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Dupré, la Tour Tom. "Nonlinear models for neurophysiological time series." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLT018/document.
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Dans les séries temporelles neurophysiologiques, on observe de fortes oscillations neuronales, et les outils d'analyse sont donc naturellement centrés sur le filtrage à bande étroite.Puisque cette approche est trop réductrice, nous proposons de nouvelles méthodes pour représenter ces signaux.Nous centrons tout d'abord notre étude sur le couplage phase-amplitude (PAC), dans lequel une bande haute fréquence est modulée en amplitude par la phase d'une oscillation neuronale plus lente.Nous proposons de capturer ce couplage dans un modèle probabiliste appelé modèle autoregressif piloté (DAR). Cette modélisation permet une sélection de modèle efficace grâce à la mesure de vraisemblance, ce qui constitue un apport majeur à l'estimation du PAC.%Nous présentons différentes paramétrisations des modèles DAR et leurs algorithmes d'inférence rapides, et discutons de leur stabilité.Puis nous montrons comment utiliser les modèles DAR pour l'analyse du PAC, et démontrons l'avantage de l'approche par modélisation avec trois jeux de donnée.Puis nous explorons plusieurs extensions à ces modèles, pour estimer le signal pilote à partir des données, le PAC sur des signaux multivariés, ou encore des champs réceptifs spectro-temporels.Enfin, nous proposons aussi d'adapter les modèles de codage parcimonieux convolutionnels pour les séries temporelles neurophysiologiques, en les étendant à des distributions à queues lourdes et à des décompositions multivariées. Nous développons des algorithmes d'inférence efficaces pour chaque formulations, et montrons que l'on obtient de riches représentations de façon non-superviséeIn neurophysiological time series, strong neural oscillations are observed in the mammalian brain, and the natural processing tools are thus centered on narrow-band linear filtering.As this approach is too reductive, we propose new methods to represent these signals.We first focus on the study of phase-amplitude coupling (PAC), which consists in an amplitude modulation of a high frequency band, time-locked with a specific phase of a slow neural oscillation.We propose to use driven autoregressive models (DAR), to capture PAC in a probabilistic model. Giving a proper model to the signal enables model selection by using the likelihood of the model, which constitutes a major improvement in PAC estimation.%We first present different parametrization of DAR models, with fast inference algorithms and stability discussions.Then, we present how to use DAR models for PAC analysis, demonstrating the advantage of the model-based approach on three empirical datasets.Then, we explore different extensions to DAR models, estimating the driving signal from the data, PAC in multivariate signals, or spectro-temporal receptive fields.Finally, we also propose to adapt convolutional sparse coding (CSC) models for neurophysiological time-series, extending them to heavy-tail noise distribution and multivariate decompositions. We develop efficient inference algorithms for each formulation, and show that we obtain rich unsupervised signal representations
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Li, Dao. "Common Features in Vector Nonlinear Time Series Models." Doctoral thesis, Högskolan Dalarna, Statistik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:du-13253.
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This thesis consists of four manuscripts in the area of nonlinear time series econometrics on topics of testing, modeling and forecasting nonlinear common features. The aim of this thesis is to develop new econometric contributions for hypothesis testing and forecasting in these area. Both stationary and nonstationary time series are concerned. A definition of common features is proposed in an appropriate way to each class. Based on the definition, a vector nonlinear time series model with common features is set up for testing for common features. The proposed models are available for forecasting as well after being well specified. The first paper addresses a testing procedure on nonstationary time series. A class of nonlinear cointegration, smooth-transition (ST) cointegration, is examined. The ST cointegration nests the previously developed linear and threshold cointegration. An Ftypetest for examining the ST cointegration is derived when stationary transition variables are imposed rather than nonstationary variables. Later ones drive the test standard, while the former ones make the test nonstandard. This has important implications for empirical work. It is crucial to distinguish between the cases with stationary and nonstationary transition variables so that the correct test can be used. The second and the fourth papers develop testing approaches for stationary time series. In particular, the vector ST autoregressive (VSTAR) model is extended to allow for common nonlinear features (CNFs). These two papers propose a modeling procedure and derive tests for the presence of CNFs. Including model specification using the testing contributions above, the third paper considers forecasting with vector nonlinear time series models and extends the procedures available for univariate nonlinear models. The VSTAR model with CNFs and the ST cointegration model in the previous papers are exemplified in detail,and thereafter illustrated within two corresponding macroeconomic data sets.
