This thesis deals with the use of statistical state space models of exponential smooth- ing for estimating the conditional probability distribution of future values of time series. This knowledge allows calculation of interval predictions, not only point forecasts. Meth- ods of exponential smoothing are described and set into the context of state space models. Analytical and simulation methods used in the calculation of interval predictions are presented, in particular simulations based on assumption of normality, bootstrap method or estimated parametric model. The methods are applied to simulated as well as real data and their results are compared. 1

No description available.

[pt] Nesse artigo aplicamos a técnica de estimação dos nowcasts apresentada por Giannone, Reichlin e Small (2008), para o PIB e inflação brasileiros. Extraímos informações de um elevado número de variáveis e produzimos modelos capazes de informar contemporaneamente uma medida para as variáveis em questão. Em posse dessa leitura cotidiana, produzida por esses modelos, estimamos uma regra de Taylor diária para o Banco Central do Brasil (BCB), o que permitiu melhor identificar choques monetários e alterações na função de reação do BCB ao longo do tempo. Concluímos, primeiramente, que os modelos nowcasts apresentam acurácia comparável às previsões do relatório Focus do BCB. Segundo, 2 (duas) comparações históricas realizadas mostraram indícios que nossa proxy para choques monetários diários está relacionada às decisões explícitas de política monetária. Por fim, encontramos evidências que os modelos nowcasts puderam capturar grande parte da informação relevante para a determinação da taxa de juros de curto prazo, o que deveria estimular a aplicação de tais modelos nos processos decisórios públicos e privados. / [en] In this article we apply the two-steps nowcasting method, described in Giannone, Reichlin, and Small (2008), to build nowcast models for Brazilian GDP and inflation. Throught the application of this method, we could extract information from a large data-set and build models which could be used to produce a daily measurement of GDP and inflation. Using this measurement was possible to build a daily Taylor rule for the Brazilian Central Bank (BCB). This new application of nowcast models allowed us to extract a daily measurement of monetary shocks. Our study produced three main findings. First, the nowcast model showed an accuracy close to projections presented in the Focus survey. Second, we identified by historical comparison that the monetary shocks proxy, measured by the differences between the daily Taylor rule and the movements in the short-term interest rate, are related with unanticipated monetary policies decisions. Finally, nowcasts were able to capture a great part of relevant information to determine the short-term interest rate, which should stimulate the policymakers and financial markets members to apply those models.

Abstract Undoubtedly, liquidity is one of the most popular topics of research among the academia for decades. However intuitively-clear it is, scholars and experts have always found it not only hard but vague to define and measure. Moreover, researches or methods concerning commonality in liquidity are proposed one after another. Most of these works attempt to document what lies beneath the commonality by offering industry-wide or market-wide explanations. Nevertheless, this paper adopts an exact multivariate model-based structural decomposition methodology developed by Casals, Jerez and Sotoca (2002) to analyze the co-movement in market liquidity measures in a totally different manner. Except for decomposing three well-known market liquidity measures, share volume, dollar volume and turnover rate, of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) into trend, cycle, seasonal and irregular components, we conduct advanced bivariate analysis to extract common components, visualize them, and make a comparison among them at last. Evidence suggests that not only do these three liquidity proxies highly co-move with one another, but dollar volume seems to co-move slightly closer with share volume than with turnover rate. In the end, where this phenomenon, co-movement in market liquidity measures, accrues from is another long story and needs some further work not covered in this study.

