Download e-book for iPad: Seasonal Adjustment Methods and Real Time Trend-Cycle by Estela Bee Dagum, Silvia Bianconcini

By Estela Bee Dagum, Silvia Bianconcini

ISBN-10: 3319318209

ISBN-13: 9783319318202

ISBN-10: 3319318225

ISBN-13: 9783319318226

This ebook explores popular seasonal adjustment equipment and up to date advancements in actual time trend-cycle estimation. It discusses intimately the homes and boundaries of X12ARIMA, TRAMO-SEATS and STAMP - the most seasonal adjustment tools utilized by statistical companies. numerous real-world situations illustrate each one procedure and genuine info examples should be during the textual content. The trend-cycle estimation is gifted utilizing nonparametric options in response to relocating averages, linear filters and reproducing kernel Hilbert areas, taking contemporary advances into consideration. The ebook presents a systematical therapy of effects that thus far were scattered through the literature.

Seasonal adjustment and actual time trend-cycle prediction play a vital half in any respect degrees of job in glossy economies. they're utilized by governments to counteract cyclical recessions, via critical banks to manage inflation, by means of selection makers for larger modeling and making plans and by way of hospitals, brands, developers, transportation, and shoppers normally to settle on applicable action.

This publication appeals to practitioners in executive associations, finance and company, macroeconomists, and different execs who use monetary facts in addition to educational researchers in time sequence research, seasonal adjustment tools, filtering and sign extraction. it's also helpful for graduate and final-year undergraduate classes in econometrics and time sequence with a superb figuring out of linear regression and matrix algebra, in addition to ARIMA modelling.

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Extra resources for Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation

Example text

25 consecutive days, yearly series cannot contain seasonality. Flow series can be affected by other variations associated with the composition of the calendar. The most important are the trading day variations, which are due to the fact that the activity in some days of the week is more important than others. Months with five of the most important days register an excess of activity (ceteris paribus) in comparison to months with four such days. Conversely, months with five of the least important days register a short-fall of activity.

The X-11 Variant of the Census Method II Seasonal Adjustment Program. Technical Paper 15 (revised). US Department of Commerce, Bureau of the Census, Washington, DC. 52. Tiao, G. , & Tsay, R. S. (1983). Consistency properties of least squares estimates of autoregressive parameters in Arma models. The Annals of Statistics, 11, 856–871. 53. Tsay, R. S. (1984). Regression models with time series errors. Journal of the American Statistical Association, 79, 118–124. 54. Young, A. H. (1968). A linear approximation to the Census and BLS seasonal adjustment methods.

Over the past 15 years, however, economists have increasingly applied tractable parametric nonlinear time series models to business cycle data; most prominent in this set of models are the classes of threshold autoregressive (TAR) models, Markov switching autoregressive (MSAR), and smooth transition autoregressive (STAR) models. The basic approach to the analysis of current economic conditions (known as recession and recovery analysis, see [36]) is that of assessing the short-term trend of major economic indicators (leading, coincident, and lagging) using percentage changes, based on seasonally adjusted units and calculated for months and quarters in chronological sequence.

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Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation by Estela Bee Dagum, Silvia Bianconcini


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