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黃鎮山 and Chun-shan Wong. "Statistical inference for some nonlinear time series models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31239444.
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Wong, Chun-shan. "Statistical inference for some nonlinear time series models /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B20715316.
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Sando, Simon Andrew. "Estimation of a class of nonlinear time series models." Thesis, Queensland University of Technology, 2004. https://eprints.qut.edu.au/15985/1/Simon_Sando_Thesis.pdf.
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The estimation and analysis of signals that have polynomial phase and constant or time-varying amplitudes with the addititve noise is considered in this dissertation.Much work has been undertaken on this problem over the last decade or so, and there are a number of estimation schemes available. The fundamental problem when trying to estimate the parameters of these type of signals is the nonlinear characterstics of the signal, which lead to computationally difficulties when applying standard techniques such as maximum likelihood and least squares. When considering only the phase data, we also encounter the well known problem of the unobservability of the true noise phase curve.
The methods that are currently most popular involve differencing in phase followed by regression, or nonlinear transformations. Although these methods perform quite well at high signal to noise ratios, their performance worsens at low signal to noise, and there may be significant bias. One of the biggest problems to efficient estimation of these models is that the majority of methods rely on sequential estimation of the phase coefficients, in that the highest-order parameter is estimated first, its contribution removed via demodulation, and the same procedure applied to estimation of the next parameter and so on. This is clearly an issue in that errors in estimation of high order parameters affect the ability to estimate the lower order parameters correctly. As a result, stastical analysis of the parameters is also difficult.
In thie dissertation, we aim to circumvent the issues of bias and sequential estiamtion by considering the issue of full parameter iterative refinement techniques. ie. given a possibly biased initial estimate of the phase coefficients, we aim to create computationally efficient iterative refinement techniques to produce stastically efficient estimators at low signal to noise ratios. Updating will be done in a multivariable manner to remove inaccuracies and biases due to sequential procedures.
Stastical analysis and extensive simulations attest to the performance of the schemes that are presented, which include likelihood, least squares and bayesian estimation schemes. Other results of importance to the full estimatin problem, namely when there is error in the time variable, the amplitude is not constant, and when the model order is not known, are also condsidered.
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Sando, Simon Andrew. "Estimation of a class of nonlinear time series models." Queensland University of Technology, 2004. http://eprints.qut.edu.au/15985/.
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The estimation and analysis of signals that have polynomial phase and constant or time-varying amplitudes with the addititve noise is considered in this dissertation.Much work has been undertaken on this problem over the last decade or so, and there are a number of estimation schemes available. The fundamental problem when trying to estimate the parameters of these type of signals is the nonlinear characterstics of the signal, which lead to computationally difficulties when applying standard techniques such as maximum likelihood and least squares. When considering only the phase data, we also encounter the well known problem of the unobservability of the true noise phase curve. The methods that are currently most popular involve differencing in phase followed by regression, or nonlinear transformations. Although these methods perform quite well at high signal to noise ratios, their performance worsens at low signal to noise, and there may be significant bias. One of the biggest problems to efficient estimation of these models is that the majority of methods rely on sequential estimation of the phase coefficients, in that the highest-order parameter is estimated first, its contribution removed via demodulation, and the same procedure applied to estimation of the next parameter and so on. This is clearly an issue in that errors in estimation of high order parameters affect the ability to estimate the lower order parameters correctly. As a result, stastical analysis of the parameters is also difficult. In thie dissertation, we aim to circumvent the issues of bias and sequential estiamtion by considering the issue of full parameter iterative refinement techniques. ie. given a possibly biased initial estimate of the phase coefficients, we aim to create computationally efficient iterative refinement techniques to produce stastically efficient estimators at low signal to noise ratios. Updating will be done in a multivariable manner to remove inaccuracies and biases due to sequential procedures. Stastical analysis and extensive simulations attest to the performance of the schemes that are presented, which include likelihood, least squares and bayesian estimation schemes. Other results of importance to the full estimatin problem, namely when there is error in the time variable, the amplitude is not constant, and when the model order is not known, are also condsidered.
