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Akgun, Burcin. "Identification Of Periodic Autoregressive Moving Average Models." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1083682/index.pdf.
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In this thesis, identification of periodically varying orders of univariate
Periodic Autoregressive Moving-Average (PARMA) processes is mainly studied.
The identification of the varying orders of PARMA process is carried
out by generalizing the well-known Box-Jenkins techniques to a seasonwise
manner. The identification of pure periodic moving-average (PMA) and pure
periodic autoregressive (PAR) models are considered only. For PARMA model
identification, the Periodic Autocorrelation Function (PeACF) and Periodic Partial
Autocorrelation Function (PePACF), which play the same role as their ARMA
counterparts, are employed.
For parameter estimation, which is considered only to refine model
identification, the conditional least squares estimation (LSE) method is used
which is applicable to PAR models. Estimation becomes very complicated,
difficult and may give unsatisfactory results when a moving-average (MA)
component exists in the model. On account of overcoming this difficulty,
seasons following PMA processes are tried to be modeled as PAR processes
with reasonable orders in order to employ LSE. Diagnostic checking, through
residuals of the fitted model, is also performed stating its reasons and methods.
The last part of the study demonstrates application of identification
techniques through analysis of two seasonal hydrologic time series, which
consist of average monthly streamflows. For this purpose, computer programs
were developed specially for PARMA model identification.
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PEREIRA, ANGELO SERGIO MILFONT. "IDENTIFICATION MECHANISMS OF SPURIOUS DIVISIONS IN THRESHOLD AUTOREGRESSIVE MODELS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2002. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=3191@1.
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O objetivo desta dissertação é propor um mecanismo de
testes para a avaliação dos resultados obtidos em uma
modelagem TS-TARX.A principal motivação é encontrar uma
solução para um problema comum na modelagem TS-TARX : os
modelos espúrios que são gerados durante o processo de
divisão do espaço das variáveis independentes.O modelo é
uma heurística baseada em análise de árvore de regressão,
como discutido por Brieman -3, 1984-. O modelo proposto
para a análise de séries temporais é chamado TARX -
Threshold Autoregressive with eXternal variables-. A idéia
central é encontrar limiares que separem regimes que podem
ser explicados através de modelos lineares. Este processo é
um algoritmo que preserva o método de regressão por
mínimos quadrados recursivo -MQR-. Combinando a árvore de
decisão com a técnica de regressão -MQR-, o modelo se
tornou o TS-TARX -Tree Structured - Threshold
AutoRegression with external variables-.Será estendido aqui
o trabalho iniciado por Aranha em -1, 2001-. Onde a partir
de uma base de dados conhecida, um algoritmo eficiente gera
uma árvore de decisão por meio de regras, e as equações de
regressão estimadas para cada um dos regimes encontrados.
Este procedimento pode gerar alguns modelos espúrios ou por
construção,devido a divisão binária da árvore, ou pelo fato
de não existir neste momento uma metodologia de comparação
dos modelos resultantes.Será proposta uma metodologia
através de sucessivos testes de Chow -5, 1960- que
identificará modelos espúrios e reduzirá a quantidade de
regimes encontrados, e consequentemente de parâmetros a
estimar. A complexidade do modelo final gerado é reduzida a
partir da identificação de redundâncias, sem perder o poder
preditivo dos modelos TS-TARX .O trabalho conclui com
exemplos ilustrativos e algumas aplicações em bases de
dados sintéticas, e casos reais que auxiliarão o
entendimento.The goal of this dissertation is to propose a test mechanism to evaluate the results obtained from the TS-TARX modeling procedure.The main motivation is to find a solution to a usual problem related to TS-TARX modeling: spurious models are generated in the process of dividing the space state of the independent variables.The model is a heuristics based on regression tree analysis, as discussed by Brieman -3, 1984-. The model used to estimate the parameters of the time series is a TARX -Threshold Autoregressive with eXternal variables-.The main idea is to find thresholds that split the independent variable space into regimes which can be described by a local linear model. In this process, the recursive least square regression model is preserved. From the combination of regression tree analysis and recursive least square regression techniques, the model becomes TS-TARX -Tree Structured - Threshold Autoregression with eXternal variables-.The works initiated by Aranha in -1, 2001- will be extended. In his works, from a given data base, one efficient algorithm generates a decision tree based on splitting rules, and the corresponding regression equations for each one of the regimes found.Spurious models may be generated either from its building procedure, or from the fact that a procedure to compare the resulting models had not been proposed.To fill this gap, a methodology will be proposed. In accordance with the statistical tests proposed by Chow in -5, 196-, a series of consecutive tests will be performed.The Chow tests will provide the tools to identify spurious models and to reduce the number of regimes found. The complexity of the final model, and the number of parameters to estimate are therefore reduced by the identification and elimination of redundancies, without bringing risks to the TS-TARX model predictive power.This work is concluded with illustrative examples and some applications to real data that will help the readers understanding.
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Braun, Robin [Verfasser]. "Three Essays on Identification in Structural Vector Autoregressive Models / Robin Braun." Konstanz : KOPS Universität Konstanz, 2019. http://d-nb.info/1191693473/34.
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Bertsche, Dominik [Verfasser]. "Three Essays on Identification and Dimension Reduction in Vector Autoregressive Models / Dominik Bertsche." Konstanz : KOPS Universität Konstanz, 2020. http://d-nb.info/1209879778/34.
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Avventi, Enrico, Anders Lindquist, and Bo Wahlberg. "ARMA Identification of Graphical Models." KTH, Optimeringslära och systemteori, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-39065.
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Consider a Gaussian stationary stochastic vector process with the property that designated pairs of components are conditionally independent given the rest of the components. Such processes can be represented on a graph where the components are nodes and the lack of a connecting link between two nodes signifies conditional independence. This leads to a sparsity pattern in the inverse of the matrix-valued spectral density. Such graphical models find applications in speech, bioinformatics, image processing, econometrics and many other fields, where the problem to fit an autoregressive (AR) model to such a process has been considered. In this paper we take this problem one step further, namely to fit an autoregressive moving-average (ARMA) model to the same data. We develop a theoretical framework and an optimization procedure which also spreads further light on previous approaches and results. This procedure is then applied to the identification problem of estimating the ARMA parameters as well as the topology of the graph from statistical data.Updated from "Preprint" to "Article" QC 20130627
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Yang, Kai. "Essays on Multivariate and Simultaneous Equations Spatial Autoregressive Models." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1461277549.
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Bruns, Martin [Verfasser]. "Essays in Empirical Macroeconomics: Identification in Vector Autoregressive Models and Robust Inference in Early Warning Systems / Martin Bruns." Berlin : Freie Universität Berlin, 2019. http://d-nb.info/119064522X/34.
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Ogbonna, Emmanuel. "A multi-parameter empirical model for mesophilic anaerobic digestion." Thesis, University of Hertfordshire, 2017. http://hdl.handle.net/2299/17467.
