O setor agrícola é, historicamente, um dos pilares da economia brasileira, e apesar de ter sua importância diminuída com o desenvolvimento do setor industrial e de serviços ainda é responsável por dar dinamismo econômico ao país, bem como garantir a segurança alimentar, auxiliar no controle da inflação e na formação de reservas monetárias. Neste contexto as safras agrícolas exercem grande influência no comportamento do setor e equilíbrio no mercado agrícola. Foram desenvolvidas diversas metodologias de previsão de safra, sendo em sua maioria modelos de simulação de crescimento. Entretanto, recentemente os modelos estatísticos vem sendo utilizados mais comumente devido às suas predições mais rápidas em períodos anteriores à colheita. No presente trabalho foram avaliadas duas destas metodologias, os modelos ARIMA e os Modelos Lineares Dinâmicos (MLD), sendo utilizada tanto a inferência clássica quanto a bayesiana. A avaliação das metodologias deu-se por meio da análise das previsões dos modelos, bem como da facilidade de implementação e poder computacional necessário. As metodologias foram aplicadas a dados de produção de soja para o município de Mamborê-PR, no período de 1980 a 2013, sendo área plantada (ha) e precipitação acumulada (mm) variáveis auxiliares nos modelos de regressão dinâmica. Observou-se que o modelo ARIMA (2,1,0) reparametrizado na forma de um MLD e estimado por meio de máxima verossimilhança, gerou melhores previsões do que aquelas obtidas com o modelo ARIMA(2,1,0) não reparametrizado. / The agriculture is, historically, one of Brazil\'s economic pillars, and despite having it\'s importance diminished with the development of the industry and services it still is responsible for giving dynamism to the country inland\'s economy, ensuring food security, controlling inflation and assisting in the formation of monetary reserves. In this context the agricultural crops exercise great influence in the behaviour of the sector and agricultural market balance. Diverse crop forecast methods were developed, most of them being growth simulation models, however, recently the statistical models are being used due to its capability of forecasting early when compared to the other models. In the present thesis two of these methologies were evaluated, ARIMA and Dynamic Linear Models, utilizing both classical and bayesian inference. The forecast accuracy, difficulties in the implementation and computational power were some of the caracteristics utilized to assess model efficiency. The methodologies were applied to Soy production data of Mamborê-PR, in the 1980-2013 period, also noting that planted area (ha) and cumulative precipitation (mm) were auxiliary variables in the dynamic regression. The ARIMA(2,1,0) reparametrized in the DLM form and adjusted through maximum likelihood generated the best forecasts, folowed by the ARIMA(2,1,0) without reparametrization.

Fatores exógenos, como fotoperíodo, temperatura da superfície do mar e abundância de presas, e endógenos, como os ciclos circadianos e circanuais e alterações metabólicas são conhecidos como iniciadores dos movimentos migratórios. Este trabalho tem como objetivo estabelecer os principais parâmetros iniciadores da migração das baleias-jubarte. Foram analisados o fotoperíodo, índice de oscilação do oceano austral (SOI), temperatura da superfície do mar, concentração de clorofila-a e densidade de krill em relação ao número máximo de indivíduos avistados e o dia do pico de avistagem. O fotoperíodo mostrou ser o principal fator que influencia a migração da Antártica em direção a Abrolhos, enquanto que o caminho contrário, além de fotoperíodo, parece ser influenciado também pelo os fatores tais como temperatura da superfície do mar e a quantidade de presas disponíveis no verão anterior. Quanto maior a densidade de krill, maior o número máximo de indivíduos avistados e a temporada reprodutiva mais longa. O SOI mostrou ter influência no ciclo reprodutivo do krill. Valores negativos registraram maior densidade de krill e valores positivos, menor densidade de krill, através do modelo GLM. Altos valores de TSM apresentaram correlação negativa com a densidade de krill, e com o número de baleias avistadas e o tempo de permanência na área reprodutiva, indicando que o aquecimento da região antártica impõe condições não favoráveis para a temporada reprodutiva das baleias. / Exogenous factors, such as photoperiod, sea surface temperature and abundance of prey, and endogenous, such circadian and circannual cycles and metabolic changes are known as initiators of migratory movements. This work aims to establish the main parameters initiators of the migration of the humpback whales. The photoperiod, the Southern Ocean Index (SOI), the sea surface temperature, the chlorophyll-a concentration and the density of krill were analyzed in relation to the maximum number of individuals sighted and the duration of the reproductive season. The photoperiod showed to be the main factor that influences the migration from Antarctica to Abrolhos, while the opposite way, besides photoperiod, seemed to be influenced also by other factors such as sea surface temperature and the amount of prey available in the previous summer. The higher the density of krill, the greater the maximum number of individuals sighted and the longer the reproductive season. The SOI showed to have influence on the reproductive cycle of krill. Negative values correspond to higher density of krill, and positive values, lower density of krill, through the GLM model. High values of TSM presented negative correlation with the density of krill, and with the number of whales sighted and the reproductive season duration in the reproductive area, indicating that the Antartic warming impose unfavorable conditions for the reproductive season of whales.

