Seasonal Adjustment

This paper examines the use and environmental consequences of engineered structures on reef platform islands in the Maldives. The Maldives comprises 1 500 islands which are mostly low-lying sand cays. These cays are inherently unstable, changing their size, and position on reef platforms in response to short-term and seasonal adjustments in wind, wave and current patterns. Island instability combined with high population densities h as resulted in the proliferation of engineered structures to combat erosion and to provide boat access. In many instances introduction of hard engineered structures has exacerbated island erosion and degraded reef productivity. Reasons for the negative environmental consequences are twofold. First, the design, materials used in construction and the mode of construction contravene most standard measures of sound engineering design. Constraining sound design is a total lack of environmental information, particularly wave and current data. Second, structures are often inappropriate with respect to natural coastal processes. This is illustrated by wave and current measurements on Hulhudhoo Island in Baa Atoll. Results show the nearshore process regime is dominated by strong alongshore current gradients. Furthermore, seasonal shifts in monsoon winds produce reversals in wave, current and sediment transport patterns. The unique circulatory nature of coastal processes around islands has a number of implications for use of engineered structures. First, conventional engineering practices resulting from an understanding of onshore/offshore and alongshore processes are not necessarily appropriate. Second, it requires reconsideration of notions of passive erosion and placement loss. Effects of structures that are usually transferred alongshore are contained within the 360 o island coastline and act to compound island instability and erosion.

Objective: To develop a time series model and analyze the characteristic fluctuations of the average white eggs consumer prices in the Mexico Valley (AWECP), quantifying the seasonal and cyclical fluctuations of said prices. Design/methodology/approach: A Moving Average Time Series Smoothing method was used to reduce the variation of a data set, and separate cyclical, seasonal, and OLS variations to calculate the trend line. Results: The Seasonal Component (SC) indicates that in autumn-winter the AWECP is volatile above the annual average by 6.3% while in spring-summer it declines by 9.13% on average. The price volatility is largely explained by external factors and the biological process of poultry animals throughout one year. The cyclical component (CC) was 34 months and occurs irregularly. Limitations on study/implications: This research focused on finding the SC, T, and CC, but not their respective indices. To make short-term forecasts, it is necessary to calculate the indices and compare them to other time series analysis methodologies. Findings/conclusions: The analysis of the AWECP in the Mexico Valley indicates the presence of SC and an upward CC trend in the time series. The AWECP volatility has harmful effects on the basic basket of the middle and lower-class population in the Mexico Valley given that this food is the most complete, cheap, and accessible source of animal protein in the Mexican market.

I declare that this thesis was composed by myself and that the work contained therein is my own, except where listed below. This work has not been submitted in whole or part for any other degree. Chapter 2 was originally published as O. Embury, C. J. Merchant, and M. J. Filipiak. A reprocessing for climate of sea surface temperature from the along-track scanning radiometers: Basis in radiative transfer. Remote Sensing of Environment, 116: 32-46, 2012. • The introductory sections 2.1 and 2.2 were drafted by C. Merchant • The work described in section 2.3.2 (Emissivity of seawater) was performed by M. Filipiak Chapter 3 was originally published as O. Embury and C. J. Merchant. A reprocessing for climate of sea surface temperature from the along-track scanning radiometers: A new retrieval scheme. Remote Sensing of Environment, 116: 47-61, 2012. • The introductory section 3.1 was drafted by C. Merchant • The discussion section 3.6 was drafted by C. Merchant Chapter 4 was originally published as O. Embury, C. J. Merchant, and G. K. Corlett, A reprocessing for climate of sea surface temperature from the along-track scanning radiometers: Initial validation, accounting for skin and diurnal variability effects. Remote Sensing of Environment, 116:62-78, 2012. • The introductory section 4.1 was drafted by C. Merchant • G. Corlett maintains the ATSR match-up database described in section 4.2.2 and provided guidance on its contents. • The Met Office skin and depth corrections described in section 4.3 were provided by K. Fennig. • The discussion section 4.5 includes contributions from C. Merchant and G. Corlett. Chapter 5 is intended for submission to Remote Sensing of Environment as: A reprocessing for climate of sea surface temperature from the along-track scanning radiometers: Harmonisation of Satellite Datasets into a single record.

