This thesis is focused on analysis of the development of gross month wage and in particular on development and examining seasonality. There are also described the institutions and their surveys of wages, such as the Czech Statistical Office, Ministry of Finance and the Ministry of Labor and Social Affairs, which administers the Information System of Average Earnings. The monthly income is compared between regions and between major classes KZAM. The development of time series is modeled by the Box-Jenkins methodology, further charts of seasonal values and seasonal indexes . For comparison the average relative wage growth in regions are used cartograms. The base for these analyses is data obtained from business statistical return systems and structural statistics from the site of the Czech Statistical Office and the Ministry of Labor and Social Affairs.

by Moon-Tong Chan. / Thesis submitted in: December 1997. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 123-124). / Abstract also in Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The Data Set --- p.8 / Chapter Chapter 2 --- The Box-Jenkins Time Series Models --- p.15 / Chapter 2.1 --- The White-noise Process --- p.16 / Chapter 2.2 --- Stationarity of Time Series --- p.17 / Chapter 2.3 --- Differencing --- p.19 / Chapter 2.4 --- Seasonal ARIMA Models - SARIMA Models --- p.20 / Chapter 2.5 --- Intervention Models --- p.22 / Chapter 2.6 --- The Three Phases of ARMA Procedure --- p.24 / Chapter Chapter 3 --- Seasonal ARMA Models with Several Mean Levels --- p.38 / Chapter 3.1 --- Review of Linear Models --- p.40 / Chapter 3.1.1 --- Method of Weighted Least Squares --- p.41 / Chapter 3.2 --- The Proposed Model --- p.41 / Chapter 3.2.1 --- The Weightings --- p.43 / Chapter 3.2.2 --- Selection of Submodels --- p.45 / Chapter 3.2.3 --- Estimation of Model (3.4) --- p.46 / Chapter 3.3 --- Model Adequacy Checking --- p.55 / Chapter 3.3.1 --- Checking of Independence of Residuals --- p.56 / Chapter 3.3.2 --- Checking of Normality of Residuals --- p.58 / Chapter 3.4 --- Forecasting --- p.62 / Chapter Chapter 4 --- Comparison --- p.77 / Chapter 4.1 --- Similarities and Differences Between the Two Models --- p.78 / Chapter 4.2 --- Model Comparative Criterion --- p.81 / Chapter 4.2.1 --- Model Fitting Comparison --- p.82 / Chapter 4.2.2 --- Model Forecasting Comparison --- p.83 / Chapter 4.3 --- Conclusion --- p.90 / Chapter 4.4 --- Generation of Predicted Hourly Calls --- p.91 / Chapter 4.5 --- Extension --- p.92 / Appendix A --- p.97 / Appendix B --- p.105 / Appendix C --- p.122 / References --- p.123

Ghana faces a macroeconomic problem of inflation for a long period of time. The problem in somehow slows the economic growth in this country. As we all know, inflation is one of the major economic challenges facing most countries in the world especially those in African including Ghana. Therefore, forecasting inflation rates in Ghana becomes very important for its government to design economic strategies or effective monetary policies to combat any unexpected high inflation in this country. This paper studies seasonal autoregressive integrated moving average model to forecast inflation rates in Ghana. Using monthly inflation data from July 1991 to December 2009, we find that ARIMA (1,1,1)(0,0,1)12 can represent the data behavior of inflation rate in Ghana well. Based on the selected model, we forecast seven (7) months inflation rates of Ghana outside the sample period (i.e. from January 2010 to July 2010). The observed inflation rate from January to April which was published by Ghana Statistical Service Department fall within the 95% confidence interval obtained from the designed model. The forecasted results show a decreasing pattern and a turning point of Ghana inflation in the month of July.

Solar radiation is a primary driving force behind a number of solar energy applications such as photovoltaic systems for electricity generation amongst others. Hence, the accurate modelling and prediction of the solar flux incident at a particular location, is essential for the design and performance prediction of solar energy conversion systems. In this regard, literature shows that time series models such as the Box-Jenkins Seasonal/Non-seasonal Autoregressive Integrated Moving Average (S/ARIMA) stochastic models have considerable efficacy to describe, monitor and forecast solar radiation data series at various sites on the earths surface (see e.g. Reikard, 2009). This success is attributable to their ability to capture the stochastic component of the irradiance series due to the effects of the ever-changing atmospheric conditions. On the other hand at the top of the atmosphere, there are no such conditions and deterministic models which have been used successfully to model extra-terrestrial solar radiation. One such modelling procedure is the use of a sinusoidal predictor at determined harmonic (Fourier) frequencies to capture the inherent periodicities (seasonalities) due to the diurnal cycle. We combine this deterministic model component and SARIMA models to construct harmonically coupled SARIMA (HCSARIMA) models to model the resulting mixture of stochastic and deterministic components of solar radiation recorded at the earths surface. A comparative study of these two classes of models is undertaken for the horizontal global solar irradiance incident on the solar panels at UKZN Howard College (UKZN HC), located at 29.9º South, 30.98º East with elevation, 151.3m. The results indicated that both SARIMA and HCSARIMA models are good in describing the underlying data generating processes for all data series with respect to different diagnostics. In terms of the predictive ability, the HCSARIMA models generally had a competitive edge over the SARIMA models in most cases. Also, a tentative study of long range dependence (long memory) shows this phenomenon to be inherent in high frequency data series. Therefore autoregressive fractionally integrated moving average (ARFIMA) models are recommended for further studies on high frequency irradiance. / M.Sc. University of KwaZulu-Natal, Durban 2014.