A fase mais difícil e que exige mais recursos em um programa de melhoramento de milho é a avaliação experimental dos híbridos, pois geralmente um elevado número de híbridos necessita ser avaliado em diversos ambientes. Deste modo, tanto o número de híbridos como o de ambientes são limitados pelos recursos disponíveis, o que poderia levar a uma redução do número de ambientes, e, portanto, conjuntos de híbridos comumente são avaliados em diferentes ambientes levando a comparações desbalanceadas entre os híbridos. A metodologia estatística conhecida como REM/BLUP tem sido amplamente utilizada no melhoramento animal, mas nos programas de melhoramento vegetal a sua utilização tem sido restrita a culturas perenes, onde experimentos desbalanceados são comuns. Há pouca informação na literatura sobre a confiabilidade do método REML/BLUP utilizando dados experimentais para a predição de valores genotípicos sob experimentos desbalanceados para programas de melhoramento de culturas anuais. Assim, o objetivo desta pesquisa foi avaliar se o método REML/BLUP poderia ser útil para predizer os valores genotípicos de híbridos simples de milho sob situações de desbalanceamento. Um conjunto de 256 híbridos simples foi avaliado em delineamento látice 16 x 16 com duas repetições por ambiente em 13 ambientes e as características analisadas foram produção de grãos, altura da planta e acamamento de plantas. Uma vez que a avaliação constou de 26 observações para cada híbrido simples, suas médias gerais ajustadas computadas pelo método dos quadrados mínimos foram consideradas como seus valores genotípicos, para fins de comparações com as predições dos valores genotípicos pelo método REML/BLUP. As predições dos híbridos simples foram computadas pelo método REML/BLUP considerando conjuntos desbalanceados de híbridos dentro de ambientes e perdas completas dos dados de ambientes. Os dados foram submetidos a um desbalanceamento aleatório e cada situação foi simulada 1.000 vezes utilizando o método bootstrap. Foram computados coeficientes de correlação entre os valores genotípicos preditos e as médias gerais ajustadas, e seus valores foram elevados ao quadrado para obter os valores de R2; assim 1.000 valores de R2 foram obtidos para cada situação considerada. Além disso, foi praticada seleção utilizando os valores genotípicos preditos e as médias gerais ajustadas dos híbridos simples e as percentagens de coincidência foram computadas. Independentemente do caráter analisado, os valores de R2 e o percentual de coincidência dos híbridos simples selecionados mostrou que o REML/BLUP prediz com alta acurácia os valores genotípicos dos híbridos simples com até 20% das perdas de híbridos dentro de ambientes ou com redução de até 23% dos ambientes. Nota-se que o caráter produção de grãos apresentou interação genótipos x ambientes significativa e complexa, e mesmo assim o método REML/BLUP fez a predição dos valores genotípicos com alta acurácia. Deste modo, o método REML/BLUP poderia ser considerado como uma valiosa ferramenta no melhoramento genético de milho para predizer os valores genotípicos dos híbridos sob dados desbalanceados. Entretanto, os resultados também apontaram que há um limite para a sua acurácia, o qual corresponde a cerca de 20% dos dados desbalanceados. / The more difficult phase and that demands more funding in a maize breeding program is the experimental evaluation of the hybrids, because usually a high number of hybrids needs to be evaluated in several environments. Then, both the number of environments and hybrids are limited by the resources available, which could lead to a reduction in the number of environments, and therefore, sets of hybrids are commonly tested in different environments leading to unbalanced comparisons among the hybrids. The statistical methodology known as REM/BLUP has been widely used in animal breeding, but in plant breeding programs its use has been restricted to perennial crops where unbalanced experiments are very common. There is limited