This study aimed to forecast the monthly electricity expenses of a single household in the Philippines using time series analysis, with the goal of supporting informed financial planning and energy management at the residential level. Employing a quantitative research design, the study utilized the Autoregressive Integrated Moving Average (ARIMA) model, implemented via the Gretl software, to generate forecasts based on historical monthly electric bill data from January 2020 to December 2024. The dataset was preprocessed to ensure consistency and stationarity and validated using the Augmented Dickey-Fuller test. The model selection followed the Box-Jenkins methodology, with the optimal ARIMA (1,1,1) model identified based on Akaike Information Criterion (AIC) and diagnostic checking. Forecast accuracy was assessed using statistical metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results demonstrated the model's strong predictive capability, with forecasted bills for 2025 aligning closely with seasonal trends observed in the historical data. The findings highlight the practical utility of time series forecasting for household-level budgeting and emphasize the importance of localized predictive tools in managing electricity consumption in resource-constrained settings. This study contributes to the growing literature on residential energy analytics and offers a replicable methodology for similar applications in low-and middle-income countries.

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

This paper analyses the temporal dynamic of longterm fraction of Absorbed Photosynthetically Active Radiation (fAPAR) time series over Europe as derived from a 15 years dataset of satellite images collected between 2001 and 2015 from the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor onboard of Terra satellite. A fitting of piecewise logistic functions of time was performed for each cell in order to account for the multi-cycle dynamic of fAPAR. Most of the cells within the domain area showed a single peak cycle, even if a non-negligible fraction of the domain (about 15%) showed two distinct peaks in the yearly cycle. The results of the logistic function fittings allow identifying the key transition dates between increasing/decreasing trends in fAPAR, constituting a starting point for a more detailed analysis of drought effects on ecosystems.

This paper applies methods of seasonal ARIMA time series analysis to U. S. monthly birth and death rates for January 1950–December 1978. For model selection, the conventional Box-Jenkins (1970, 1976) diagnostic checks are used in conjunction with some suggestions of Nerlove et al. (1979). This results in final models that contain significant second-order autoregressive components, as well as seasonal moving-average components, for both series. In addition, some evidence is found in the birth rate series for weekly periodicities. These findings imply the existence of a seasonal adjustment procedure that improves on the Census X-11 program currently used by vital statistics agencies.

A model for the integrate mo of order one, IMA (1, 1), having a seasonal (cyclic) component is presented. The model incorporates a parameter for possible change in level of the process after intervention, following methods developed by Box and Tiao (1965), and Gottmann (1972). Least-squares estimates and associated significance tests for the parameters of the model (in particular, the intervention parameter) are derived. The results of a computer study and an example from real data are given with analysis and interpretation of parameter estimates. Results of the theoretical derivation are extended to other models [IMA (1, 1) with deterministic drift, multi-component models], and limitations of the model are noted. (Author)

Trabajo escrito en colaboración con la Inteligencia Artificial. Nota del autor Este trabajo ha sido desarrollado de manera independiente por Daniel Monteagut Rosales, investigador autodidacta con formación en ciencias médicas y experiencia en emprendimientos económicos. Su motivación surge de la necesidad estudiar y comprender las causas y consecuencias de las crisis económicas para evitar su impacto negativo en la Sociedad. El presente artículo se desarrolla en el contexto de un estudio sobre la economía de mercado y los principios de la Escuela Austríaca de Economía, combinando enfoques multidisciplinarios destacando la matemática y la economía, pero también relacionando otras ramas del saber que permiten entender aspectos más subjetivos como la psicología del consumidor, la incertidumbre, etc. Precede a esta investigación un trabajo anterior titulado: ''Validación teórica de la ley de la mano invisible mediante la teoría de juegos y la utilidad marginal decreciente''. En este caso nos ocupa enfocarnos en la demostración del efecto de la utilidad marginal decreciente a nivel macroeconómico a partir de un caso histórico concreto: la crisis económica durante la década perdida en Japón (años 1990 del siglo pasado). Este documento no está afiliado a ninguna institución académica, pero ha sido elaborado con el mayor rigor posible, con el propósito de contribuir al debate científico y promover futuras investigaciones aplicadas. Agradecemos cualquier sugerencia para enriquecer y mejorar el estudio. Este trabajo aborda el impacto de la utilidad marginal sobre el consumo privado en Japón durante la crisis de los años 90, a través de un estudio analítico, retrospectivo, cuantitativo y de datos secundarios sobre el comportamiento del consumo privado en la economía japonesa durante las décadas de 1980 y 1990, incorporando principios micro y macroeconómicos clásicos con otros aportes desde el estudio del comportamiento del consumidor bajo incertidumbre, así como la Teoría subjetiva del valor y otros aportes cruciales de la Escuela Austríaca de Economía. Los resultados obtenidos permitieron corroborar que en Japón durante la década de 1990 la utilidad marginal decreciente constituyó un factor negativo impidiendo una recuperación más rápida de la economía al desestimular el consumo privado, lo cual a su vez corrobora la hipótesis planteada en el trabajo Validación teórica de la ley de la mano invisible mediante la teoría de juegos y la utilidad marginal decreciente, de que la utilidad marginal decreciente juega un papel importante a nivel macroeconómico y constituye un indicador pronóstico del progreso social y económico. Palabras claves: utilidad marginal decreciente, consumo privado, década perdida, Japón, incertidumbre, teoría subjetiva del valor, escuela austríaca, mano invisible.

