriesBackground paper - National Commission on Employment and Unemployment Statistics ; no. 31
    ContributionsUnited States. National Commission on Employment and Unemployment Statistics
    The Physical Object
    Paginationvi, 46 p. ;
    Number of Pages46
    ID Numbers
    Open LibraryOL13602604M

    Stationarity and differencing. A stationary time series is one whose properties do not depend on the time at which the series is observed. 14 Thus, time series with trends, or with seasonality, are not stationary — the trend and seasonality will affect the value of the time series at different times. On the other hand, a white noise series is stationary — it does not matter when you. Time Series: Economic Forecasting Time-series forecasts are used in a wide range of economic activities, including setting monetary and fiscal policies, state and local budgeting, financial management,ments of economic forecasting include selecting the fore-castingmodel(s)appropriatefortheproblemathand,File Size: 72KB.

    Seasonal Adjustment of Time Series and Calendar Influence on Economic Activity 1 Central banks and statistical agencies, in addition to original statistics, publish seasonally and calendar. Historically, economists sought to understand the economic significance of macro fluctuations associated with seasons. During the s and s, the focus shifted to business cycles, and seasonal fluctuations were treated as noise that could be removed from data before by:

    tionary economic and business series. Their need sparked a return to empiricism, resulting in the development and generalization of exponential weighting for smoothing and forecasting. Suppose it is desired to measure the location at current time t of a nonstationary economic time series {Z1}. For this purpose, the first two postulates advanced. Seasonal adjustment or deseasonalization is a statistical method for removing the seasonal component of a time series. It is usually done when wanting to analyse the trend, and cyclical deviations from trend, of a time series independently of the seasonal components. It is normal to report seasonally adjusted data for unemployment rates to reveal the underlying trends and .


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On the seasonal adjustment of economic time series aggregates by Estela Bee Dagum Download PDF EPUB FB2

In the statistical literature, dealing with the analysis of economic time series, it is common practice to classify the types of movements that characterize a time series as trend, cyclical, seasonal, and irregular. Get this from a library. On the seasonal adjustment of economic time series aggregates: a case study of the unemployment rate.

[Estela Bee Dagum; United States. National Commission on Employment and Unemployment Statistics.]. On the seasonal adjustment of economic time series aggregates: a case study of the unemployment rate.

[Estela Bee Dagum; United States. National Commission on Employment and Unemployment Statistics.]. S EASONAL fluctuations in economic time series present problems for both the business analyst and the econometrician. In this paper we examine first the logical implications of certain simple consistency requirements that might reasonably be applied in appraising alternative procedures for seasonal adjust-ment.

Examination of actual economic time series indicates, that the optimal seasonal adjustment and aggregation of data provide a substantial improvement in the quality of sectorally disaggregated, Author: John Geweke.

Dagum, E.B. (), On the Seasonal Adjustment of economic Time Series Agggregates: A Case Study of the Unemployment Rate, Counting the Labor Force, National Commission on Employment and. ISSUES INVOLVED WITH THE SEASONAL ADJUSTMENT OF ECONOMIC TIME SERIES William R. Bell U.S. Bureau of the Census Washington, D.C.

This series contains research reports, written by or in cooperation with staff members of the Statistical Research Division, whose content may be of interest to the general statistical research community.

State industry employment time series published by the Current Employment Statistics State and Area program (CES State and Area) can exhibit regularly recurring seasonal movements.

Seasonal adjustment eliminates the part of the change attributable to the normal seasonal variation and makes it possible to observe the cyclical and other nonseasonal movements in CES State and Area series. the seasonal and calendar adjustment of daily time series, even though an increas-ing number of series with daily observations are available.

The aim of this paper is the development of a procedure to estimate and adjust for periodically recurring systematic e ects and the in uence of moving holidays in time series with daily observations.

To remove the residual seasonality in aggregate GDP, we applied a second seasonal adjustment to the BEA’s seasonally adjusted real GDP series. This second seasonal adjustment operates directly on data at an aggregate level and can be a useful supplement to the BEA’s bottom-up seasonal adjustment procedure that uses only disaggregated data.

A seasonal adjustment is a statistical technique designed to even out periodic swings in statistics or movements in supply and demand related to changing seasons.

It can, therefore, eliminate misleading seasonal components of an economic time series. Seasonal adjustment is a method of data-smoothing.

The changeover from the seasonal adjustment method Census X to Census XARIMA Since the beginning of the s the Deutsche Bundesbank has been using the Census X method developed by the U.S. Bureau of the Census to season-ally adjust time series.

It is now the most widely employed technique in the world. In the last few years, the Bureau. aggregate series can be calculated either directly (i.e. by performing the seasonal adjustment to the aggregated original series), or indirectly (i.e. by adding the season-ally adjusted series of the countries).

Currently, the indi-rect approach is applied in the EMU, and the choice of the seasonal adjustment method and the pre-treatment is left. seasonal adjustment was initially developed in the 's and 's as a tool for the analysis of seasonal economic time series in the absence of suitable statis-tical models for such series.

The methods were devel-oped empirically, using tools such as moving averages. Adequate models for seasonal series were not used until. ary approach to seasonal adjustment. In a first stage, seasonal adjustment should be applied to the most important aggregates of the QNA (such as the GDP).

For some time, these seasonally adjusted series may be used internally or published as experimental data. Next, seasonal adjustment could be expanded to the.

Direct seasonal adjustment is performed if all time series, including aggregates, are seasonally adjusted on an individual basis. Indirect seasonal adjustment is performed if the seasonally adjusted estimate for a time series is derived by combining the estimates for two or more directly adjusted series.

Comparing direct and indirect seasonal adjustments of aggregate series Catherine C. Hood and David F. Findley If a time series is a sum (or other composite) of component series that are seasonally adjusted, we can sum the seasonally adjusted component series to get an indirect adjustment for the aggregate series.

Finally, the book explores the crucial issue of quality assurance and the implications for public trust. This book is an essential reference for anybody interested in better understanding the important role that economic statistics play in our lives.

Understanding ECONOMIC STATISTICS AN OECD PERSPECTIVE Enrico Giovannini Understanding ECONOMIC File Size: 2MB. 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. The package 'Seasonal' facilitates seasonal adjustment in R. The R package provides an easy-to-handle wrapper around the XARIMA-SEATS Fortran libraries provided by the US Census Bureau.

XARIMA-SEATS is the state-of-the-art seasonal adjustment software produced, distributed, and maintained by the Census Bureau. The software permits extensive time series. Dagum, Estela Bee.

“ On the Seasonal Adjustment of Economic Time Series Aggregates: A Case Study of the Unemployment Rate,” in Data Collection, Processing and Presentation: National and Local. Appendix Vol. II, Counting the Labor Force.

National Commission on Employment and Unem­ ployment Statistics. Washington, D.C., If an aggregate time series is a sum (or other composite) of component series that are seasonally adjusted, then the sum of the adjusted component series provides a seasonal adjustment of the aggregate series that is called the indirect adjustment.precise magnitude of the seasonal adjustment is of very great im-portance.

For example, between the third and fourth quarters of seasonally adjusted GNP rose by $2 billion, then in the next quarter it fell $4 billion, marking the beginning of the recession. But the seasonal adjustment had eliminated a rise of nearly $15 billion be.