information about the reliability of the REM/BLUP method using experimental data for the genotypic values prediction under unbalanced experiments for annual crops breeding programs. Thus, the objective of this research was to assess whether the REM/BLUP method could be useful to predict the genotypic values of maize single-crosses under unbalanced situations. A set of 256 single-crosses was evaluated in a 16 x 16 lattice design with two replications per environment in 13 environments, and the traits analyzed were grain yield, plant lodging and plant height. As the evaluation consisted of 26 observations for each single-cross, their adjusted overall means computed by the least squares method were considered as their genotypic values for the sake of comparisons with the genotypic predictions by REM/BLUP method. The predictions of the single-crosses were computed considering unbalanced sets of hybrids within environments and unbalanced sets of environments. The data were submitted to a random unbalance and each situation was simulated 1,000 times using the bootstrap method. Coefficients of correlation were then computed between the predicted genotypic values and the adjusted overall means, and their values were squared to obtain the R2 values; thus 1,000 R2 values were obtained for each considered situation. Also, selection were performed using the predict values and the adjusted overall means of the single-crosses, and the percentage of coincidence were computed. Regardless of the trait analyzed, the R2 values and the percentage of coincidence of the selected single-crosses showed that the REM/BLUP predict with high accuracy the genotypic values of the single-crosses up to 20% of losses of hybrids within environments and up to 23% of environments reduction. It should be noted that grain yield showed a significant cross-over interaction, and even so the REM/BLUP predicted the genotypic values of the hybrids with high accuracy. Thus, the REM/BLUP method can be considered as a valuable tool in maize breeding programs to predict the genotypic values of the hybrids under unbalanced data. However, the results also pointed out that there is a limit for its accuracy, which is around 20% of unbalanced data.

多項式回歸模型可用來模擬賽馬過程。不同研究者對模型中馬匹的效用的分佈採取不同的假設，包括指數分佈，它與Harville 模型（Harville, 1973）相同，伽馬分佈（Stern, 1990）和正態分佈（Henery, 1981）。Harville 模型無法模擬賽馬過程中競爭第二位和第三位等非冠軍位置時增加的隨機性（Benter, 1994）。Stern 模型假設效用服從形狀參數大於一的伽馬分佈，Henery 模型假設效用服從正態分佈。Bacon-Shone，Lo 和 Busche（1992），Lo 和 Bacon-Shone（1994）和 Lo（1994）研究證明了相較於Harville 模型，這兩個模型能更好地模擬賽馬過程。本文利用賽馬歷史數據，採用貝葉斯方法對賽馬結果中馬匹勝出的概率進行預測。本文假設效用服從伽馬分佈。本文針對多項式回歸模型，提出一個在Metropolis-Hastings 抽樣方法中選擇提議分佈的簡便方法。此方法由Scott（2008）首次提出。我們在似然函數中加入服從伽馬分佈的效用作為潛變量。通過將服從伽馬分佈的效用變換成一個服從Mihram（1975）所描述的廣義極值分佈的隨機變量，我們得到一個線性回歸模型。由此線性模型我們可得到最小二乘估計，本文亦討論最小二乘估計的漸進抽樣分佈。我們利用此估計的方差得到Metropolis-Hastings 抽樣方法中的提議分佈。最後，我們可以得到回歸參數的後驗分佈樣本。本文用香港賽馬數據做模擬賽馬投資以檢驗本文提出的估計方法。 / In multinomial regression of racetrack betting, dierent distributions of utilities have been proposed: exponential distribution which is equivalent to Harville’s model (Harville, 1973), gamma distribution (Stern, 1990) and normal distribution (Henery, 1981). Harville’s model has the drawback that it ignores the increasing randomness of the competitions for the second and third place (Benter, 1994). The Stern’s model using gamma utilities with shape parameter greater than 1 and the Henery’s model using normal utilities have been shown to produce a better t (Bacon-Shone, Lo and Busche, 1992; Lo and Bacon-Shone, 1994; Lo, 1994). In this thesis, we use the Bayesian methodology to provide prediction on the winning probabilities of horses with the historical observed data. The gamma utility is adopted throughout the thesis. In this thesis, a convenient method of selecting Metropolis-Hastings proposal distributions for multinomial models is developed. A similar method is rst exploited by Scott (2008). We augment the gamma distributed utilities in the likelihood as latent variables. The gamma utility is transformed