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Ainkaran, Ponnuthurai. "Analysis of Some Linear and Nonlinear Time Series Models." Thesis, The University of Sydney, 2004. http://hdl.handle.net/2123/582.
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Abstract This thesis considers some linear and nonlinear time series models. In the linear case, the analysis of a large number of short time series generated by a first order autoregressive type model is considered. The conditional and exact maximum likelihood procedures are developed to estimate parameters. Simulation results are presented and compare the bias and the mean square errors of the parameter estimates. In Chapter 3, five important nonlinear models are considered and their time series properties are discussed. The estimating function approach for nonlinear models is developed in detail in Chapter 4 and examples are added to illustrate the theory. A simulation study is carried out to examine the finite sample behavior of these proposed estimates based on the estimating functions.
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Ainkaran, Ponnuthurai. "Analysis of Some Linear and Nonlinear Time Series Models." University of Sydney. Mathematics & statistics, 2004. http://hdl.handle.net/2123/582.
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Abstract This thesis considers some linear and nonlinear time series models. In the linear case, the analysis of a large number of short time series generated by a first order autoregressive type model is considered. The conditional and exact maximum likelihood procedures are developed to estimate parameters. Simulation results are presented and compare the bias and the mean square errors of the parameter estimates. In Chapter 3, five important nonlinear models are considered and their time series properties are discussed. The estimating function approach for nonlinear models is developed in detail in Chapter 4 and examples are added to illustrate the theory. A simulation study is carried out to examine the finite sample behavior of these proposed estimates based on the estimating functions.
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Pitrun, Ivet 1959. "A smoothing spline approach to nonlinear inference for time series." Monash University, Dept. of Econometrics and Business Statistics, 2001. http://arrow.monash.edu.au/hdl/1959.1/8367.
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Batten, Douglas James. "Nonlinear time series modeling of some Canadian river flow data." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ54860.pdf.
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Lin, Zhongli. "On the statistical inference of some nonlinear time series models." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43757625.
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Jin, Shusong, and 金曙松. "Nonlinear time series modeling with application to finance and other fields." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B3199605X.
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Lin, Zhongli, and 林中立. "On the statistical inference of some nonlinear time series models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43757625.
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Sofia, Stefano. "Nonlinear time-series models for Mediterranean rainfall data with zeroes." Thesis, University of Sunderland, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.439975.
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Zeng, Songlin. "Nonlinear Time Series Models with Applications in Macroeconomics and Finance." Thesis, Cergy-Pontoise, 2013. http://www.theses.fr/2013CERG0638.