Resulta evidente la importancia que tiene para las decisiones de política económica poder separar aquellos movimientos seculares en las variables económicas de sus componentes estacionales, tanto para interpretar los datos de la coyuntura como para evaluar el efecto de las políticas mediante modelos econométricos. Pese a su importancia, usualmente se considera que la estacionalidad no es más que un ruido molesto que debe ser retirado de las series antes de proceder a su análisis. Remover la estacionalidad, sin embargo, no resulta trivial. Este trabajo demuestra que los actuales métodos de ajuste estacional no pueden ser considerados una simplificación inocente de los datos sin pérdida de información. Técnicas modernas de análisis de estacionalidad sugieren, además, que ellos podrían distorsionar de manera importante nuestra visión sobre la evolución de la economía y el impacto que tienen los instrumentos de políticas. En particular, los resultados de este estudio sugieren que muchas variables macroeconómicas presentan estructuras dinámicas muy diferentes de las usualmente supuestas, caracterizadas por la presencia de raices unitarias en frecuencias bianuales y trimestrales. Ello sugiere que el análisis y la simulación de la coyuntura y las políticas económicas que usualmente se hace con series desestacionalizadas sea cuidadosamente complementado con un análisis equivalente para las variables originales.

This paper examines the implications of applying the Hylleberg, Engle, Granger, and Yoo (1990,Journal of Econometrics44, 215–238) (HEGY) seasonal root tests to a process that is periodically integrated. As an important special case, the random walk process is also considered, where the zero-frequency unit roott-statistic is shown to converge to the Dickey–Fuller distribution and all seasonal unit root statistics diverge. For periodically integrated processes and a sufficiently high order of augmentation, the HEGYt-statistics for unit roots at the zero and semiannual frequencies both converge to the same Dickey–Fuller distribution. Further, the HEGY joint test statistic for a unit root at the annual frequency and all joint test statistics across frequencies converge to the square of this distribution. Results are also derived for a fixed order of augmentation. Finite-sample Monte Carlo results indicate that, in practice, the zero-frequency HEGY statistic (with augmentation) captures ...

We test for the presence of low-dimensional chaotic structure in the Stock Exchange of Thailand (SET) Index. While we find strong evidence of nonlinear dependencies, the evidence is not consistent with chaos. Our test results indicate that ARCH-type processes generally explain the nonlinearities in the data. We also show that employing seasonally adjusted index series contributes to obtaining robust results via some of the existing tests for chaotic structure.

We test for the presence of low-dimensional chaotic structure in the Stock Exchange of Thailand (SET) Index. While we find strong evidence of nonlinear dependencies, the evidence is not consistent with chaos. Our test results indicate that ARCH-type processes generally explain the ...

We conduct tests for the presence of low-dimensional chaotic structure in the futures prices of four important agricultural commodities. Though there is strong evidence of non-linear dependence, the evidence suggests that there is no long-lasting chaotic structure. The dimension estimates for the commodity futures series are generally much higher than would be for low dimension chaotic series. Our test results indicate that autoregressive conditional heteroskedasticity (ARCH)-type processes, with controls for seasonality and contract-maturity effects, explain much of the non-linearity in the data. We make a case that employing seasonally adjusted price series is important in obtaining robust results via some of the existing tests for chaotic structure. Finally, maximum likelihood methodologies, that are robust to the non-linear dynamics, lend strong support to the Samuelson hypothesis of maturity effects in futures price changes.

Ajuste estacional de series macroeconómicas chilenas * Las series ajustadas por estacionalidad permiten un análisis más directo de los movimientos subyacentes que exhiben las variables económicas y, por lo tanto, contribuyen al diagnóstico de la situación económica. Este documento presenta el marco teórico y empírico para el ajuste estacional de series macroeconómicas compiladas por el Banco Central de Chile.