to a variable that follows generalized extreme value distribution described by Mihram (1975) through which we get a linear regression model. Least squares estimate of the parameters is easily obtained from this linear model. The asymptotic sampling distribution of the least squares estimate is discussed. The Metropolis-Hastings proposal distribution is generated conditioning on the variance of the estimator. Finally, samples from the posterior distribution of regression parameters are obtained. The proposed method is tested through betting simulations using data from Hong Kong horse racing market. / Detailed summary in vernacular field only. / Xu, Wenjun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 46-48). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Hong Kong Horse Racing Market and Models in Horse Racing --- p.4 / Chapter 2.1 --- Hong Kong Horse Racing Market --- p.4 / Chapter 2.2 --- Models in Horse Racing --- p.6 / Chapter 3 --- Metropolis-Hastings Algorithm in Multinomial Regression with Gamma Utilities --- p.10 / Chapter 3.1 --- Notations and Posterior Distribution --- p.10 / Chapter 3.2 --- Metropolis-Hastings Algorithm --- p.11 / Chapter 4 --- Application --- p.15 / Chapter 4.1 --- Variables --- p.16 / Chapter 4.2 --- Markov Chain Simulation --- p.17 / Chapter 4.3 --- Model Selection --- p.27 / Chapter 4.4 --- Estimation Result --- p.31 / Chapter 4.5 --- Betting Strategies and Comparisons --- p.33 / Chapter 5 --- Conclusion --- p.41 / Appendix A --- p.43 / Appendix B --- p.44 / Bibliography --- p.46

Para dados provenientes de bioensaios entomol´ogicos, na maioria das vezes, ´e necess´ario levar em considera¸cao a ocorrencia de mortalidade natural e a superdispers ao. Para incorporar a mortalidade natural, pode-se utilizar a f´ormula de Abbott, que associada ao modelo binomial, caracteriza o modelo padrao de mortalidade natural. Modelos padroes de superdispersao incluem os modelos beta-binomial, log´stico normal, misturas discretas e o uso do fator de heterogeneidade. Como alternativa aos modelos padrao de mortalidade natural, e de mortalidade natural com o fator de heterogeneidade, foi desenvolvido o modelo de mortalidade natural com a inclusao de um efeito aleat´orio no preditor linear, para melhor acomodar a superdispersao. Para obter as estimativas dos parametros desse novo modelo, foram usados os algoritmos de Newton Raphson e EM. Para a verifica¸cao dos ajustes dos modelos foram usados gr´aficos semi-normais de probabilidade com envelopes de simula¸cao, e para realizar a compara¸cao entre os modelos foram utilizados o teste da razao de verossimilhan¸cas e o crit´eiro AIC. A seguir, foram calculadas as estimativas das doses efetivas. Os procedimentos foram todos implementados no software R. Como aplica¸cao, foram analisados tres conjuntos de dados, provenientes de ensaios entomol´ogicos. Para os tres conjuntos de dados, concluiu-se que o modelo de mortalidade natural com efeito aleat´orio ´e superior aos procedimentos padroes, geralmente, utilizados. / When fitting dose-response models to entomological data it is often necessary to take account of natural mortality and/or overdispersion. The standard approach to handle natural mortality is to use Abbotts formula, which allows for a constant underlying mortality rate. Standard overdispersion models include beta-binomial models, logistic-normal, discrete mixtures and the use of the heterogeneity factor. We extend the standard natural mortality model and include a random effect to handle the overdispersion. To obtain the parameters estimates of this new model, two algorithms were used: the Newton Raphson and the EM. For the application, were used three data sets. We introduce the likelihood ratio test, effective dose, and simulated envelope for the natural mortality model with a random effect. The procedures are implemented in the R system. For the three the data sets studied, a significant further improvement in the fit is possible by using the random-effect model.