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Anaerobic digestion, which is the process by which bacteria breakdown organic matter to produce biogas (renewable energy source) and digestate (biofertiliser) in the absence of oxygen, proves to be the ideal concept not only for sustainable energy provision but also for effective organic waste management. However, the production amount of biogas to keep up with the global demand is limited by the underperformance in the system implementing the AD process. This underperformance is due to the difficulty in obtaining and maintaining the optimal operating parameters/states for anaerobic bacteria to thrive with regards to attaining a specific critical population number, which results in maximising the biogas production. This problem continues to exist as a result of insufficient knowledge of the interactions between the operating parameters and bacterial community. In addition, the lack of sufficient knowledge of the composition of bacterial groups that varies with changes in the operating parameters such as temperature, substrate and retention time. Without sufficient knowledge of the overall impact of the physico-environmental operating parameters on anaerobic bacterial growth and composition, significant improvement of biogas production may be difficult to attain. In order to mitigate this problem, this study has presented a nonlinear multi-parameter system modelling of mesophilic AD. It utilised raw data sets generated from laboratory experimentation of the influence of four operating parameters, temperature, pH, mixing speed and pressure on biogas and methane production, signifying that this is a multiple input single output (MISO) system. Due to the nonlinear characteristics of the data, the nonlinear black-box modelling technique is applied. The modelling is performed in MATLAB through System Identification approach. Two nonlinear model structures, autoregressive with exogenous input (NARX) and Hammerstein-Wiener (NLHW) with different nonlinearity estimators and model orders are chosen by trial and error and utilised to estimate the models. The performance of the models is determined by comparing the simulated outputs of the estimated models and the output in the validation data. The approach is used to validate the estimated models by checking how well the simulated output of the models fits the measured output. The best models for biogas and methane production are chosen by comparing the outputs of the best NARX and NLHW models (each for biogas and methane production), and the validation data, as well as utilising the Akaike information criterion to measure the quality of each model relative to each of the other models. The NLHW models mhw2 and mhws2 are chosen for biogas and methane production, respectively. The identified NLHW models mhw2 and mhws2 represent the behaviour of the production of biogas and methane, respectively, from mesophilic AD. Among all the candidate models studied, the nonlinear models provide a superior reproduction of the experimental data over the whole analysed period. Furthermore, the models constructed in this study cannot be used for scale-up purpose because they are not able to satisfy the rules and criteria for applying dimensional analysis to scale-up.
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Uhrin, Gábor B. [Verfasser], Martin [Akademischer Betreuer] Wagner, and Walter [Gutachter] Krämer. "In search of Q: results on identification in structural vector autoregressive models / Gábor B. Uhrin ; Gutachter: Walter Krämer ; Betreuer: Martin Wagner." Dortmund : Universitätsbibliothek Dortmund, 2017. http://d-nb.info/1138115134/34.
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Úriz-Jáuregui, Fermín. "Mise en place d'une méthodologie pour l'identification de modèles d'extrapolation de température : application aux équipements de nacelles de turboréacteurs." Thesis, Université de Lorraine, 2012. http://www.theses.fr/2012LORR0381/document.
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Airbus réalise pour chaque avion et pour chaque équipement de nombreux essais, au sol ou en vol et doit garantir qu'en tout point de vol possible, la température de chacun des équipements reste inférieure à la température limite correspondante. Pour pouvoir valider la température de chaque équipement dans l'enveloppe de vol, il faudrait disposer d'essais réalisés aux frontières. Or, tous les essais en vol sont confrontés aux contraintes climatiques et opérationnelles qui ne permettent pas d'explorer tout le domaine. C'est pourquoi Airbus a besoin d'élaborer des méthodes d'extrapolation de température, de manière à prédire le comportement thermique des matériaux et des équipements dans les pires conditions. Les techniques proposées sont basées sur la théorie de l'identification de systèmes qui consiste à déterminer des modèles de comportement d'un point de vue heuristique à partir de mesures et considérations physiques. Plus précisément, le présent document valide les modèles ARX comme un outil pour l'identification de la température du système. Les modèles et les techniques sont étudiés, tout d'abord, d'un point de vue de la simulation numérique et après, confrontés face à des tests représentatifs au laboratoire. Les techniques proposées permettent prédire la température des composants avion pour des conditions différentesAirbus must ensure that for all flight conditions that a given aircraft could face, the temperature of each powerplant system must be less than the corresponding critical temperature. In order to validate the temperature of each device in the flight envelope, tests at the border should be done. Airbus produces for each aircraft component many trials, either in flight or ground. However, all flight tests are faced with climatic and operational constraints which do not permit exploring the whole area. That's why Airbus needs to develop methods of extrapolation of temperature in order to predict the thermal behavior of materials and equipments in the worst conditions. The proposed techniques are based on the system identification theory which consists on heuristically determining an analytical model using physical insights and measurements. More precisely, this paper validates ARX models as a tool for the identification of the system's temperature. The models and techniques are studied, first, from a numerical simulation point of view and second, based on laboratory representative tests. The proposed techniques allow predicting the temperature of aircraft components at different conditions
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Auber, Romain. "Contribution à la reconnaissance d'activités à partir d'un objet connecté." Thesis, Normandie, 2019. http://www.theses.fr/2019NORMC242.
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Ce manuscrit porte sur la reconnaissance d’activités à partir de données accéléromètriques. Le dispositif utilisé pour collecter les données de l’accéléromètre est eTact, dispositif développé par la société Bodycap. Plusieurs solutions sont proposées afin d'optimiser l’autonomie de l’objet connecté. Ces solutions sont mises en oeuvre et comparées sur différentes séries de données. L'originalité d'une de ces solutions consiste à binariser les données de l’accéléromètre avant de les transférer vers une plateforme externe où elles sont analysées. L’utilisation de données binaires entraîne la perte de nombreuses informations, cependant il est montré dans ce manuscrit qu’il est possible d’estimer, entres autres, les paramètres d'un modèle Auto Régressif d’une série temporelle à partir de l'information binaire sur cette série. A ce titre, un algorithme d'identification est proposé et analyséThis manuscript deals with the recognition of activities from accelerometric data. The device used to collect the accelerometer data is eTact, a device developed by Bodycap. Several solutions are proposed to optimize the autonomy of the connected object. These solutions are implemented and compared on different data sets. The originality of one of these solutions is to binarize the data of the accelerometer before transferring them to an external platform where they are analyzed. The use of binary data induces the loss of a lot of information, however it is shown in this manuscript that it is possible to estimate, among other things, the parameters of an Auto Regressive model of a time series from the binary information on this series. In this respect, an identification algorithm is proposed and analyzed
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Claudino, Joana Filipa Caetano. "Intelligent system for time series pattern identification and prediction." Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/21036.
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Mestrado em Gestão de Sistemas de InformaçãoOs crescentes volumes de dados representam uma fonte de informação potencialmente valiosa para as empresas, mas também implicam desafios nunca antes enfrentados. Apesar da sua complexidade intrínseca, as séries temporais são um tipo de dados notavelmente relevantes para o contexto empresarial, especialmente para tarefas preditivas. Os modelos Autorregressivos Integrados de Médias Móveis (ARIMA), têm sido a abordagem mais popular para tais tarefas, porém, não estão preparados para lidar com as cada vez mais comuns séries temporais de maior dimensão ou granularidade. Assim, novas tendências de investigação envolvem a aplicação de modelos orientados a dados, como Redes Neuronais Recorrentes (RNNs), à previsão. Dada a dificuldade da previsão de séries temporais e a necessidade de ferramentas aprimoradas, o objetivo deste projeto foi a implementação dos modelos clássicos ARIMA e as arquiteturas RNN mais proeminentes, de forma automática, e o posterior uso desses modelos como base para o desenvolvimento de um sistema modular capaz de apoiar o utilizador em todo o processo de previsão. Design science research foi a abordagem metodológica adotada para alcançar os objetivos propostos e envolveu, para além da identificação dos objetivos, uma revisão aprofundada da literatura que viria a servir de suporte teórico à etapa seguinte, designadamente a execução do projeto e findou com a avaliação meticulosa do artefacto produzido. No geral todos os objetivos propostos foram alcançados, sendo os principais contributos do projeto o próprio sistema desenvolvido devido à sua utilidade prática e ainda algumas evidências empíricas que apoiam a aplicabilidade das RNNs à previsão de séries temporais.
