An ARIMA-Model-Based Approach to Seasonal Adjustment

Hillmer, Steven C.; Tiao, George C.

doi:10.1080/01621459.1982.10477767

Cited by 335 publications

(143 citation statements)

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“…In other words, if there exists an admissible decomposition, we need to determine what is called a canonical decomposition. A detailed examination of these two problems of admissible and canonical decompositions are outside the scope of this paper and has been solved elsewhere (Hillmer and Tiao 1982;Maravall and Planas 1999;Fiorentini and Planas 2001). In Sect.…”

Section: Principles Behind Seatsmentioning

confidence: 99%

On some remarks about SEATS signal extraction

Mélard

2015

SERIEs

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In seasonal adjustment a time series is considered as a juxtaposition of several components, the trend-cycle, and the seasonal and irregular components. The Bureau of the Census X-11 method, based on moving averages, correction of large errors and trading day adjustments, has long dominated. With the success of ARIMA modelling at the end of the 20th century, methods with better outlier detection and trading day corrections by regression with ARIMA errors have appeared, with the regARIMA module of Census X-12-ARIMA or Bank of Spain TRAMO-SEATS. SEATS consists of extracting the components by an ARIMA-model-based unobserved components approach. This means that models are used for each component such that the sum of the components is compatible with the ARIMA model for the corrected time series. The underlying theory of the SEATS program is studied in many papers but there is no complete and systematic description of its output. Our purpose is to examine SEATS text output and to explain the results in simple words and formulas. This is done on a simple example, a time series with a non-seasonal model so that the computations can be verified step by step. The principles behind SEATS are first described, including the admissible decompositions and the canonical decomposition, and the derivation of the Wiener-Kolmogorov filter. Then the example is introduced: the interest rates of US certificates of deposits. The text output from SEATS is presented in edited form in several tables. Finally, the main results are checked on the example by means of a Microsoft Excel workbook and direct computations. In particular, the forecasts and backcasts are obtained; the admissible and canonical decompositions with two components are discussed; the filters are first derived using autocorrelations of two auxiliary ARMA processes, then applied on the prolonged time series; and the characteristics of the estimates, the revisions and the growth rates are analyzed.

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Section: Principles Behind Seatsmentioning

confidence: 99%

On some remarks about SEATS signal extraction

Mélard

2015

SERIEs

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“…The implemented prediction methodology adopted the Autoregressive Integrated Moving Average ARI MA(p, d, q) paradigm [44], which defined by the following general-purpose forecast model:…”

Section: Predictionmentioning

confidence: 99%

Entropy-Based Economic Denial of Sustainability Detection

Monge

Vidal

Villalba

2017

Entropy

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In recent years, an important increase in the amount and impact of Distributed Denial of Service (DDoS) threats has been reported by the different information security organizations. They typically target the depletion of the computational resources of the victims, hence drastically harming their operational capabilities. Inspired by these methods, Economic Denial of Sustainability (EDoS) attacks pose a similar motivation, but adapted to Cloud computing environments, where the denial is achieved by damaging the economy of both suppliers and customers. Therefore, the most common EDoS approach is making the offered services unsustainable by exploiting their auto-scaling algorithms. In order to contribute to their mitigation, this paper introduces a novel EDoS detection method based on the study of entropy variations related with metrics taken into account when deciding auto-scaling actuations. Through the prediction and definition of adaptive thresholds, unexpected behaviors capable of fraudulently demand new resource hiring are distinguished. With the purpose of demonstrate the effectiveness of the proposal, an experimental scenario adapted to the singularities of the EDoS threats and the assumptions driven by their original definition is described in depth. The preliminary results proved high accuracy.

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“…Applying standard model selection criteria to estimates of (1 EDVHG RQ DQ LQLWLDO JXHVV IRU t C (we used the deviation in total employment growth from seasonal means and a time trend) provides us with a basis for deciding upon initial guesses for the orders r and m. The resulting specifications were used to estimate the complete model. We then respecified (7) ) as autoregressive processes differs from the canonical univariate specification for seasonal time series described in Hillmer and Tiao (1982) and Bell and Hillmer (1984). The canonical model assumes that the observed series is the sum of unobserved seasonal and non-seasonal components and imposes restrictions on these which imply that, at a minimum, the time series specification for the observed data contains a moving average (MA) term of order s. In our model, allowing the { it u } to follow MA processes greatly limits the extent to which we can reduce the dimensionality of the state-space model by prefiltering equations (1).…”

Section: Estimationmentioning

confidence: 99%

The cyclical sensitivity of seasonality in U.S. employment

Krane

Wascher

1999

Journal of Monetary Economics

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Fax: +41 61 / 280 91 00 and +41 61 / 280 81 00This publication is available on the BIS website (www.bis.org). There is a growing recognition in the literature on business cycles that production technologies may give rise to complicated interactions between seasonal and cyclical movements in economic time series, which can distort business cycle inference based on seasonally adjusted data. For the most part, however, the empirical research in this area has relied on standard univariate seasonal adjustment techniques that provide only a partial description of such interactions. In this paper, we develop an unobserved components model that explicitly accounts for the effects of business cycles on industry-level seasonality and for the potential feedback from seasonality to the aggregate business cycle. © Bank forIn particular, the model extracts an aggregate "common cycle" from industry-level data, allows formal statistical testing of seasonal differences in the comovement of an industry with the common cycle, and identifies economy-wide and industry-specific contributions to the seasonal and non-seasonal variation in the data. Applying the model to quarterly US payroll employment data, we frequently find evidence of statistically significant differences across seasons in the comovement between sectoral employment and the common cycle.On the other hand, we also find that seasonal fluctuations in employment at the industry level are largely idiosyncratic and that the proportion of the total variance of the common cycle accounted for by seasonality is much less than for aggregate employment. This suggests that seasonal shocks may have less of a business cycle element to them than one might infer from the seasonal movements in aggregate variables.

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An ARIMA-Model-Based Approach to Seasonal Adjustment

Cited by 335 publications

References 10 publications

On some remarks about SEATS signal extraction

On some remarks about SEATS signal extraction

Entropy-Based Economic Denial of Sustainability Detection

The cyclical sensitivity of seasonality in U.S. employment

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