Fatores exógenos, como fotoperíodo, temperatura da superfície do mar e abundância de presas, e endógenos, como os ciclos circadianos e circanuais e alterações metabólicas são conhecidos como iniciadores dos movimentos migratórios. Este trabalho tem como objetivo estabelecer os principais parâmetros iniciadores da migração das baleias-jubarte. Foram analisados o fotoperíodo, índice de oscilação do oceano austral (SOI), temperatura da superfície do mar, concentração de clorofila-a e densidade de krill em relação ao número máximo de indivíduos avistados e o dia do pico de avistagem. O fotoperíodo mostrou ser o principal fator que influencia a migração da Antártica em direção a Abrolhos, enquanto que o caminho contrário, além de fotoperíodo, parece ser influenciado também pelo os fatores tais como temperatura da superfície do mar e a quantidade de presas disponíveis no verão anterior. Quanto maior a densidade de krill, maior o número máximo de indivíduos avistados e a temporada reprodutiva mais longa. O SOI mostrou ter influência no ciclo reprodutivo do krill. Valores negativos registraram maior densidade de krill e valores positivos, menor densidade de krill, através do modelo GLM. Altos valores de TSM apresentaram correlação negativa com a densidade de krill, e com o número de baleias avistadas e o tempo de permanência na área reprodutiva, indicando que o aquecimento da região antártica impõe condições não favoráveis para a temporada reprodutiva das baleias. / Exogenous factors, such as photoperiod, sea surface temperature and abundance of prey, and endogenous, such circadian and circannual cycles and metabolic changes are known as initiators of migratory movements. This work aims to establish the main parameters initiators of the migration of the humpback whales. The photoperiod, the Southern Ocean Index (SOI), the sea surface temperature, the chlorophyll-a concentration and the density of krill were analyzed in relation to the maximum number of individuals sighted and the duration of the reproductive season. The photoperiod showed to be the main factor that influences the migration from Antarctica to Abrolhos, while the opposite way, besides photoperiod, seemed to be influenced also by other factors such as sea surface temperature and the amount of prey available in the previous summer. The higher the density of krill, the greater the maximum number of individuals sighted and the longer the reproductive season. The SOI showed to have influence on the reproductive cycle of krill. Negative values correspond to higher density of krill, and positive values, lower density of krill, through the GLM model. High values of TSM presented negative correlation with the density of krill, and with the number of whales sighted and the reproductive season duration in the reproductive area, indicating that the Antartic warming impose unfavorable conditions for the reproductive season of whales.

Although much emphasis has been given to the analysis of continuous forest conversion in tropical regions, our understanding in detecting, mapping and interpreting the spatial pattern of woodland deforestation and degradation is still limited. This thesis focuses on two factors contributing to this limitation: uncertainties in retrieving woodland change from remote sensing imagery, and the complex processes that may cause woodland deforestation and degradation. Firstly, I investigate approaches to minimising uncertainty in ALOS PALSAR-derived biomass maps by modifying a widely used processing chain, with the aim of provide recommendations for producing radar-based biomass maps with reduced uncertainty. Secondly, to further improve the retrieval of woody biomass from ALOS PALSAR imagery, the semi-empirical Water Cloud Model (WCM) is introduced to account for backscattering from soil. In wooded areas with low canopy (such as the miombo woodland which dominates the study area) the effect from soil moisture on the received backscattered signal is critical. Thirdly, based on the biomass maps retrieved from the refined radar-remote-sensing-based methodology discussed above, the influence of driving variables of the woodland deforestation and degradation, and how they alter the spatial patterns of these two processes, are analysed. The threshold for defining woodland deforestation and degradation in terms of biomass loss intensity is generated through integration of radar-based biomass loss maps, an optical forest cover change map and fieldwork investigation. Multi-linear model simulations of the spatial variation of deforestation and degradation events were constructed at a district and 1 km resolution respectively to rank the relative importance of driving variables. Results suggest that biomass-backscatter relationships based on plots of approximately 1 ha, and processed with high resolution DEMs, are needed for low uncertainty biomass maps using ALOS PALSAR data. Although plots sizes of 0.1 - 0.5 ha lead to large uncertainties, aggregating 0.1 ha plots into larger calibration sites shows some promise even in hilly terrain, potentially opening up the use of common forest inventory data to calibrate remote-sensing-based biomass retrieval models. Such relationships appear to hold across the miombo woodland ecoregion, which implies that there is a consistent relationship at least in the miombo woodland. From this I infer that random error, different processing methods and fitting techniques, and data from small plots are the source of the differences in the savanna biomass-backscatter relationships seen in the literature. The interpreted WCM presented in this study for L-band backscatter at HV polarisation improves biomass retrieval for areas with a biomass value less than 15 tC/ha (or 0.025 m2/m2 in backscatter). Use of the WCM also results in better quality regional biomass mosaics. This is because the WCM helped to improve the correlation of biomass estimation for overlay areas by reducing bias between adjacent paths, especially the bias introduced by changes in soil moisture conditions between different acquisition dates for different paths. Result shows that active and combined soil moisture datasets (from the Climate Change Initiative Soil Moisture Dataset) can be used as effective soil moisture proxies in the WCM for biomass retrieval. This quantitative analysis on the driving variables of woodland deforestation and degradation suggests that large uncertainty exists in modelling the occurrence of deforestation and degradation, especially at a 1 km scale. The spatial patterns of woodland deforestation and degradation differ in terms of shape, size, intensity, and location. Agriculture-related driving variables account for most of the explained variance in deforestation, whereas for degradation, distance to settlements also plays an important role. Deforestation happens regardless of the original biomass levels, while degradation is likely to happen at high biomass areas. The sizes of degradation events are significantly smaller than those of deforestation events, with 90% of deforestation events sharing boundaries with degradation events. This thesis concludes by outlining the importance and difficulties in integrating 'distal' (underlying) drivers in modelling the spatial dynamics of deforestation and degradation. Further work on the causal connection between deforestation and degradation is also needed. The processing chain and biomass retrieval models presented in this study could be used to support monitoring and analysis of biomass change elsewhere in the tropics, and should be compatible with data derived from ALOS-2 and the future SAOCOM and BIOMASS satellite missions.