The current growing volumes of data present a source of potentially valuable information for companies, but they also pose new challenges never faced before. Despite their intrinsic complexity, time series are a notably relevant kind of data in the entrepreneurial context, especially regarding prediction tasks. The Autoregressive Integrated Moving Average (ARIMA) models have been the most popular approach for such tasks, but they do not scale well to bigger and more granular time series which are becoming increasingly common. Hence, newer research trends involve the application of data-driven models, such as Recurrent Neural Networks (RNNs), to forecasting. Therefore, given the difficulty of time series prediction and the need for improved tools, the purpose of this project was to implement the classical ARIMA models and the most prominent RNN architectures in an automated fashion and posteriorly to use such models as foundation for the development of a modular system capable of supporting the common user along the entire forecasting process. Design science research was the adopted methodology to achieve the proposed goals and it comprised the activities of goal definition, followed by a thorough literature review aimed at providing the theoretical background necessary to the subsequent step that involved the actual project execution and, finally, the careful evaluation of the produced artifact. In general, each the established goals were accomplished, and the main contributions of the project were the developed system itself due to its practical usefulness along with some empirical evidence supporting the suitability of RNNs to time series forecasting.
info:eu-repo/semantics/publishedVersion
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Fischer, Manfred M., Florian Huber, Michael Pfarrhofer, and Petra Staufer-Steinnocher. "The dynamic impact of monetary policy on regional housing prices in the United States." WU Vienna University of Economics and Business, 2018. http://epub.wu.ac.at/6658/1/2018%2D11%2D16_housing_favar_(002).pdf.
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This paper uses a factor-augmented vector autoregressive model to examine the impact of monetary
policy shocks on housing prices across metropolitan and micropolitan regions. To simultaneously
estimate the model parameters and unobserved factors we rely on Bayesian estimation
and inference. Policy shocks are identified using high-frequency suprises around policy
announcements as an external instrument. Impulse reponse functions reveal differences in regional
housing price responses, which in some cases are substantial. The heterogeneity in policy
responses is found to be significantly related to local regulatory environments and housing supply
elasticities. Moreover, housing prices responses tend to be similar within states and adjacent
regions in neighboring states.Series: Working Papers in Regional Science
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Romano, Donato. "Machine Learning algorithms for predictive diagnostics applied to automatic machines." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22319/.
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In questo lavoro di tesi è stato analizzato l'avvento dell'industria 4.0 all'interno dell' industria nel settore packaging. In particolare, è stata discussa l'importanza della diagnostica predittiva e sono stati analizzati e testati diversi approcci per la determinazione di modelli descrittivi del problema a partire dai dati. Inoltre, sono state applicate le principali tecniche di Machine Learning in modo da classificare i dati analizzati nelle varie classi di appartenenza.
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Yeung, Miu Han Iris. "Continuous time threshold autoregressive model." Thesis, University of Kent, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.328661.
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Sellner, Richard, Manfred M. Fischer, and Matthias Koch. "A Spatial Autoregressive Poisson Gravity Model." Wiley-Blackwell, 2013. http://dx.doi.org/10.1111/gean.12007.
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In this article, a Poisson gravity model is introduced that incorporates spatial dependence
of the explained variable without relying on restrictive distributional assumptions of the
underlying data-generating process. The model comprises a spatially filtered component
including the origin-, destination-, and origin-destination-specific variables and a spatial
residual variable that captures origin- and destination-based spatial autocorrelation. We
derive a two-stage nonlinear least-squares (NLS) estimator (2NLS) that is heteroscedasticity-
robust and, thus, controls for the problem of over- or underdispersion that often
is present in the empirical analysis of discrete data or, in the case of overdispersion, if
spatial autocorrelation is present. This estimator can be shown to have desirable properties
for different distributional assumptions, like the observed flows or (spatially) filtered component
being either Poisson or negative binomial. In our spatial autoregressive (SAR)
model specification, the resulting parameter estimates can be interpreted as the implied
total impact effects defined as the sum of direct and indirect spatial feedback effects. Monte
Carlo results indicate marginal finite sample biases in the mean and standard deviation of
the parameter estimates and convergence to the true parameter values as the sample size
increases. In addition, this article illustrates the model by analyzing patent citation flows
data across European regions. (authors' abstract)
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Kriwoluzky, Alexander. "Matching DSGE models to data with applications to fiscal and robust monetary policy." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2009. http://dx.doi.org/10.18452/16052.
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Diese Doktorarbeit untersucht drei Fragestellungen. Erstens, wie die Wirkung von plötzlichen Änderungen exogener Faktoren auf endogene Variablen empirisch im Allgemeinen zu bestimmen ist. Zweitens, welche Effekte eine Erhöhung der Staatsausgaben im Speziellen hat. Drittens, wie optimale Geldpolitik bestimmt werden kann, wenn der Entscheider keine eindeutigen Modelle für die ökonomischen Rahmenbedingungen hat. Im ersten Kapitel entwickele ich eine Methode, mithilfe derer die Effekte von plötzlichen Änderungen exogener Faktoren auf endogene Variablen geschätzt werden können. Dazu wird die gemeinsame Verteilung von Parametern einer Vektor Autoregression (VAR) und eines stochastischen allgemeinen Gleichgewichtsmodelles (DSGE) bestimmt. Auf diese Weise können zentrale Probleme gelöst werden: das Identifikationsproblem der VAR und eine mögliche Misspezifikation des DSGE Modells. Im zweitem Kapitel wende ich die Methode aus dem ersten Kapitel an, um den Effekt einer angekündigten Erhöhung der Staatsausgaben auf den privaten Konsum und die Reallöhne zu untersuchen. Die Identifikation beruht auf der Einsicht, dass endogene Variablen, oft qualitative Unterschiede in der Periode der Ankündigung und nach der Realisation zeigen. Die Ergebnisse zeigen, dass der private Konsum negativ im Zeitraum der Ankündigung reagiert und positiv nach der Realisation. Reallöhne steigen zum Zeitpunkt der Ankündigung und sind positiv für zwei Perioden nach der Realisation. Im abschließendem Kapitel untersuche ich gemeinsam mit Christian Stoltenberg, wie Geldpolitik gesteuert werden sollte, wenn die Modellierung der Ökonomie unsicher ist. Wenn ein Modell um einen Parameter erweitert wird, kann das Modell dadurch so verändert werden, dass sich die Politikempfehlungen zwischen dem ursprünglichen und dem neuen Modell unterscheiden. Oft wird aber lediglich das erweiterte Modell betrachtet. Wir schlagen eine Methode vor, die beiden Modellen Rechnung trägt und somit zu einer besseren Politik führt.This thesis is concerned with three questions: first, how can the effects macroeconomic policy has on the economy in general be estimated? Second, what are the effects of a pre-announced increase in government expenditures? Third, how should monetary policy be conducted, if the policymaker faces uncertainty about the economic environment. In the first chapter I suggest to estimate the effects of an exogenous disturbance on the economy by considering the parameter distributions of a Vector Autoregression (VAR) model and a Dynamic Stochastic General Equilibrium (DSGE) model jointly. This allows to resolve the major issue a researcher has to deal with when working with a VAR model and a DSGE model: the identification of the VAR model and the potential misspecification of the DSGE model. The second chapter applies the methodology presented in the preceding chapter to investigate the effects of a pre-announced change in government expenditure on private consumption and real wages. The shock is identified by exploiting its pre-announced nature, i.e. different signs of the responses in endogenous variables during the announcement and after the realization of the shock. Private consumption is found to respond negatively during the announcement period and positively after the realization. The reaction of real wages is positive on impact and positive for two quarters after the realization. In the last chapter ''Optimal Policy Under Model Uncertainty: A Structural-Bayesian Estimation Approach'' I investigate jointly with Christian Stoltenberg how policy should optimally be conducted when the policymaker is faced with uncertainty about the economic environment. The standard procedure is to specify a prior over the parameter space ignoring the status of some sub-models. We propose a procedure that ensures that the specified set of sub-models is not discarded too easily. We find that optimal policy based on our procedure leads to welfare gains compared to the standard practice.