Human sensorimotor control is remarkably adept at utilising contextual information to learn and recall systematic sensorimotor transformations. Here, we investigate the motor representations that underlie such learning, and examine how motor memories acquired based on different contextual information interact. Using a novel three-dimensional robotic manipulandum, the 3BOT, we examined the spatial transfer of learning across various movement directions in a 3D environment, while human subjects performed reaching movements under velocity-dependent force field. The obtained pattern of generalisation suggested that the representation of dynamic learning was most likely defined in a target-based, rather than an extrinsic, coordinate system. We further examined how motor memories interact when subjects adapt to force fields applied in orthogonal dimensions. We found that, unlike opposing fields, learning two spatially orthogonal force fields led to the formation of separate motor memories, which neither interfered with nor facilitated each other. Moreover, we demonstrated a novel, more general aspect of the spontaneous recovery phenomenon using a two-dimensional force field task: when subjects learned two orthogonal force fields consecutively, in the following phase of clamped error feedback, the expression of adaptation spontaneously rotated from the direction of the second force field, towards the direction of the first force field. Finally, we examined the interaction of sensorimotor memories formed based on separate contextual information. Subjects performed reciprocating reaching and object manipulation tasks under two alternating contexts (movement directions), while we manipulated the dynamics of the task in each context separately. The results suggested that separate motor memories were formed for the dynamics of the task in different contexts, and that these motor memories interacted by sharing error signals to enhance learning. Importantly, the extent of interaction was not fixed between the context-dependent motor memories, but adaptively changed according to the task dynamics to potentially improve overall performance. Together, our experimental and theoretical results add to the understanding of mechanisms that underlie sensorimotor learning, and the way these mechanisms interact under various tasks and different dynamics.

Considerando a importância de uma medida de alta frequência para o PIB do Rio Grande do Sul, o principal indicador de atividade econômica do estado, este trabalho foi dividido em três objetivos. O primeiro foi a estimação de uma série com frequência mensal para o PIB real do Rio Grande do Sul entre janeiro de 2002 e março de 2017, dado que o mesmo só é contabilizado em frequência trimestral. Para tanto, foi utilizado um modelo em espaço de estados que permite a estimação e nowcast do PIB mensal, utilizando séries coincidentes como fonte de informação para a interpolação dos dados trimestrais do PIB, em linha com Bernanke, Gertler e Watson (1997), Mönch e Uhlig (2005) e Issler e Notini (2016). O segundo objetivo foi comparar a série estimada com um indicador de atividade calculado pelo Banco Central do Brasil para o estado, o Índice de Atividade Econômica Regional (IBCR-RS), tanto em termos metodológicos como na capacidade em antecipar as variações do PIB trimestral antes de sua divulgação (nowcasting). O terceiro objetivo foi estabelecer a cronologia dos ciclos de expansão e recessão da economia gaúcha com o uso do algoritmo de Bry e Boschan (1971). Após a etapa de seleção das séries coincidentes e da estimação de diversos modelos de interpolação, foi escolhido para gerar a série mensal do PIB o modelo que utiliza somente a produção industrial como variável auxiliar, tendo este apresentado o melhor ajuste. A comparação do PIB mensal interpolado com o IBCR-RS mostrou que, além da vantagem computacional a favor do método proposto neste trabalho, a imposição da disciplina de que as variações do PIB mensal estimado devem ser exatamente iguais às do PIB trimestral faz com que a dinâmica de curto e longo prazo das variáveis sejam idênticas, o que não ocorre com o IBCR-RS. A cronologia dos pontos de inflexão da atividade econômica apontou três períodos recessivos na economia gaúcha desde janeiro de 2002: jun/2003 a abr/2005 (23 meses e queda acumulada de 8,79%); abr/2011 a abr/2012 (13 meses e queda acumulada de 9,47%); e jun/2013 a nov/2016 (42 meses e queda acumulada de 10,41%), sendo o encerramento deste último apontado somente com a inclusão dos resultados estimados pelo modelo para o segundo trimestre de 2017. Finalmente, os resultados do exercício de nowcasting do PIB mostraram desempenho superior do método proposto frente ao IBCR-RS em termos de antecipação do resultado do PIB de um trimestre a frente, tomando como base as medidas de MAE (erro absoluto médio, em inglês) e MSE (erro quadrático médio, em inglês), comumente usadas nesse intuito. / Giving the importance of a high frequency measure for Rio Grande do Sul’s GDP, the main indicator of economic activity of the state, this work was divided into three objectives. The first one was the estimation of monthly frequency series for Rio Grande do Sul’s real GDP between January/2002 and March/2017, since it is only accounted in quarterly basis. Therefore, we used a State-Space model that enables to estimate and nowcast the monthly GDP, using coincident series as a source of information for the interpolation of quarterly GDP data, in line with Bernanke, Gertler e Watson (1997), Mönch e Uhlig (2005) and Issler e Notini (2016). The second objective was to compare the estimated series with an activity indicator calculated by the Central Bank of Brazil for the state, the Regional Economic Activity Index (IBCR-RS), both in methodological terms and in the capability to anticipate the quarterly GDP release (nowcasting). The third objective was to establish the chronology of the cycles of expansion and recession of the economy of Rio Grande do Sul using the algorithm of Bry e Boschan (1971). After the selection of the coincident series and the estimation of several interpolation models, the chosen model to generate the monthly GDP series uses only the industrial production as an auxiliary variable, and this one presented the best fit. The comparison of the monthly GDP interpolated with the IBCR-RS showed that, in addition to the computational advantage in favor of the method proposed in this work, the imposition of the discipline that the estimated monthly GDP changes must be exactly the same as the quarterly GDP makes the short-term and long-term dynamics of the variables are identical, which is not the case with IBCR-RS. The chronology of the turning points of the economic activity pointed to three recessive periods in the economy of Rio Grande do Sul since January 2002: June/2003 to April/2005 (23 months and accumulated drop of 8.79%); April/2011 to April/2012 (13 months and accumulated fall of 9.47%); and June/2013 to November/2016 (42 months and 10.41% accumulated decrease), with the latter one closing only with the inclusion of the results estimated by the model for the second quarter of 2017. Finally, results for GDP’s nowcasting showed superior performance of the proposed method compared to the IBCR-RS in terms of anticipating quarter-to-quarter GDP results, based on the measures of MAE (absolute mean error) and MSE (mean square error), commonly used for this purpose.