The Central Bank of Bangladesh publishes financial data on a weekly, quarterly, monthly, and yearly basis. Among the published data, GDP, production, export, import, balance payment, wage earners remittance, Stock etc. are time-based and often subject to seasonal variation. But like most of the developed country's central banks such as the USA, Australia, Singapore, UK, French etc. Bangladesh Bank does not publish seasonally adjusted data. These published time series are used by economists, decision-makers, and consumers to inform decisions. They look for key characteristics of economic series like trends, turning points, and coherence with other economic data. Seasonal shifts might occasionally make it challenging to see these features. Therefore, the research aimed to explore the best seasonal adjustment technique for different financial data published by Bangladesh Bank. Here, the nonfilter-based seasonal adjustment technique: Ratio to moving average, Ratio to trend, and SARIMA and filter-based techniques: X-11 and X-13-ARIMA-SEATS were applied to monthly import and export data published by Bangladesh Bank. The seasonal adjustment method that produces fewer forecasting errors (MAPE, MASE, MAE, and RMSE) were decided as the best technique for seasonal adjustment and X-13-ARIMA-SEATS method founds the appropriate seasonal adjustment method for our considered data. Therefore, X-13-ARIMA-SEATS method can be recommended to forecast monthly export and import of Bangladesh published by Bangladesh Bank.

This study examines the properties of monthly CPI inflation in G7 countries and the Euro area (aggregate) over the period 1973-2007 using a new iterative decomposition procedure that separates changes in mean, seasonal and dynamic components together with conditional volatility. We uncover mean and seasonality breaks for all countries and, even allowing for these, changes in persistence are indicated for all countries except Canada. Further, while volatility reductions are widespread in the mid-to early 1980s, Canada, France and the US all exhibit increased volatility from 1999 onwards. Of methodological interest, iteration is shown to provide more evidence of persistence breaks and fewer volatility breaks overall compared with the usual approach of sequentially examining changes in the properties of inflation, while application of linear seasonal adjustment also reduces evidence of persistence breaks. Although failure to allow for breaks in mean, seasonal or dynamic components affects conclusions about the existence and dates of volatility breaks, nevertheless, evidence remains of a volatility increase in some countries in 1999.

This research work evaluates the performance of advance GARCH family models in modeling and forecasting of petroleum pump price in the event of extreme data outlier and volatility in Nigeria by addressing the problems related to extreme outliers and asymmetric shocks in traditional methods. The study compares EGARCH for leverage effects, Robust-GARCH with MM-estimators/GM-estimators for outlier resistance and traditional GARCH and FGARCH models using monthly data from 2000 to 2023. The predictive capability of Robust-GARCH(GM) reaches its best level (lower MAE and RMSE) through its ability to reduce extreme values such as the 2023 price spikes which is supported by Diebold-Mariano tests at p = 0.01. The most critical step in parameter estimation relies on logdifferencing because it transforms data and eliminates erratic fluctuations that prevent stable variance results. The research provides reliable practical methodology to policymakers and energy analysts in utilizing the Robust-GARCH (GM) for market price predictions during crisis times and EGARCH for volatility analysis regarding subsidies. The findings develop a reliable approach to volatility analysis for commodity-based economic systems which supports risk management as well as energy policy creation strategies.

The analysis of prices and market arrivals over time is important for formulating a sound agricultural price policy. Fluctuations in market arrivals largely contribute to price instability. In order to devise the appropriate ways and means for reducing the price fluctuations of agricultural commodities, there is a need to have a thorough understanding of the price behavior over time.Forecasting the price of agricultural commodities, presents some unique challenges such as data quality issues, weather aberrations, high fluctuations, price variations across neighboring marketplaces, etc. On the demand side, the instability in the prices of agricultural commodities is influenced by a number of factors such as annual variation in production, low price elasticity of demand and seasonality of agricultural production.The study relied upon the secondary time series data on monthly market prices and arrivals of Okra collected from the Agricultural Produce Market Committee, Surat. Analysis wa...

In Nigeria, the average monthly quantity of currency in circulation (CIC) has increased by 269 billion nairas, reaching 2.13 trillion as of 2019 and 2.41 trillion as of 2020. The current value of currency in circulation is expected to be 2.88 trillion naira. The economy of Nigeria is impacted by the seasonal fluctuations in its currency, and it is unavoidable that the economy would need to be adjusted. The purpose of this study was to adjust the seasonal effect of eight days to Easter and Muslim holidays on CIC, model and predict the CIC in Nigeria using the United State Census Bureau's X-12 ARIMA Seasonal adjustment software. The data utilized in the study was the monthly amount of money in circulation that was taken from the Central Bank of Nigeria (CBN) Bulletin between January 2012 and March 2022. Natural logarithm was used to standardize the data, and series seasonality was removed using seasonal differencing. Based on these data, it is clear that X-12-ARIMA (2 1 1)(0 1 1) ...