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Brüggemann, Ralf. "Model reduction methods for vector autoregressive processes /." Berlin [u.a.] : Springer, 2004. http://www.loc.gov/catdir/enhancements/fy0818/2003067373-d.html.
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Nur, Darfiana. "Parameter estimation of smooth threshold autoregressive models." Curtin University of Technology, School of Mathematics and Statistics, 1998. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=10781.
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This thesis is mainly concerned with the estimation of parameters of a first-order Smooth Threshold Autoregressive (STAR) model with delay parameter one. The estimation procedures include classical and Bayesian methods from a parametric and a semiparametric point of view.As the theoretical importance of stationarity is a primary concern in estimation of time series models, we begin the thesis with a thorough investigation of necessary or sufficient conditions for ergodicity of a first-order STAR process followed by the necessary and sufficient conditions for recurrence and classification for null-recurrence and transience.The estimation procedure is started by using Bayesian analysis which derives posterior distributions of parameters with a noninformative prior for the STAR models of order p. The predictive performance of the STAR models using the exact one-step-ahead predictions along with an approximation to multi-step-ahead predictive density are considered. The theoretical results are then illustrated by simulated data sets and the well- known Canadian lynx data set.The parameter estimation obtained by conditional least squares, maximum likelihood, M-estimator and estimating functions are reviewed together with their asymptotic properties and presented under the classical and parametric approaches. These estimators are then used as preliminary estimators for obtaining adaptive estimates in a semiparametric setting. The adaptive estimates for a first-order STAR model with delay parameter one exist only for the class of symmetric error densities. At the end, the numerical results are presented to compare the parametric and semiparametric estimates of this model.
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Rastenė, Irma. "Testing and estimating changed segment in autoregressive model." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2011. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2011~D_20110628_134429-88914.
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In the doctoral dissertation, we consider problems of testing and estimating changed segment with unknown starting position and duration of epidemic state in the autoregressive first-order model. The proposed tests are based on partial sums of model residuals and model-parameter partial-estimator polygonal line processes. We derive asymptotic results for these processes in Holder spaces. The behavior of test statistics under the null hypothesis of no change and alternative is provided. Empirical power analysis has shown that tests are more powerful when absolute values of model parameter are quite large or autoregressive process changes from a stationary state to a nonstationary one. We prove the consistency of the least square changed-segment estimators and provide their convergence rates.Disertacijoje nagrinėjamas pirmos eilės autoregresinio modelio pasikeitusio segmento testavimo ir vertinimo uždavinys. Aprašomo modelio epideminio pasikeitimo pradžia ir ilgis nėra žinomi. Pasiūlyti kriterijai pasikeitusio segmento testavimui, kurie pagrįsti modelio paklaidų įvertinių dalinių sumų ir modelio parametro dalinių įvertinių laužčių procesais. Šiems procesams gautos ribinės teoremos Hiolderio erdvėse. Nurodomas testų statistikų ribinis elgesys esant teisingai nulinei ir alternatyviajai hipotezėms. Iš empirinio kriterijų galios tyrimo rezultatų matyti, kad pasiūlytų testų galia didžiausia aptinkant pasikeitimus iš stacionarios būklės į nestacionarią arba esant artimoms vienetui modelio parametro reikšmėms. Taip pat įrodoma, kad mažiausių kvadratų metodu gauti pasikeitusio segmento pradžios ir ilgio įverčiai bei autoregresinio modelio su pasikeitusiu segmentu parametrų įverčiai yra suderintieji bei pateikiamas jų konvergavimo greitis.
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Yano, Marcus Omori. "Extrapolation of autoregressive model for damage progression analysis /." Ilha Solteira, 2019. http://hdl.handle.net/11449/182287.
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Orientador: Samuel da SilvaResumo: O principal objetivo deste trabalho é usar métodos de extrapolação em coeficientes de modelos autorregressivos (AR), para fornecer informações futuras de condições de estruturas na existência de mecanismo de danos pré-definidos. Os modelos AR são estimados considerando a predição de um passo à frente, verificados e validados a partir de dados de vibração de uma estrutura na condição não danificada. Os erros de predição são usados para extrair um indicador para classificar a condição do sistema. Então, um novo modelo é identificado se qualquer variação de índices de dano ocorrer, e seus coeficientes são comparados com os do modelo de referência. A extrapolação dos coeficientes de AR é realizada através das splines cúbicas por partes que evitam possíveis instabilidades e alterações indesejáveis dos polinômios, obtendo aproximações adequadas através de polinômios de baixa ordem. Uma curva de tendência para o indicador capaz de predizer o comportamento futuro pode ser obtida a partir da extrapolação direta dos coeficientes. Uma estrutura de três andares com um para-choque e uma coluna de alumínio colocada no centro do último andar são analisados com diferentes cenários de dano para ilustrar a abordagem. Os resultados indicam a possibilidade de estimar a condição futura do sistema a partir dos dados de vibração nas condições de danos iniciais.
Abstract: The main purpose of this work is to apply extrapolation methods upon coefficients of autoregressive models (AR), to provide future condition information of structures in the existence of predefined damage mechanism. The AR models are estimated considering one-step-ahead prediction, verified and validated from vibration data of a structure in the undamaged condition. The prediction errors are used to extract an indicator to classify the system state condition. Then, a new model is identified if any variation of damage indices occurs, and its coefficients are compared to the ones from the reference model. The extrapolation of the AR coefficients is performed through the piecewise cubic splines that avoid possible instabilities and undesirable changes of the polynomials, obtaining suitable approximations through low-order polynomials. A trending curve for the indicator capable of predicting future behavior can be obtained from direct coefficient extrapolation. A benchmark of a three-story building structure with a bumper and an aluminum column placed on the center of the top floor is analyzed with different damage scenarios to illustrate the approach. The results indicate the feasibility of estimating the future system state from the vibration data in the initial damage conditions.