<p>Diss. (sammanfattning) Stockholm : Handelshögskolan, 2006 [6], xiii, [1] s.: sammanfattning, s. 1-259, [5] s.: 4 uppsatser. Spikblad saknas</p>

The main topic of the paper is on-line filtering for non-Gaussian dynamic (state space) models by approximate computation of the first two posterior moments using efficient numerical integration. Based on approximating the prior of the state vector by a normal density, we prove that the posterior moments of the state vector are related to the posterior moments of the linear predictor in a simple way. For the linear predictor Gauss-Hermite integration is carried out with automatic reparametrization based on an approximate posterior mode filter. We illustrate how further topics in applied state space modelling such as estimating hyperparameters, computing model likelihoods and predictive residuals, are managed by integration-based Kalman-filtering. The methodology derived in the paper is applied to on-line monitoring of ecological time series and filtering for small count data. (author's abstract) / Series: Forschungsberichte / Institut für Statistik

The topic of the paper is filtering for non-Gaussian dynamic (state space) models by approximate computation of posterior moments using numerical integration. A Gauss-Hermite procedure is implemented based on the approximate posterior mode estimator and curvature recently proposed in 121. This integration-based filtering method will be illustrated by a dynamic trend model for non-Gaussian time series. Comparision of the proposed method with other approximations ([15], [2]) is carried out by simulation experiments for time series from Poisson, exponential and Gamma distributions. (author's abstract) / Series: Forschungsberichte / Institut für Statistik

This thesis explores the methodology of state, and in particular, parameter estimation for time series datasets. Various approaches are investigated that are suitable for nonlinear models and non-Gaussian observations using state space models. The methodologies are applied to a dataset consisting of the historical lynx and hare populations, typically modeled by the Lotka- Volterra equations. With this model and the observed dataset, particle filtering and parameter estimation methods are implemented as a way to better predict the state of the system. Methods for parameter estimation considered include: maximum likelihood estimation, state augmented particle filtering, multiple iterative filtering and particle Markov chain Monte Carlo (PMCMC) methods. The specific advantages and disadvantages for each technique are discussed. However, in most cases, PMCMC is the preferred parameter estimation solution. It has the advantage over other approaches in that it can well approximate any posterior distribution from which inference can be made. / Master's thesis