X-13A-S 프로그램을 이용한 계절조정방법 분석 - X-12 필터와 SEATS 방법의 비교 -

Seasonal Adjustment of Monetary Aggregates and Loans Series at the Banque de France: Theoretical Background and Implementation (La Désaisonnalisation des Séries D’Agrégats Monétaires et de Crédit à la Banque de France: Aspects Théoriques et Mise en Oeuvre) (French)

This paper investigates the differences between directly and indirectly seasonally adjusted aggregates. This difference is derived analytically for linear seasonal adjustment methods. GDP data for five European countries and three classes of seasonal adjustment methods are used to show empirically the differences between both approaches. For this purpose cointegration methods and cross-spectral analysis are applied. The analysis shows that there are no differences in the long-run components of directly and indirectly adjusted aggregates, whereas differences in the short-run components depend strongly on the seasonal adjustment methods applied in the indirect and direct approaches.

Time series of different nature might be characterised by the presence of deterministic and/or stochastic seasonal patterns. By seasonality, we refer to periodic fluctuations affecting not only the mean but also the shape, the dispersion and in general the density of the variable of interest over time. Using traditional approaches for seasonal adjustment might not be efficient because they do not ensure, for instance, that the adjusted data are free from periodic behaviours in, say, higher-order moments. We introduce a seasonal adjustment method based on quantile regression that is capable of capturing different forms of deterministic and/or stochastic seasonal patterns. Given a variable of interest, by describing its seasonal behaviour over an approximation of the entire conditional distribution, we are capable of removing seasonal patterns affecting the mean and/or the variance, or seasonal patterns varying over quantiles of the conditional distribution. In the first part of this work, we provide a proposed approach to deal with the deterministic seasonal pattern cases. We provide empirical examples based on simulated and real data where we compare our proposal to least-squares approaches. The results are in favour of the proposed approach in case if the seasonal patterns change across quantiles. In the second part of this work, we improve the proposed approach flexibly to account for the essential effect of the structural breaks in the time series. Again, we compare the proposed methods to segmented-least squares and provide several empirical examples based on simulated and real data that are affected by both the structural breaks and seasonal patterns. The results, in case of stochastic periodic behaviour, are in favour of the proposed approaches especially when the seasonal patterns change across quantiles.

Hebei Province is one of China’s high-incidence regions for hemorrhagic fever with renal syndrome (HFRS), posing significant public health challenges. This study aims to evaluate the efficacy of key Hemorrhagic Fever with Renal Syndrome (HFRS) control measures. This study analyzed annual incidence data (1981–2024) and monthly data (2004–2024) using interrupted time series (ITS) analysis and the X-12 seasonal adjustment method. The X-12 procedure decomposes the series into trend, seasonal, and irregular components and removes seasonal variation to produce seasonally adjusted data. Specifically, we assessed the impact of two interventions: the 2002 Hebei Epidemic Hemorrhagic Fever Prevention and Control Plan and the 2008 Vaccination Work Plan for Key HFRS-endemic Counties in Hebei Province. The results demonstrated that before 2002, the HFRS incidence rate increased significantly at 28.8% annually (β₁ = 0.25, p < 0.01). After policy implementation, the trend reversed, with an annual decline of 32.0% (β₁ + β₃ = −0.117, p < 0.05), indicating effective control. Notably, epidemic peaks temporarily elevated incidence levels (β₄ = 0.83, p < 0.01). Following the 2008 vaccination program, short-term incidence rate reductions of 1.229 cases per 100,000 (β₂ = -1.229, p = 0.044) and 0.351 cases per 100,000 (β₂ = -0.351, p = 0.10) were observed in Qinhuangdao and Tangshan, respectively, though the latter was not statistically significant. Long-term trends revealed slower monthly declines of 0.003 cases per 100,000 in Qinhuangdao (β₁ + β₃ = −0.003, p = 0.049) and 0.002 cases per 100,000 in Tangshan (β₁ + β₃ = −0.002, p = 0.019). These findings highlight the differential effects of policy interventions on HFRS incidence, with the 2002 plan achieving sustained reduction, while the 2008 vaccination program showed limited long-term impact. Not applicable.

This paper offers an empirical appraisal of nine seasonal adjustment methods on the basis of seven criteria (percentage change, orthogonality, idempotency, two tests on residuals, stability, estimation accuracy of known seasonals).