Mestre
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Silvennoinen, Annastiina. "Essays on autoregressive conditional heteroskedasticity." Doctoral thesis, Stockholm : Economic Research Institute, Stockholm School of Economics (EFI), 2006. http://www2.hhs.se/EFI/summary/711.htm.
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Li, Chun-wah. "On a double threshold autoregressive heteroskedastic time series model /." [Hong Kong : University of Hong Kong], 1994. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13745037.
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Ludkin, Matthew Robert. "The autoregressive stochastic block model with changes in structure." Thesis, Lancaster University, 2017. http://eprints.lancs.ac.uk/125642/.
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Network science has been a growing subject for the last three decades, with sta- tistical analysis of networks seing an explosion since the advent of online social networks. An important model within network analysis is the stochastic block model, which aims to partition the set of nodes of a network into groups which behave in a similar way. This thesis proposes Bayesian inference methods for problems related to the stochastic block model for network data. The presented research is formed of three parts. Firstly, two Markov chain Monte Carlo samplers are proposed to sample from the posterior distribution of the number of blocks, block memberships and edge-state parameters in the stochastic block model. These allow for non-binary and non-conjugate edge models, something not considered in the literature. Secondly, a dynamic extension to the stochastic block model is presented which includes autoregressive terms. This novel approach to dynamic network models allows the present state of an edge to influence future states, and is therefore named the autoregresssive stochastic block model. Furthermore, an algorithm to perform inference on changes in block membership is given. This problem has gained some attention in the literature, but not with autoregressive features to the edge-state distribution as presented in this thesis. Thirdly, an online procedure to detect changes in block membership in the au- toregresssive stochastic block model is presented. This allows networks to be monitored through time, drastically reducing the data storage requirements. On top of this, the network parameters can be estimated together with the block memberships. Finally, conclusions are drawn from the above contributions in the context of the network analysis literature and future directions for research are identified.
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Crespo, Cuaresma Jesus, and Philipp Piribauer. "Bayesian Variable Selection in Spatial Autoregressive Models." WU Vienna University of Economics and Business, 2015. http://epub.wu.ac.at/4584/1/wp199.pdf.
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This paper compares the performance of Bayesian variable selection approaches for spatial autoregressive models. We present two alternative approaches which can be implemented using Gibbs sampling methods in a straightforward way and allow us to deal with the problem of model uncertainty in spatial autoregressive models in a flexible and computationally efficient way. In a simulation study we show that the variable selection approaches tend to outperform existing Bayesian model averaging techniques both in terms of in-sample predictive performance and computational efficiency.
(authors' abstract)Series: Department of Economics Working Paper Series
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Schnücker, Annika [Verfasser]. "Model Selection Methods for Panel Vector Autoregressive Models / Annika Schnücker." Berlin : Freie Universität Berlin, 2018. http://d-nb.info/1176708147/34.
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Camehl, Annika [Verfasser]. "Model Selection Methods for Panel Vector Autoregressive Models / Annika Schnücker." Berlin : Freie Universität Berlin, 2018. http://d-nb.info/1176708147/34.
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Liu, Yingying. "Bayesian hierarchical normal intrinsic conditional autoregressive model for stream networks." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6606.
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Water quality and river/stream ecosystems are important for all living creatures. To protect human health, aquatic life and the surrounding ecosystem, a considerable amount of time and money has been spent on sampling and monitoring streams and rivers. Water quality monitoring and analysis can help researchers predict and learn from natural processes in the environment and determine human impacts on an ecosystem. Measurements such as temperature, pH, nitrogen concentration, algae and fish count collected along the network are all important factors in water quality analysis. The main purposes of the statistical analysis in this thesis are (1) to assess the relationship between the variable measured in the water (response variable) and other variables that describe either the locations on/along the stream network or certain characteristics at each location (explanatory variable), and (2) to assess the degree of similarity between the response variable values measured at different locations of the stream, i.e. spatial dependence structure. It is commonly accepted that measurements taken at two locations close to each other should have more similarity than locations far away. However, this is not always true for observations from stream networks. Observations from two sites that do not share water flow could be independent of each other even if they are very close in terms of stream distance, especially those observations taken on objects that move passively with the water flow. To model stream network data correctly, it is important to quantify the strength of association between observations from sites that do not share water.
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Qu, Xi. "Three Essays on the Spatial Autoregressive Model in Spatial Econometrics." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1365455610.
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Ehlers, Ricardo Sandes. "Bayesian model discrimination for time series and state space models." Thesis, University of Surrey, 2002. http://epubs.surrey.ac.uk/843599/.
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In this thesis, a Bayesian approach is adopted to handle parameter estimation and model uncertainty in autoregressive moving average (ARMA) time series models and dynamic linear models (DLM). Bayesian model uncertainty is handled in a parametric fashion through the use of posterior model probabilities computed via Markov chain Monte Carlo (MCMC) simulation techniques. Attention is focused on reversible jump Markov chain Monte Carlo (RJMCMC) samplers, which can move between models of different dimensions, to address the problem of model order uncertainty and strategies for proposing efficient sampling schemes in autoregressive moving average time series models and dynamic linear models are developed. The general problem of assessing convergence of the sampler in a dimension-changing context is addressed by computing estimates of the probabilities of moving to higher and lower dimensional spaces. Graphical and numerical techniques are used to compare different updating schemes. The methodology is illustrated by applying it to both simulated and real data sets and the results for the Bayesian model selection and parameter estimation procedures are compared with the classical model selection criteria and maximum likelihood estimation.
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Uysal, Ela. "Application Of Nonlinear Unit Root Tests And Threshold Autoregressive Models." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614878/index.pdf.
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Popularity of nonlinear threshold models and unit root tests has increased after the recent empirical studies concerning the effects of business cycles on macroeconomic data. These studies have shown that an economic variable may react differently in response to downturns and recoveries in a business cycle. Inspiring from empirical results, this thesis investigates dynamics of Turkish key macroeconomic data, namely capacity utilization rate, growth of import and export volume indices, growth of gross domestic product, interest rate for cash loans in Turkish Liras and growth of industrial production index. Estimation results imply that capacity utilization rate and growth of industrial production index show M-TAR type nonlinear stationary behavior according to the unit root test proposed by Enders and Granger (1998).
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Pfarrhofer, Michael, and Philipp Piribauer. "Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models." Elsevier, 2019. http://epub.wu.ac.at/6839/1/1805.10822.pdf.
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Several recent empirical studies, particularly in the regional economic growth literature, emphasize the importance of explicitly accounting for uncertainty surrounding model specification. Standard approaches to deal with the problem of model uncertainty involve the use of Bayesian model-averaging techniques. However, Bayesian model-averaging for spatial autoregressive models suffers from severe drawbacks both in terms of computational time and possible extensions to more flexible econometric frameworks. To alleviate these problems, this paper presents two global-local shrinkage priors in the context of high-dimensional matrix exponential spatial specifications. A simulation study is conducted to evaluate the performance of the shrinkage priors. Results suggest that they perform particularly well in high-dimensional environments, especially when the number of parameters to estimate exceeds the number of observations. Moreover, we use pan-European regional economic growth data to illustrate the performance of the proposed shrinkage priors.