Existen efectos calendario definidos como patrones estacionales irregulares que afectan el comportamiento de las series económicas. En este documento se utilizaron las metodologías de Tramo-Seats propuesta por Gómez y Maravall (1994, 1996) y de tbats planteada por para ajustar estacionalmente los índices de producción industrial sectoriales en Colombia para el período comprendido entre enero de 1990 y septiembre de 2014, teniendo en cuenta efectos calendario como la Semana Santa y los días feriados. Se encontró que la mayoría de actividades que componen la industria se ven afectadas negativamente en su producción por dichos efectos, excepto por la refinación de petróleo, las sustancias químicas y vidrio, donde el impacto no es significativo. Los coeficientes estimados asociados a la Semana Santa presentan una magnitud mayor que la de días feriados y existe alta heterogeneidad en los resultados por actividad industrial. Por último, el impacto más significativo sobre la producción industrial sectorial se presenta cuando irregularmente * Los autores son, en su orden, asistente de investigación, econometrista principal de la

Seasonal cointegration generalizes the idea ofcointegration to processes with unit roots at frequencies different from 0. Here, "common seasonals," also a dual notion of common trends, is adopted for the seasonal case. The features are demonstrated in exemplary models for German and U.K. data. An evaluation of the predictive value of accounting for seasonal cointegration shows that seasonal cointegration may be difficult to exploit to improve predictive accuracy even in cases where seasonal non-cointegration is clearly rejected on statistical grounds. The findings from the real-world examples are corroborated by Monte Carlo simulation.

Abstract: Seasonal cointegration generalizes the idea of cointegration to processes with unit roots at frequencies different from 0. Here, also the dual notion of common trends, "common seasonals", is adopted for the seasonal case. Using a five-variable macroeconomic core system of the Austrian economy, it is demonstrated how common seasonals and seasonal cointegrating vectors look in practice. Statistical tests provide clear evidence on seasonal cointegration in the system. However, it is shown that accounting for seasonal cointegration does not necessarily entail increased predictive accuracy for individual series. For the "master series" of GDP, seasonal cointegration modeling is profitable for very short forecast horizons but for long horizons simple seasonal dummies appear to dominate.;

This article is addressed to the problem of modeling and exploring mean value structure of large-scale time series data and time-space data. A smoothness prior modeling approach (Smoothness Prior Analysis of Time Series, Lecture Notes in Statistics, vol. 116, Springer, New York, 1996.) is taken. In this approach, the observed series are decomposed into several components each of which are expressed by smoothness priors models. In the analysis of POS and GPS data, various useful information were extracted by this decomposition, and result in discoveries in these areas.

This paper uses a EGARCH method to model inflation uncertainty in Turkey. Unlike ARCH and GARCH methods, the EGARCH method both hampers the effect of outlying shocks in the estimation of inflation uncertainty and enables the separate treatment of the negative and positive shocks to inflation. As inflation uncertainty itself may follow a seasonal pattern, the series is subjected to monthly seasonal adjustment. The inflation measure we use in this paper is the monthly CPI inflation and covers the period from 1986:1 to 2000:12. The evidence in the paper shows that monthly seasonality has a significant effect on inflation uncertainty. Moreover, once seasonal effects are accounted for, lagged inflation no longer appears to affect inflation uncertainty.

Sea surface temperature (SST) fields obtained from the series of space-borne five-channel Advanced Very High Resolution Radiometers (AVHRRs) provide the longest continuous time series of global SST available to date (1981–present). As a result, these data have been used for many studies and significant effort has been devoted to their careful calibration in an effort to provide a climate quality data record. However, little attention has been given to the local precision of the SST retrievals obtained from these instruments, which we refer to as the pixel-to-pixel (p2p) variability, a characteristic important in the ability to resolve structures such as ocean fronts characterized by small gradients in the SST field. In this study, the p2p variability is estimated for Level-2 SST fields obtained with the Pathfinder retrieval algorithm for AVHRRs on NOAA-07, 9, 11, 12 and 14-19. These estimates are stratified by year, season, day/night and along-scan/along-track. The overall variabili...

Background/Aims: Type-1 diabetics have been considered to be at risk for increased oxidative stress which has been implicated in the development of long-term diabetes complications. Evidence suggests that antioxidant activity may be an important mechanism by which carotenoids could confer protection in human health. Our aim was to compare the serum carotenoid depletion rate in type-1 diabetics and control subjects consuming low-carotenoid diets. Methods: Ten type-1 diabetics and 8 controls followed a very low-carotenoid diet for 21 days. Dietary intake was recorded daily and fasting blood samples collected at baseline and after 1, 2, 3, 6, 11, 15, 16, 17, and 21 days. Individual carotenoids in serum were analyzed by a validated HPLC method. Results: In both groups, carotenoid intake was less than 5% of the season-adjusted carotenoid reference intake. These diets resulted in decreased serum carotenoid levels in both groups, although depletion curves, final mean concentrations and the...