Using a simple linear regression model for estimation could give misleading results about the relationship between Yt, and Xt, . Possible problems involve (1) feedback from the output series to the inputs, (2) omitted time-lagged input terms, (3) an auto correlated disturbance series and, (4) common autocorrelation patterns shared by Y and X that can produce spurious correlations. The primary aim of this study was therefore to use the Box-Jenkins Transfer Function analysis to fit a model that related petroleum consumption to disposable income> The final Transfer Function Model z1t=)C(1-w1 B)/((1-δ1 B) B^5 Z(t^((x) +(1-θ1 B)at significantly described the data. Forecasts generated from this model show that petroleum consumption will hit a record of up to 4.8636 in 2014 if disposable income is augmented. There is 95% confidence that the forecasted value of petroleum consumption will lie between 4.5276 and 5.1997 in 2014. / Thesis (M. Com. (Statistics) North-West University, Mafikeng Campus, 2005

Submitted by Cristiano Dallagassa Gontijo Oliveira (c_dallagassa@hotmail.com) on 2016-03-12T18:40:55Z No. of bitstreams: 1 Dissertação Cristiano - FORMATADA E REVISADA 12.03 - PDF.pdf: 1509635 bytes, checksum: 504b76cb93f85ab2e09d5d2b8e04d4e1 (MD5) / Approved for entry into archive by Fabiana da Silva Segura (fabiana.segura@fgv.br) on 2016-03-14T16:00:47Z (GMT) No. of bitstreams: 1 Dissertação Cristiano - FORMATADA E REVISADA 12.03 - PDF.pdf: 1509635 bytes, checksum: 504b76cb93f85ab2e09d5d2b8e04d4e1 (MD5) / Made available in DSpace on 2016-03-14T16:14:31Z (GMT). No. of bitstreams: 1 Dissertação Cristiano - FORMATADA E REVISADA 12.03 - PDF.pdf: 1509635 bytes, checksum: 504b76cb93f85ab2e09d5d2b8e04d4e1 (MD5) Previous issue date: 2016-02-16 / The aims of this work was forecast the demand for agricultural tractors in the Brazilian market during the triennium 2016-2018, using for this, techniques of time series econometrics, in this case, univariate models ARIMA and SARIMA and or multivariate models SARIMAX. Justified this research when holding the industry of agricultural machinery in Brazil, given the economic cycles and other external factors to the economic funda-mentals of demand, where it faces many challenges. Among this, demand estimation stands out because exert a strong impact, for example, planning and cost of short and medium term production, inventory levels, in relation to hand materials and suppliers of local labor and consequently in creating value to the shareholders. During literature review it was found several scientific papers address the agribusiness and its various areas, however, they were not found scientific papers published in Brazil that address the demand forecast of agricultural tractors in Brazil, which served as a motivation for add knowledge to the scientific world and value to the Brazilian market. It was concluded after testing with several models that are presented in the text and appendices, the model SARIMA (15, 1, 1) (1, 1, 1) fulfilled the assumptions set out in the specific objectives to choose the model that best fit itself to the data, and then was chosen as the model to forecast the demand for agricultural tractors in Brazil. These results point to a demand for agricultural tractors in Brazil oscillating between 46,000 and 49,000 units per year between the years 2016 and 2018. / O objetivo desta dissertação foi estimar a demanda de tratores agrícolas para o mercado brasileiro no triênio 2016-2018, utilizando-se para isto de técnicas de econometria de séries temporais, neste caso, modelos univariados da classe ARIMA e SARIMA e ou multivariados SARIMAX. Justifica-se esta pesquisa quando se observa a indústria de máquinas agrícolas no Brasil, dados os ciclos econômicos e outros fatores exógenos aos fundamentos econômicos da demanda, onde esta enfrenta muitos desafios. Dentre estes, a estimação de demanda se destaca, pois exerce forte impacto, por exemplo, no planejamento e custo de produção de curto e médio prazo, níveis de inventários, na relação com fornecedores de materiais e de mão de obra local, e por consequência na geração de valor para o acionista. Durante a fase de revisão bibliográfica foram encontrados vários trabalhos científicos que abordam o agronegócio e suas diversas áreas de atuação, porém, não foram encontrados trabalhos científicos publicados no Brasil que abordassem a previsão da demanda de tratores agrícolas no Brasil, o que serviu de motivação para agregar conhecimento à academia e valor ao mercado através deste. Concluiu-se, após testes realizados com diversos modelos que estão dispostos no texto e apêndices, que o modelo univariado SARIMA (15,1,1) (1,1,1) cumpriu as premissas estabelecidas nos objetivos específicos para escolha do modelo que melhor se ajusta aos dados, e foi escolhido então, como o modelo para estimação da demanda de tratores agrícolas no Brasil. Os resultados desta pesquisa apontam para uma demanda de tratores agrícolas no Brasil oscilando entre 46.000 e 49.000 unidades ano entre os anos de 2016 e 2018.