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Horton, Wendy Elizabeth. "A vector autoregressive model of a regional Phillips curve in the United States." Thesis, Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/30515.
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Kou, Tong-Zhang. "Parameter estimation for exponential signals in colored noise using the pseudo-autoregressive (PAR) model." Diss., This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-03042009-041238/.
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Albarracin, Orlando Yesid Esparza. "Generalized autoregressive and moving average models: control charts, multicollinearity, and a new modified model." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-21112017-184544/.
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Recently, in the health surveillance area, control charts have been proposed to decide if the morbidity or mortality of a specific disease reached an epidemic level. This thesis is composed by 3 papers. In the first two papers, CUSUM and EWMA control charts were proposed to monitor count time series with seasonal and trend effects using the Generalized Autoregressive and Moving Average models (GARMA), instead of the independent Generalized Linear Model (GLM) as it is usually used in practice. Different statistics based on transformations, for variables that follow a Negative Binomial distribution, were used in these control charts. In the second paper, two new statistics were proposed based on the ratio of log-likelihood function. Different scenarios describing disease profiles were considered to evaluate the effect of omission of serial correlation in EWMA and CUSUM control charts. The performance of CUSUM and EWMA charts when the serial correlation is neglected in the regression model was measure in terms of average run length (ARL). In summary, when the autocorrelation is neglected, fitting a pure GLM instead of a GARMA model will lead to an increase of false alarms. However, no statistics among the tested ones seem to be robust, in a sense to produce the smallest increase of false alarms in all scenarios. In general, all monitored statistics presented a smaller ARL_0 for higher values of autocorrelation. \\\\ In the last paper, the GARMA models (p, q) with p and q simultaneously different from zero were studied since that two features were observed in practice. One is the multicollinearity, which may lead to a non-convergence of the maximum likelihood, using iteratively reweighted least squares. The second is the inclusion of the same lagged observations into the autoregressive and moving average components confounding the interpretation of the parameters. In a general sense, simulation studies show that the modified model provide estimators closer to the parameters and offer confidence intervals with higher coverage percentage than obtained with the GARMA model, but some restrictions in the parametric space are imposed to guarantee the stationarity of the process. Also, a real data analysis illustrate the GARMA-M fit for daily hospilatization rates of elderly people due to respiratory diseases from October 2012 to April 2015 in São Paulo city, Brazil.Recentemente, no campo da saúde, gráficos de controle têm sido propostos para monitorar a morbidade ou a mortalidade decorrentes de doenças. Este trabalho está composto por três artigos. Nos dois primeiros artigos, gráficos de controle CUSUM e EWMA foram propostos para monitorar séries temporais de contagens com efeitos sazonais e de tendência usando os modelos Generalized autoregressive and moving average models (GARMA), em vez dos modelos lineares generalizados (GLM), como usualmente são utilizados na prática. Diferentes estatísticas baseadas em transformações, para variávies que seguem uma distribuição Binomial Negativa, foram usadas nestes gráficos de controle. No segundo artigo foram propostas duas novas estatísticas baseadas na razão da função de log-verossimilhança. Diferentes cenários que descrevem perfis de doenças foram considerados para avaliar o efeito da omissão da correlação serial nesses gráficos de controle. Este impacto foi medido em termos do Average Run Lenght (ARL). Notou-se que a negligência da correlação serial induz um aumento de falsos alarmes. Em geral, todas as estatísticas monitoradas apresentaram menores valores de ARL_0 para maiores valores de autocorrelação. No entanto, nenhuma estatística entre as consideradas mostrou ser mais robusta, no sentido de produzir o menor aumento de falsos alarmes nos cenários considerados. No último artigo, foram estudados os modelos GARMA (p, q) com p e q simultaneamente diferentes de zero, uma vez que duas características foram observadas na prática. A primeira é a presença de multicolinearidade, que induz à não-convergência do método de máxima verossimilhança usando mínimos quadrados ponderados reiterados. A segunda é a inclusão dos mesmos termos defasados nos componentes autorregressivos e de médias móveis. Um modelo modificado, GARMA-M, foi apresentado para lidar com a multicolinearidade e melhorar a interpretação dos parâmetros. Em sentido geral, estudos de simulação mostraram que o modelo modificado fornece estimativas mais próximas dos parâmetros e intervalos de confiança com uma cobertura percentual maior do que a obtida nos modelos GARMA. No entanto, algumas restrições no espaço paramétrico são impostas para garantir a estacionariedade do processo. Por último, uma análise de dados reais ilustra o ajuste do modelo GARMA-M para o número de internações diárias de idosos devido a doenças respiratórias de outubro de 2012 a abril de 2015 na cidade de São Paulo, Brasil.
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Somal, Harsimran S. "Heterogeneous computing for the Bayesian hierarchical normal intrinsic conditional autoregressive model with incomplete data." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/2145.
Full textAbstract:
A popular model for spatial association is the conditional autoregressive (CAR) model, and generalizations exist in the literature that utilize intrinsic CAR (ICAR) models within spatial hierarchical models. One generalization is the class of Bayesian hierarchical normal ICAR models, abbreviated HNICAR. The Bayesian HNICAR model can be used to smooth areal or lattice data, estimate the directional strength of spatio-temporal associations, and make posterior predictions at each point in space or time. Furthermore, the Bayesian HNICAR model allows for sample-based posterior inference about model parameters and predictions. R package CARrampsOcl enables fast, independent sampling-based inference for a Bayesian HNICAR model when data are complete and the spatial precision matrix is expressible as a Kronecker sum of lower order matrices. This thesis presents an independent sampling algorithm to accommodate incomplete data and arbitrary precision structures, a parallelized implementation of the algorithm that can be executed on a wide range of hardware, including NVIDIA and AMD graphical processing units (GPUs) and multicore Intel CPUs, analysis of the effects of missingness on the posterior distribution of model parameters and predictive densities, and a survey of model comparison methods for CAR models. The merits of the model and algorithm are demonstrated through both simulation and analysis of an environmental data set.
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Lee, Joo Young, and Youn Mi Lee. "Dynamic Impact of Aging on Income Inequality in the U.S. with Vector Autoregressive Model." Digital Commons @ East Tennessee State University, 2020. https://dc.etsu.edu/secfr-conf/2020/schedule/57.
Full textAbstract:
Income inequality has been showing a steady increase for past decades and will be worsened in the future (Piketty, 2014). One of the most important factors to explain the worsening income inequality can be aging. Previous studies on aging focus on its impact on traditional issues such as health, retirement, and economic growth. This study finds the direct relationship between aging and income inequality using the vector autoregressive (VAR) model (Blanchard and Quah, 1989). The VAR model is useful to analyze the long-run response of aging on income inequality. The empirical results will verify the negative impact of aging on income inequality in the U.S. The governmental efforts to reduce the negative impact of aging on health care and pensions could delay the worsening income inequality.
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Bao, Yan. "The Simultaneous Spatial Autoregressive Model and Its Application in the Housing and Pharmaceutical Markets." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366131746.
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Nyarige, Euna Gesare Verfasser], and Jürgen [Akademischer Betreuer] [Franke. "The Bootstrap for the Functional Autoregressive Model FAR(1) / Euna Gesare Nyarige. Betreuer: Jürgen Franke." Kaiserslautern : Technische Universität Kaiserslautern, 2016. http://d-nb.info/1106250273/34.