We propose recent functional data analysis techniques to study the intra-daily volatility. In particular, the volatility extraction is based on functional principal components and the volatility prediction on functional AR(1) models. The estimation of the corresponding parameters is carried out using the functional equivalent to OLS. We apply these ideas to the empirical analysis of the IBEX35 returns observed each

This paper examines the performance of Random Forest models for forecasting short-term monthly inflation in Argentina, especially for the current month or the following. Using a database with indicators on a monthly basis since 1962, it is concluded that these models achieve a forecasting accuracy statistically comparable to the consensus of market analysts surveyed by the Central Bank of the Argentine Republic (BCRA) and to traditional econometric models. One advantage of Random Forest models is that, as they are non-parametric, they allow for the exploration of nonlinear effects in the predictive power of certain macroeconomic variables on inflation. It is found, among other things, that: 1) the relative relevance of the exchange rate gap for forecasting inflation grows when the gap between the parallel and official exchange rates exceeds 60%; 2) the predictive power of the exchange rate on inflation increases when the BCRA's net international reserves are negative or close to zero (specifically, less than USD 2 billion); 3) the relative relevance of lagged inflation and the nominal interest rate to forecast inflation for the following month increases when the level of inflation and/or the level of the interest rate rise.

The aim of this work has been assessment of regional atmospheric influence on satellite derivation of the Adriatic Sea surface temperature (SST). To that end the ECMWF ERA-40 reanalysis dataset has been employed to provide the temperature and humidity profiles and surface data, while the RTTOV 8.7 radiative transfer model was used to calculate the top of atmosphere brightness temperatures for the AVHRR channels. Ten ERA-40 grid points over the Adriatic Sea were used in the analysis, providing 29590, 00 UTC and 12 UTC, clear-sky profiles. Climatological analysis of the ERA-40 profiles demonstrated a distinct seasonal variability over the Adriatic. Seasonality noted in the temperature and specific humidity profiles also evinced in the atmospheric transmittance, thermal channels temperature deficit, and derived γ and ρ parameters. A multivariate analysis was applied to relate the simulated top of the atmosphere (TOA) brightness temperatures (BT) to the Adriatic SSTs, in order to generate exploratory sets of SST retrieval coefficients. All 10 derived coefficient sets exhibited smaller noise amplification factor than the global counterpart. A test comparison of satellite-derived SST with an eleven-month in situ SST series showed than locally derived coefficients provide smaller scatter (improved precision), and bias that requires empirical adjustment before operational use. Almost identical SST residual and metrics was obtained with seasonally adjusted "classical" split-window coefficients and with coefficients explicitly accommodating water vapour dependence. The comparison to data has reinforced the notion that the over-the-Adraitic atmosphere may exhibit variability which globally adjusted correction can not fully accommodate.

The Central Bank of Bangladesh publishes financial data on a weekly, quarterly, monthly, and yearly basis. Among the published data, GDP, production, export, import, balance payment, wage earners remittance, Stock etc. are time-based and often subject to seasonal variation. But like most of the developed country's central banks such as the USA, Australia, Singapore, UK, French etc. Bangladesh Bank does not publish seasonally adjusted data. These published time series are used by economists, decision-makers, and consumers to inform decisions. They look for key characteristics of economic series like trends, turning points, and coherence with other economic data. Seasonal shifts might occasionally make it challenging to see these features. Therefore, the research aimed to explore the best seasonal adjustment technique for different financial data published by Bangladesh Bank. Here, the nonfilter-based seasonal adjustment technique: Ratio to moving average, Ratio to trend, and SARIMA and filter-based techniques: X-11 and X-13-ARIMA-SEATS were applied to monthly import and export data published by Bangladesh Bank. The seasonal adjustment method that produces fewer forecasting errors (MAPE, MASE, MAE, and RMSE) were decided as the best technique for seasonal adjustment and X-13-ARIMA-SEATS method founds the appropriate seasonal adjustment method for our considered data. Therefore, X-13-ARIMA-SEATS method can be recommended to forecast monthly export and import of Bangladesh published by Bangladesh Bank.