Unemployment is a common phenomenon in economy. The unemployment rate is an indicator reflecting the economic situation significantly. Unemployment is followed by the public intensively and that is why it is an important political topic. To fight unemployment it is necessary to analyze its current state, development and estimated future prospects. This thesis is focused on analysis of the state and development of unemployment in the Czech Republic and EU. Its future development is estimated using the Box-Jenkins method.

The main goal of the thesis is to analyze the basic indicators of marriage rate, divorce rate and the proportion of live births outside marriage. The first part is focused on the evaluation of the past and present development of the selected indicators. The thesis also includes a prediction of the future development of these indicators by using the Box-Jenkins methodology. The last part is dedicated to an international comparison of marriage, divorce and non-marital fertility indicators in the selected countries of the European Union. Moreover, the last part also includes a cluster analysis, where countries are divided into homogeneous groups according to the selected indicators.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

Cette thèse traite de l’identification de systèmes dynamiques à partir de données échantillonnées à pas variable. Ce type de données est souvent rencontré dans les domaines biomédical, environnemental, dans le cas des systèmes mécaniques où un échantillonnage angulaire est réalisé ou lorsque les données transitent sur un réseau. L’identification directe de modèles à temps continu est l’approche à privilégier lorsque les données disponibles sont échantillonnées à pas variable ; les paramètres des modèles à temps discret étant dépendants de la période d’échantillonnage. Dans une première partie, un estimateur optimal de type variable instrumentale est développé pour estimer les paramètres d’un modèle Box-Jenkins à temps continu. Ce dernier est itératif et présente l’avantage de fournir des estimées non biaisées lorsque le bruit de mesure est coloré et sa convergence est peu sensible au choix du vecteur de paramètres initial. Une difficulté majeure dans le cas où les données sont échantillonnées à pas variable concerne l’estimation de modèles de bruit de type AR et ARMA à temps continu (CAR et CARMA). Plusieurs estimateurs pour les modèles CAR et CARMA s’appuyant sur l’algorithme Espérance-Maximisation (EM) sont développés puis inclus dans l’estimateur complet de variable instrumentale optimale. Une version étendue au cas de l’identification en boucle fermée est également développée. Dans la deuxième partie de la thèse, un estimateur robuste pour l'identification de systèmes à retard est proposé. Cette classe de systèmes est très largement rencontrée en pratique et les méthodes disponibles ne peuvent pas traiter le cas de données échantillonnées à pas variable. Le retard n’est pas contraint à être un multiple de la période d’échantillonnage, contrairement à l’hypothèse traditionnelle dans le cas de modèles à temps discret. L’estimateur développé est de type bootstrap et combine la méthode de variable instrumentale itérative pour les paramètres de la fonction de transfert avec un algorithme numérique de type gradient pour estimer le retard. Un filtrage de type passe-bas est introduit pour élargir la région de convergence pour l’estimation du retard. Tous les estimateurs proposés sont inclus dans la boîte à outils logicielle CONTSID pour Matlab et sont évalués à l’aide de simulation de Monte-Carlo / The output of a system is always corrupted by additive noise, therefore it is more practical to develop estimation algorithms that are capable of handling noisy data. The effect of white additive noise has been widely studied, while a colored additive noise attracts less attention, especially for a continuous-time (CT) noise. Sampling issues of CT stochastic processes are reviewed in this thesis, several sampling schemes are presented. Estimation of a CT stochastic process is studied. An expectation-maximization-based (EM) method to CT autoregressive/autoregressive moving average model is developed, which gives accurate estimation over a large range of sampling interval. Estimation of CT Box-Jenkins models is also considered in this thesis, in which the noise part is modeled to improve the performance of plant model estimation. The proposed method for CT Box-Jenkins model identification is in a two-step and iterative framework. Two-step means the plant and noise models are estimated in a separate and alternate way, where in estimating each of them, the other is assumed to be fixed. More specifically, the plant is estimated by refined instrumental variable (RIV) method while the noise is estimated by EM algorithm. Iterative means that the proposed method repeats the estimation procedure several times until a optimal estimate is found. Many practical systems have inherent time-delay. The problem of identifying delayed systems are of great importance for analysis, prediction or control design. The presence of a unknown time-delay greatly complicates the parameter estimation problem, essentially because the model are not linear with respect to the time-delay. An approach to continuous-time model identification of time-delay systems, combining a numerical search algorithm for the delay with the RIV method for the dynamic has been developed in this thesis. In the proposed algorithm, the system parameters and time-delay are estimated reciprocally in a bootstrap manner. The time-delay is estimated by an adaptive gradient-based method, whereas the system parameters are estimated by the RIV method. Since numerical method is used in this algorithm, the bootstrap method is likely to converge to local optima, therefore a low-pass filter has been used to enlarge the convergence region for the time-delay. The performance of the proposed algorithms are evaluated by numerical examples