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Hsu, Wei-Chih, and 徐偉智. "Identification of Signal Poles of Autoregressive Model Under Noisy Environment." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/57033238609295702239.
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博士國立臺灣大學
電機工程研究所
82
In order to generalize the AR modeling to noisy environment, in this dissertation, we consider the identification problems of AR model-based signals corrupted by a white Gaussian noise. The signals will be considered are the AR process, the complex sinusoids, the damped sinusoids. Our proposed approach first analyzes the observed data by a large order AR spectral estimator to get the zeros of prediction error filter (PEF). Then an identification schemes are developed to extract signal component from the overestimated zeros of the PEF. After the zeros corresponding to the signal of interest have been identified, the desired parameters can be determined subsequently. As for the noisy AR process, the proposed identification scheme combines the advantages of large order AR spectral estimator and the autoregressive moving-averaging (ARMA) modeling. With regard to the complex sinusoidal signal, the excellent resolution capability of the AR frequency estimator (FE) for two equal power sinusoids is investigated theoretically in the domain of PEF''s zeros. We also develop a combined detection-estimation (CDE) selecting procedure and its modified version to determine the estimates of the number and frequencies of the complexes sinusoids from the zeros of the PEF. Moreover, the CDE selecting procedure is generalized to identify the damped sinusoids involved in noise. In order to let the CDE selecting procedure produce more accurate estimates, an improvement filter is proposed to enhance the damped sinusoids from noise so that the signal-to-noise ratio increase. From the results demonstrated in this dissertation, it will be seen that our new methods can provide satisfactory detection and estimation performance even though the data records are short and the noise level is high.
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"IDENTIFICATION OF PERIODIC AUTOREGRESSIVE MOVING AVERAGE MODELS." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1083682/index.pdf.
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El, Ayadi Moataz. "Autoregressive models for text independent speaker identification in noisy environments." Thesis, 2008. http://hdl.handle.net/10012/4036.
Full textAbstract:
The closed-set speaker identification problem is defined as the search within a set of persons for the speaker of a certain
utterance. It is reported that the Gaussian mixture model (GMM) classifier achieves very high classification accuracies (in the
range 95% - 100%) when both the training and testing utterances are recorded in sound proof studio, i.e., there is neither additive
noise nor spectral distortion to the speech signals.
However, in real life applications, speech is usually corrupted by noise and band-limitation. Moreover, there is a mismatch between the recording conditions of the training and testing environments. As a
result, the classification accuracy of GMM-based systems deteriorates significantly. In this thesis, we propose a two-step procedure for improving the speaker identification performance under noisy environment. In the first step, we introduce a new classifier: vector autoregressive Gaussian mixture (VARGM) model. Unlike the
GMM, the new classifier models correlations between successive feature vectors. We also integrate the proposed method into the
framework of the universal background model (UBM). In addition, we develop the learning procedure according to the maximum likelihood
(ML) criterion. Based on a thorough experimental evaluation, the proposed method achieves an improvement of 3 to 5% in the
identification accuracy.
In the second step, we propose a new compensation technique based on the generalized maximum likelihood (GML) decision rule. In particular, we assume a general form for the distribution of the
noise-corrupted utterances, which contains two types of parameters: clean speech-related parameters and noise-related parameters. While the clean speech related parameters are estimated during the
training phase, the noise related parameters are estimated from the corrupted speech in the testing phase. We applied the proposed
method to utterances of 50 speakers selected from the TIMIT database, artificially corrupted by convolutive and additive noise.
The signal to noise ratio (SNR) varies from 0 to 20 dB. Simulation results reveal that the proposed method achieves good robustness
against variation in the SNR. For utterances corrupted by covolutive noise, the improvement in the classification accuracy ranges from 70% for SNR = 0 dB to around 4% for SNR = 10dB, compared to the standard ML decision rule. For utterances corrupted by additive noise, the improvement in the classification accuracy ranges from 1% to 10% for SNRs ranging from 0 to 20 dB.
The proposed VARGM classifier is also applied to the speech emotion classification problem. In particular, we use the Berlin emotional speech database to validate the classification performance of the proposed VARGM classifier. The proposed technique provides a classification accuracy of 76% versus 71% for the hidden Markov model, 67% for the k-nearest neighbors, 55% for feed-forward neural networks. The model gives also better discrimination between
high-arousal emotions (joy, anger, fear), low arousal emotions (sadness, boredom), and neutral emotions than the HMM.
Another interesting application of the VARGM model is the blind equalization of multi input multiple output (MIMO) communication
channels. Based on VARGM modeling of MIMO channels, we propose a four-step equalization procedure. First, the received data vectors are fitted into a VARGM model using the expectation maximization (EM) algorithm. The constructed VARGM model is then used to filter the received data. A Baysian decision rule is then applied to
identify the transmitted symbols up to a permutation and phase ambiguities, which are finally resolved using a small training
sequence. Moreover, we propose a fast and easily implementable model order selection technique. The new equalization algorithm is
compared to the whitening method and found to provide less symbol error probability. The proposed technique is also applied to
frequency-flat slow fading channels and found to provide a more accurate estimate of the channel response than that provided by the blind de-convolution exploiting channel encoding (BDCC) method and at a higher information rate.
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"Model selection for vector autoregressive processes." 2000. http://library.cuhk.edu.hk/record=b5890377.
Full textAbstract:
by May So-Ching Lam.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.
Includes bibliographical references (leaves 87-88).
Abstracts in English and Chinese.
Chapter Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- The importance of Vector Time Series Analysis --- p.1
Chapter 1.2 --- Objective --- p.3
Chapter Chapter 2 --- Vector Autoregressive Models --- p.5
Chapter 2.1 --- The VAR(p) models --- p.5
Chapter 2.2 --- Least square estimation method --- p.7
Chapter 2.3 --- VAR forecast --- p.9
Chapter Chapter 3 --- Model Selection Criteria --- p.12
Chapter 3.1 --- VAR order selection methods --- p.12
Chapter 3.2 --- Hsiao's sequential method --- p.17
Chapter 3.2.1 --- Two variables case --- p.19
Chapter 3.2.2 --- Three variables case --- p.24
Chapter Chapter 4 --- Illustrative Examples --- p.32
Chapter Chapter 5 --- A Simulation Study --- p.37
Chapter 5.1 --- Designs of experiments --- p.37
Chapter 5.2 --- Simulation results --- p.47
Chapter Chapter 6 --- Summary --- p.53
Tables --- p.55
References --- p.87
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"Threshold autoregressive model with multiple threshold variables." 2005. http://library.cuhk.edu.hk/record=b5892601.
Full textAbstract:
Chen Haiqiang.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.
Includes bibliographical references (leaves 33-35).
Abstracts in English and Chinese.