Lq wklv sdshu zh frqvlghu ghwhuplqlvwlf vhdvrqdo yduldwlrq lq txdu0 whuo| lqgxvwuldo surgxfwlrq iru vhyhudo Hxurshdq frxqwulhv/ dqg zh dgguhvv wkh txhvwlrq zkhwkhu wklv yduldwlrq kdv ehfrph pruh vlplodu dfurvv frxqwulhv ryhu wlph1 Gxh wr hfrqrplf dqg lqvwlwxwlrqdo idfwruv/ rqh pd| h{shfw frqyhujhqfh dfurvv exvlqhvv f|fohv1 Zkhq wkhvh kdyh vlplodu fkdudfwhulvwlfv dv vhdvrqdo f|fohv/ rqh pd| shukdsv dovr qg frqyhujhqfh lq vhdvrqdolw|1 Wr wklv dlp/ zh sursrvh d qhz phwkrg/ zklfk lv edvhg rq wuhdwlqj wkh vhw ri surgxfwlrq vhulhv dv d sdqho1 E| whvwlqj iru wkh uhohydqw sdudphwhu uhvwulfwlrqv iru prylqj zlqgrz vdpsohv/ zh h{dplqh wkh k|srwkhvlv ri frqyhujhqfh lq ghwhuplqlvwlf vhdvrqdolw| zkloh doorzlqj iru vhdvrqdo xqlw urrwv1 Zh ghulyh wkh hvwl0 pdwlrq eldv/ dqg vkrz wkdw lw lv yhu| vpdoo iru vdpsohv ri pruh wkdq wkuhh |hduv ri txduwhuo| revhuydwlrqv1 Rxu pdlq hpslulfdo qglqj lv wkdw wkhuh lv doprvw qr hylghqfh iru frqyhujhqfh lq vhdvrqdolw|1 Nh| zrugv= Frqyhujhqfh> Ghwhuplqlvwlf vhdvrqdo yduldwlrq> Hxur0 shdq lqgxvwuldo surgxfwlrq1 Fruuhvsrqglqj dxwkru1 Fruuhvsrqghqfh wr Urehuw P1 Nxqvw/ Lqvwlwxwh iru Dgydqfhg Vwxglhv/ Vwxpshujdvvh 89/ D4393 Ylhqqd/ Dxvwuld> h0pdlo dgguhvv= nxqvwClkv1df1dw1

This project is aimed at the description and analysis of the determinants and short and long term effects of the economic policies in Argentina and in the global economy. In particular, it is focused on monetary, financial, fiscal, exchange rate, trade income distribution and socioeconomic development policies. The main topics to be examined are related to their initial conditions, political and financial constraints, implementation mechanisms, macroeconomic effects on nominal and real variables, short-term sector effects, and their significance for budget constraints, financial stability, and the economic-financial decisions of consuming units, companies and governments. In addition, the project includes the assessment of their long-term implications; with regards to the analysis of topics related to the Argentine economy and others, this leads to consider the impact on economic growth, income distribution, fiscal sustainability, capital flows volatility, incentives to investment in human and physical capital, and in technological innovation, as well as their impact on trade competitiveness. As of yet, the conclusions of our investigations on the Argentine economy highlight the relevance of external factors on the Argentine business cycles-terms of trade, trade partners' economic growth, changes in the international financial conditions, and the quality of both the design and the implementation of the local economic policy-. Concerning the international economy, a significant incidence of financial developments on the business cycle of advanced economies was observed, as well as the consolidation of the influence of the main emerging economies on the cycles of the whole set of less developed countries.

The ARIMA-model-based methodology of programs TRAMO and SEATS is applied for seasonal adjustment and trend-cycle estimation of the exports, imports, and balance of trade Japanese series. The programs are used in an automatic mode, and the results are analyzed. It is shown how the SEATS output can be of help when discriminating among competing models. Finally, the example is used to discuss the important problem of the choice between direct and indirect adjustment of an aggregate. It is concluded that, because aggregation has a strong effect on the spectral shape of the series, and because seasonal adjustment is a non-linear transformation of the original series, direct adjustment is preferable, even at the cost of destroying identities between the original series.

A partir del segundo trimestre de 2009 la actividad económica global comenzó a mostrar señales de recuperación, luego de la recesión generada por la crisis subprime. Sin embargo, el optimismo de fines de 2009 ha dejado lugar a una mayor cautela sobre los tiempos de salida de la crisis, particularmente en los países desarrollados. Esto marca una segunda etapa de la crisis caracterizada por la incertidumbre respecto a las posibilidades de que se sostenga la recuperación de la economía global. En este contexto, los países emergentes y en desarrollo están mostrando un mejor desempeño económico, aunque cabe preguntarse si un “desacople” es posible en un escenario de debilidad del crecimiento en los países avanzados. Paralelamente a los desarrollos de la coyuntura, continuaron las negociaciones relacionadas con la reforma de la Arquitectura Financiera Internacional en el ámbito del G-20, que tiene como objetivo hacer a la economía global menos proclive a crisis. A pesar de los avances en ...