This paper examines the deseasonalization of time series in Romania, with a focus on its application in analyzing quarterly GDP data. Seasonal adjustment is a vital process that ensures consistency and comparability of economic indicators across quarters and years. By removing predictable seasonal variations, deseasonalization allows analysts to identify trends, turning points, and sectoral comparisons, facilitating robust economic evaluations. Romania’s adoption of JDemetra+ software and the TRAMO-SEATS methodology since 2012 reflects the nation’s commitment to aligning with the European System of Accounts (ESA 2010) standards. The challenges associated with data revisions, structural breaks, and outliers are addressed within this process. The findings highlight the effectiveness of deseasonalization in supporting accurate policymaking and economic analysis

Seasonal-adjustment procedures based on regression methods applied to a mixed additive-multiplicative model are described. The procedures are based on the traditional model of trend, seasonal and irregular components but instead of assuming that the seasonal is purely additive or purely multiplicative as in the usual methods of seasonal adjustment they permit the use of a mixture of additive and multiplicative components. Tests are developed which are intended to investigate whether a purely additive, a purely multiplicative or a mixed additive-multiplicative model is required by the data. For the general case where both additive and multiplicative components are required, the seasonal component is assumed to consist of a seasonal pattern and a seasonal amplitude which are estimated separately. The trend is estimated using moving-average filters and the seasonal component is fitted by means of a stepwise regression method applied to additive and multiplicative Fourier components. Two main computer programs have been developed, the first of which tests whether a purely additive, a purely multiplicative or a mixed additive-multiplicative seasonal model is required; the second estimates the seasonal component and produces a seasonally adjusted series. The methods are applied to a number of unemployment series for several countries.

Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China

Seasonal adjustment procedures attempt to estimate the sample realizations of an unobservable economic time series in the presence of both seasonal and irregular factors. In this article, we consider a factor that has not been considered explicitly in previous treatments of seasonal adjustment: measurement error. Because of the sample design used in the Current Population Survey, measurement error will not be a white-noise process, but instead it will be characterized by serial correlation of a known form. We first consider what effect the serially correlated measurement error has on estimation of the nonseasonal component in seasonal adjustment models. We also consider the effect of measurement error on the widely used seasonal adjustment process X-11. Estimated unobserved-components models are used to estimate the precision (root mean squared error) of the official and optimal seasonally adjusted data.

This note describes and illustrates the seasonal adjustment method proposed by Ekkehart Schlicht and Ralf Pauly (1983) in the interactive computable document format (CDF). It requires the free CDF player available at https://www.wolfram.com/player/. It can also be opened and edited by the Wolfram Mathematica program https://www.wolfram.com/mathematica/.

ABSTRACTThis report investigates whether deregulation of the Australian wheat export market induced a structural change in the price data generation process. We analyze the unit root properties of Western Australian wheat price series by testing for the possibility of single and double structural breaks. Daily spot prices for the period of May 20, 2003, to September 14, 2010 are used. We find the wheat price series has a unit root with two structural breaks but neither breaks coincides with the time when the Wheat Export Marketing Act 2008 came into effect on July 1, 2008. The implication of our results is that deregulation was not the main cause of structural breaks in the price series in the sample period.

Structural Breaks and Regional Disparities in the Transmission of Monetary Policy

Tourism industry development becomes one of the key strategies for economic development of many countries during the few decades. This study intends to predict monthly tourist arrivals in Sri Lanka for the period of six years from 2015 January to 2020 December using Time series forecasting simulation method. For the development of the model 516 monthly observations are made after adjusted for seasonality using X-13ARIMA-SEATS monthly seasonal adjustment Method. The study uses 36 alternative ARIMA models as simulation to find the best model to predict monthly tourist arrivals in Sri Lanka. The Akaike Information Criterion (AIC) is used as selection criteria for the models which find that the ARIMA (5, 1, 3) as the best model among the alternative models. According to the model, the expected tourist arrivals in the month of December 2020 will be 178, 705 tourists in Sri Lanka.

The aim of this study was to assess the accuracy of different methods of forecasting malaria incidence from historical morbidity patterns in areas with unstable transmission. We tested five methods using incidence data reported from health facilities in 20 areas in central and north-western Ethiopia. The accuracy of each method was determined by calculating errors resulting from the difference between observed incidence and corresponding forecasts obtained for prediction intervals of up to 12 months. Simple seasonal adjustment methods outperformed a statistically more advanced autoregressive integrated moving average method. In particular, a seasonal adjustment method that uses mean deviation of the last three observations from expected seasonal values consistently produced the best forecasts. Using 3 years' observation to generate forecasts with this method gave lower errors than shorter or longer periods. Incidence during the rainy months of June-August was the most predictable with this method. Forecasts for the normally dry months, particularly December-February, were less accurate. The study shows the limitations of forecasting incidence from historical morbidity patterns alone, and indicates the need for improved epidemic early warning by incorporating external predictors such as meteorological factors.

Based on the monthly social fixed asset investment data of Shanghai from January 2003 to February 2020, this paper uses the SARIMA model and the X_12_ARIMA seasonal adjustment method to make predictions. The predicted value is compared with the actual value. Based on relative error, the predicted value based on the SARIMA model is more accurate and reasonable than the predicted value based on the X-12-ARIMA seasonal adjustment method. SARIMA (2, 1, 1) (0, 1, 1)12 can be used for future forecasts and can provide a basis for future fixed asset investment in Shanghai.KeywordsSARIMAX_12_ARIMA seasonal adjustmentTotal investment in fixed assetsTime series forecast