Chapter 1. --- Introduction --- p.1
Chapter 2. --- The Model --- p.4
Chapter 3. --- Least Squares Estimations --- p.6
Chapter 4. --- Inference --- p.7
Chapter 4.1 --- Asymptotic Joint Distribution of the Threshold Estimators --- p.7
Chapter 4.2 --- Testing Threshold Effect: Model Selection Followed by Testing --- p.13
Chapter 5. --- Modeling --- p.16
Chapter 5.1 --- Generic Consistency of the Threshold Estimators under specification errors --- p.17
Chapter 5.2 --- Modeling Procedure --- p.20
Chapter 6. --- Monte Carlo Simulations --- p.21
Chapter 7. --- Empirical Application in the Financial Market --- p.24
Chapter 7.1 --- Data Description --- p.26
Chapter 7.2 --- Estimated Results --- p.26
Chapter 8. --- Conclusion --- p.30
References --- p.33
Appendix 1: Proof of theorem1 --- p.36
Appendix 2: Proof of theorem2 --- p.39
Appendix 3: Proof of theorem3 --- p.43
List of Graph --- p.49
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Chiang, Yu-Yun, and 江昱昀. "Bayesian Inference for Smooth Transition Autoregressive Model." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/9a7duw.
Full textAbstract:
碩士中原大學
應用數學研究所
106
The Bayesian statistics have been successfully commonly applied in many fields. The BUGS project, including OpenBUGS and its Windows version WinBUGS, has been one of the popular Bayesian soft. In BUGS language, users specify a statistical model by simply stating the likelihood function and the prior distributions of the corresponding parameters, then OpenBUGS computationally approximates the estimates of the empirical distribution by the MCMC methods. In this thesis, we consider to analyze the simulated data from smooth transition autoregressive (STAR) model using a newly developed R NIMBLE package, which is also defined in BUGS language and is much faster than OpenBUGS. Finally, the mean squared error of the estimates are computed to demonstrate the effectiveness of the MCMC method.
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Luruli, Fululedzani Lucy. "ARIMA forecasts of the number of beneficiaries of social security grants in South Africa." Diss., 2011. http://hdl.handle.net/10500/5810.
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The main objective of the thesis was to investigate the feasibility of accurately and precisely fore-
casting the number of both national and provincial bene ciaries of social security grants in South
Africa, using simple autoregressive integrated moving average (ARIMA) models. The series of the
monthly number of bene ciaries of the old age, child support, foster care and disability grants from
April 2004 to March 2010 were used to achieve the objectives of the thesis. The conclusions from
analysing the series were that: (1) ARIMA models for forecasting are province and grant-type spe-
ci c; (2) for some grants, national forecasts obtained by aggregating provincial ARIMA forecasts
are more accurate and precise than those obtained by ARIMA modelling national series; and (3)
for some grants, forecasts obtained by modelling the latest half of the series were more accurate
and precise than those obtained from modelling the full series.Mathematical Sciences
M.Sc. (Statistics)
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Tu, Yi-Hsuan, and 杜宜軒. "Modified WIC for Order Selection in Autoregressive Model." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/07897542770727722926.
Full textAbstract:
碩士國立成功大學
統計學系
88
We proposed two modified weighted information criteria （WIC）, WICu and WICs, combining AICu with BIC and SICc, respectively, for order selection in autoregressive model. WICu is essentially equivalent to AICu for small sample sizes and to BIC for large sample sizes. Similarly, WICs is equivalent to SICc for large sample sizes and close to AICu for small sample sizes. Both of them are weakly consistent under general conditions. We also compare WICu and WICs with several popular criteria by simulation study. It shows WICu and WICs are better or at least comparable to WIC. In particular, for small samples, WICu and WICs perform as well as AICu and outperform other criteria, and for large sample sizes, WICu and WICs perform as well as BIC and SIC, respectively, and outperform other criteria.
48
"Change point estimation for threshold autoregressive (TAR) model." 2012. http://library.cuhk.edu.hk/record=b5549066.
Full textAbstract:
時間序列之變點鬥檻模型是一種非線性的模型。此論文探討有關該模型之參數估計，同時對其參數估計作出統計分析。我們運用了遺傳式計算機運算來估計這些參數及對其作出研究。我們利用了MDL來對比不同的變點門檻模型，同時我們也利用了MDL來選取對應的變點門檻模型。This article considers the problem of modeling non-linear time series by using piece-wise TAR model. The numbers of change points, the numbers of thresholds and the corresponding order of AR in each piecewise TAR segments are assumed unknown. The goal is to nd out the “best“ combination of the number of change points, the value of threshold in each time segment, and the underlying AR order for each threshold regime. A genetic algorithm is implemented to solve this optimization problem and the minimum description length principle is applied to compare various segmented TAR. We also show the consistency of the minimal MDL model selection procedure under general regularity conditions on the likelihood function.
Detailed summary in vernacular field only.
Tang, Chong Man.
Thesis (M.Phil.)--Chinese University of Hong Kong, 2012.
Includes bibliographical references (leaves 45-47).
Abstracts also in Chinese.
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Introduction --- p.1
Chapter 2 --- Minimum Description Length for Pure TAR --- p.4
Chapter 2.1 --- Model selection using Minimum Description Length for Pure TAR --- p.4
Chapter 2.1.1 --- Derivation of Minimum Description Length for Pure TAR --- p.5
Chapter 2.2 --- Optimization Using Genetic Algorithms (GA) --- p.7
Chapter 2.2.1 --- General Description --- p.7
Chapter 2.2.2 --- Implementation Details --- p.9
Chapter 3 --- Minimum Description Length for TAR models with structural change --- p.13
Chapter 3.1 --- Model selection using Minimum Description Length for TAR models with structural change --- p.13
Chapter 3.1.1 --- Derivation of Minimum Description Length for TAR models with structural change --- p.14
Chapter 3.2 --- Optimization Using Genetic Algorithms --- p.17
Chapter 4 --- Main Result --- p.20
Chapter 4.1 --- Main results --- p.20
Chapter 4.1.1 --- Model Selection using minimum description length --- p.21
Chapter 5 --- Simulation Result --- p.24
Chapter 5.1 --- Simulation results --- p.24
Chapter 5.1.1 --- Example of TAR Model Without Structural Break --- p.24
Chapter 5.1.2 --- Example of TAR Model With Structural Break I --- p.26
Chapter 5.1.3 --- Example of TAR Model With Structural Break II --- p.29
Chapter 6 --- An empirical example --- p.33
Chapter 6.1 --- An empirical example --- p.33
Chapter 7 --- Consistency of the CLSE --- p.36
Chapter 7.1 --- Consistency of the TAR parameters --- p.36
Chapter 7.1.1 --- Consistency of the estimation of number of threshold --- p.36
Chapter 7.1.2 --- Consistency of the change point parameters --- p.43
Bibliography --- p.45
49
Huang, Huei-Chuan, and 黃慧釧. "On Continuous Time Threshold Autoregressive Model : a BayesianApplication." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/46853031482626088317.
Full textAbstract:
碩士中原大學
應用數學研究所
94
The non-linear continuous time models have important applications in time series analysis. However, their likelihood functions are usually not available. As a result, the analysis is trickier than that of their discrete time conunterparts. In this study, we use the Bayesian method to analyze the continuous time threshold autoregressive models. This approach is based on Roberts and Stramer (2001) and Lin (2003). In the applications to financial data S&P 500, we assume the daily data are not equally spaced since the stock market only opens on weekdays and we treat the paths between observed data are missing.
50
LU, SHI-PING, and 盧世屏. "An autoregressive-moving average model for shape analysis." Thesis, 1988. http://ndltd.ncl.edu.tw/handle/58917998612493413928.
Full text