Resumen El incremento en los precios internacionales de los commodities exportados por Argentina se presenta usualmente como un viento de cola que explica tanto el fuerte desempeño en materia de crecimiento como las buenas condiciones globales que han beneficiado al país desde 2003 hasta mediados de 2008. Este trabajo investiga cuáles son los determinantes de los precios de los ocho

A model for the integrate mo of order one, IMA (1, 1), having a seasonal (cyclic) component is presented. The model incorporates a parameter for possible change in level of the process after intervention, following methods developed by Box and Tiao (1965), and Gottmann (1972). Least-squares estimates and associated significance tests for the parameters of the model (in particular, the intervention parameter) are derived. The results of a computer study and an example from real data are given with analysis and interpretation of parameter estimates. Results of the theoretical derivation are extended to other models [IMA (1, 1) with deterministic drift, multi-component models], and limitations of the model are noted. (Author)

The U. S. Census Bureau's enhanced X-12-ARIMA seasonal ad justment p rogram includes the automatic ARIMA (Autoregressive Integrated M oving Average) model selection procedure developed by Statistics Canada and a second procedure based on the automatic procedure of TRAMO (Time series Regression with ARIMA noise, Missing observations and Outliers), a modeling package developed by Victor Gomez and Agustin Maravall. Each program has auto matic id entification o f key regre ssors, allowing for full automatic selection of a regARIMA model (regression with an underlying ARIMA process). X-12-ARIMA's procedure differs from T RAMO's in a number of ways. Our study updates previous work as we compared the procedures again using improved versions of the two programs. We applied the procedures to a set of Census Bureau time series and s imulations. When model choices differed, we compared standard modeling diagnostics to look for a consistent preference for either procedure. As i n t ...

Several methods exist which can adjust for trading day and holiday effects in monthly economic time series. This paper reviews and compares two such methodologies for conducting proper adjustments. The two methodologies are based upon the U.S. Census Bureau’s X-12-ARIMA method and one developed by the Statistical Offices of the European Communities, commonly referred to as Eurostat. Three different methods are used to compare the U.S. Census Bureau procedure and the Eurostat-inspired procedure. These methods are spectral analysis, samplesize corrected AIC comparisons, and examination of out-of-sample forecast errors. Finally, these comparisons are conducted using nearly 100 U.S. Census Bureau time series of manufacturing data, retail sales, and housing starts. This empirical study is the first of its kind and therefore provides an important contribution to the seasonal adjustment community.

This document,gives details about,the recently released Version 0.3 of the X-12-ARIMA seasonal,adjustment,software. New features are highlighted, and dierences between Version 0.3 and Version 0.2.10 of X-12- ARIMA are detailed. Several software,defects were corrected,in this version; details of these are given. Key Words: seasonal adjustment, ARIMA model selection, composite adjustment, metadata. Disclaimer

This paper will give the latest developments in two ongoing software projects. One is a joint collaboration with the current developers of the SEATS seasonal adjustment program, X-13ARIMA-SEATS, officially released by the Census Bureau in 2012. This program combines the seasonal adjustment and modeling modules of the X-12-ARIMA program with the model-based seasonal adjustment module from SEATS. This program allows producers of seasonally adjusted series to generate X-11 and SEATS seasonal adjustments using the same interface, and compare these seasonal adjustments using a common set of diagnostics. The second is a software system named iMetrica, which is a unique GUI oriented software for both simulating and modeling from many different types of time series models. This software package focuses on speed, user interaction, visualization tools, and point-and-click simplicity for building models for time series data of all types and is written entirely in GNU C and Fortran with a rich interactive interface written in Java. One powerful feature that is unique to the iMetrica software is the capability of easily combining both model-based and nonmodel based methodologies for designing data forecasts, signal extraction filters, or strategies for comparison of nested or non-nested models. Furthermore, the model comparisons can be computed and tested both in-sample and out-of-sample using a built-in data partitioner that effectively partitions the data into an in-sample storage where models and filters are computed and then an out-of-sample storage where new data is applied to the in-sample strategy to test for robustness and many other desired properties. This gives the user complete liberty in creating a fast and efficient test-bed for implementing signal extractions and forecasting regimes. This paper will demonstrate how SEATS adjustments are integrated into the X-13ARIMA-SEATS procedure and give examples of new diagnostics and modeling options, including some designed for use in modeling the recent recession, as well as a description of the five interacting time series analysis modules that comprise iMetrica. These include modules for X-13ARIMA-SEATS, unobserved component modeling, and state space modeling.