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Les trois chapitres suivants examinent: 1) si les taux de change réels d'Asie du Sud-Est sont nonlinéaire, 2) l'inférence bayésienne sur le modèle de série temporelle nonlinéaire avec des applications sur le taux de change réel,et 3) la cyclicité et effet de rebond dans le marché boursier.Depuis la fin des années nonante, les analyses théorique et empirique consacrée au taux de change réel suggèrent que la dynamique pourrait être bien estimés par les modèles non linéaires. Le premier chapitre examine cette possibilité utilisant les données mensuelles de l'ASEAN-5, et il s'étend la recherche existante dans deux directions. Tout d'abord, nous utilisons récemment mis au point des tests de racine unitaire ce qui permettra d'assouplir les modèles non linéaires stationnaires dans le cadre du d'autre alternative que l'couramment utilisés à SETAR ou ESTAR modèle. Deuxièmement, bien que différents modèles nonlinéaires survivre aux tests de mis-spécification, une expérience Monte Carlo à partir de généralisées fonctions de réponse impulsionnelle est utilisé pour comparer leur pertinence relative. Nos résultats i) soutenir l'hypothèse de retour nonlinéaire à la moyenne , et donc la parité de pouvoir d'achat, dans la moitié des cas et ii) indiquent MRLSTAR et ESTAR comme les plus probables processus générant des taux de change réels.Le deuxième chapitre analyse ACR modèle. Nous proposons une approche bayésienne complète d'inférence et une attention particulière est portée sur les paramètres des variables de seuil. Nous discutons le choix des distributions a priori et proposer une chaîne de Markov algorithme de Monte Carlo pour estimer les paramètres et les variables latentes. Une étude de simulation et de l'application à des données taux de change réelles illustrer l'analyse.Le troisième chapitre explore que les différentes formes de recouvrements dans les marchés financiers peuvent présenter dans un modèle de Markov Switching. Elle s'appuie sur les effets de rebond d'abord analysé par Kim, Morley et Piger [2005] dans le cycle des affaires et généralisé par Bec, Bouabdallah et Ferrara [2011] pour permettre une plus souple de type rebond.Nos résultats i) montrer que l'effet de rebond est statistiquement significative et importante dans tous les cas, mais l'Allemagne où la preuve est moins claire et ii) l'impact négatif permanent de marchés baissiers sur l'indice est notablement réduite lorsque le rebond est explicitement pris en compteThe following three chapters investigate: 1) whether Southeast Asian real exchange rates are nonlinear mean reverting, 2) bayesian inference on nonlinear time series model with applications in real exchange rate, and 3)cyclicality and bounce-back effect in stock market. Since the late nineties, both theoretical and empirical analyses devoted to the real exchange rate suggest that their dynamics might be well approximated by nonlinear models. This paper examines this possibility for post-1970 monthly ASEAN-5 data, extending the existing research in two directions. First, we use recently developed unit root tests which allow for more flexible nonlinear stationary models under the alternative than the commonly used Self-Exciting Threshold or Exponential Smooth Transition AutoRegressions. Second, while different nonlinear models survive the mis-specification tests, a Monte Carlo experiment from generalized impulse response functions is used to compare their relative relevance. Our results support the nonlinear mean-reverting hypothesis, and hence the Purchasing Power Parity, in half the cases and point to the Multiple Regime-Logistic Smooth Transition and the Self-Exciting Threshold AutoRegressive models as the most likely data generating processes of these real exchange rates.Various nonlinear threshold models are employed to mimic the real exchange rate dynamics. A natural question arises: Which model does the best job of modeling the real exchange rate process? It is difficult and not straightforward to formally compare the nonlinear models within classic approach. In the second chapter, we propose to use Bayesian approach to address this issue. The second part of my dissertation actually uses a Bayesian method to estimate some nonlinear time series models, the ACR model, SETAR model, and MAR model. We propose a full Bayesian inference approach and particular attention is paid to the parameters of the threshold variables. We discuss the choice of the prior distributions and propose a Markov-chain Monte Carlo algorithm for estimating both the parameters and the latent variables. A simulation study and the application to real exchange rate data illustrate the analysis. Our empirical results of the second chapter show that i) Bayesian estimations closely match those of the Maximum likelihood for French real exchange rate vis-a-vis Deutsche Mark; ii)the speed of real exchange rate's adjustment to equilibrium level is overestimated if heterogeneous variances in two regimes is not taken into account; iii) ACR model is preferred to other nonlinear threshold models, SETAR and MAR; iv) within ACR class models, the suitable transition function form is selected based on Bayes factor.This paper proposes an empirical study of the shape of recoveries in financial markets from a bounce-back augmented Markov Switching model. It relies on models first applied by Kim, Morley et Piger [2005] to the business cycle analysis. These models are estimated for monthly stock market returns data of five developed countries for the post-1970 period. Focusing on a potential bounce-back effect in financial markets, its presence and shape are formally tested. Our results show that i) the bounce-back effect is statistically significant and large in all countries, but Germany where evidence is less clear-cut and ii) the negative permanent impact of bear markets on the stock price index is notably reduced when the rebound is explicitly taken into account
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Lyman, Mark B. "A modified cluster-weighted approach to nonlinear time series /." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1945.pdf.