<p>The purpose of this report is to evaluate different methods for identifying states in robot models. Both linear and non-linear filters exist among these methods and are compared to each other. Advantages, disadvantages and problems that can occur during tuning and running are presented. Additional measurements from accelerometers are added and their use with above mentioned methods for state estimation is evaluated. The evaluation of methods in this report is mainly based on simulations in Matlab, even though some experiments have been performed on laboratory equipment. </p><p>The conclusion indicates that simple non-linear models with few states can be more accurately estimated with a Kalman filter than with an extended Kalman filter, as long as only linear measurements are used. When non-linear measurements are used an extended Kalman filteris more accurate than a Kalman filter. Non-linear measurements are introduced through accelerometers with non-linear measurement equations. Using accelerometers generally leads to better state estimation when the measure equations have a simple relation to the model.</p>

The world went through weeks of financial turbulence in stock markets and investors were overcome by fears fuelled by more bad news, while countries continued their attempts to calm the markets with more injection of funds. By these very disturbed times, even if traders hope extreme risk aversion has passed, an investor would like predict the future of the market in order to protect his portfolio and a speculator would like to optimize his tradings. This thesis describes the design of numerical models and algorithms for the forecasting of financial time series, for speculation on a short time interval. To this aim, we will use two models: - " Price Forecasting Model " forecasts the behavior of an asset for an interval of three hours. This model is based on Functional Clustering and smoothing by cubic-splines in the training phase to build local Neural models, and Functional Classification for generalization, - " Model of Trading " forecasts the First Stopping time, when an asset crosses for the first time a threshold defined by the trader. This model combines a Price Forecasting Model for the prediction of market trend, and a Trading Recommendation for prediction of the first stopping time. We use an auto-adaptive Dynamic State Space Model, with Particle Filters and Kalman-Bucy Filters for parameter estimation.

Among the parametric nonlinear time series model families, the smooth transition regression (STR) model has recently received attention in the literature. The considerations in this dissertation focus on the univariate special case of this model, the smooth transition autoregression (STAR) model, although large parts of the discussion can be easily generalised to the more general STR case. Many nonlinear univariate time series models can be described as consisting of a number of regimes, each one corresponding to a linear autoregressive parametrisation, between which the process switches. In the STAR models, as opposed to certain other popular models involving multiple regimes, the transition between the extreme regimes is smooth and assumed to be characterised by a bounded continuous function of a transition variable. The transition variable, in turn, may be a lagged value of the variable in the model, or another stochastic or deterministic observable variable. A number of other commonly discussed nonlinear autoregressive models can be viewed as special or limiting cases of the STAR model. The applications presented in the first two chapters of this dissertation, Chapter I: Another look at Swedish Business Cycles, 1861-1988 Chapter II: Modelling asymmetries and moving equilibria in unemployment rates, make use of STAR models. In these two studies, STAR models are used to provide insight into dynamic properties of the time series which cannot be be properly characterised by linear time series models, and which thereby may be obscured by estimating only a linear model in cases where linearity would be rejected if tested. The applications being of interest in their own right, an important common objective of these two chapters is also to develop, suggest, and give examples of various methods that may be of use in discussing the dynamic properties of estimated STAR models in general.Chapter III, Testing linearity against smooth transition autoregression using a parametric bootstrap, reports the result of a small simulation study considering a new test of linearity against STAR based on bootstrap methodology. / <p>Diss. Stockholm : Handelshögskolan, 1999</p>