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Basu, Deepankar. "Essays on Dynamic Nonlinear Time Series Models and on Gender Inequality." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1211331801.
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Li, Guodong. "On some nonlinear time series models and the least absolute deviation estimation." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B3878239X.
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Li, Guodong, and 李國棟. "On some nonlinear time series models and the least absolute deviation estimation." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B3878239X.
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Nakamura, Tomomichi. "Modelling nonlinear time series using selection methods and information criteria." University of Western Australia. School of Mathematics and Statistics, 2004. http://theses.library.uwa.edu.au/adt-WU2004.0085.
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[Truncated abstract] Time series of natural phenomena usually show irregular fluctuations. Often we want to know the underlying system and to predict future phenomena. An effective way of tackling this task is by time series modelling. Originally, linear time series models were used. As it became apparent that nonlinear systems abound in nature, modelling techniques that take into account nonlinearity in time series were developed. A particularly convenient and general class of nonlinear models is the pseudolinear models, which are linear combinations of nonlinear functions. These models can be obtained by starting with a large dictionary of basis functions which one hopes will be able to describe any likely nonlinearity, selecting a small subset of it, and taking a linear combination of these to form the model. The major component of this thesis concerns how to build good models for nonlinear time series. In building such models, there are three important problems, broadly speaking. The first is how to select basis functions which reflect the peculiarities of the time series as much as possible. The second is how to fix the model size so that the models can reflect the underlying system of the data and the influences of noise included in the data are removed as much as possible. The third is how to provide good estimates for the parameters in the basis functions, considering that they may have significant bias when the noise included in the time series is significant relative to the nonlinearity. Although these problems are mentioned separately, they are strongly interconnected
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Sandberg, Rickard. "Testing the unit root hypothesis in nonlinear time series and panel models." Doctoral thesis, Handelshögskolan i Stockholm, Ekonomisk Statistik (ES), 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-536.
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The thesis contains the four chapters: Testing parameter constancy in unit root autoregressive models against continuous change; Dickey-Fuller type of tests against nonlinear dynamic models; Inference for unit roots in a panel smooth transition autoregressive model where the time dimension is fixed; Testing unit roots in nonlinear dynamic heterogeneous panels. In Chapter 1 we derive tests for parameter constancy when the data generating process is non-stationary against the hypothesis that the parameters of the model change smoothly over time. To obtain the asymptotic distributions of the tests we generalize many theoretical results, as well as new are introduced, in the area of unit roots . The results are derived under the assumption that the error term is a strong mixing. Small sample properties of the tests are investigated, and in particular, the power performances are satisfactory. In Chapter 2 we introduce several test statistics of testing the null hypotheses of a random walk (with or without drift) against models that accommodate a smooth nonlinear shift in the level, the dynamic structure, and the trend. We derive analytical limiting distributions for all tests. Finite sample properties are examined. The performance of the tests is compared to that of the classical unit root tests by Dickey-Fuller and Phillips and Perron, and is found to be superior in terms of power. In Chapter 3 we derive a unit root test against a Panel Logistic Smooth Transition Autoregressive (PLSTAR). The analysis is concentrated on the case where the time dimension is fixed and the cross section dimension tends to infinity. Under the null hypothesis of a unit root, we show that the LSDV estimator of the autoregressive parameter in the linear component of the model is inconsistent due to the inclusion of fixed effects. The test statistic, adjusted for the inconsistency, has an asymptotic normal distribution whose first two moments are calculated analytically. To complete the analysis, finite sample properties of the test are examined. We highlight scenarios under which the traditional panel unit root tests by Harris and Tzavalis have inferior or reasonable power compared to our test. In Chapter 4 we present a unit root test against a non-linear dynamic heterogeneous panel with each country modelled as an LSTAR model. All parameters are viewed as country specific. We allow for serially correlated residuals over time and heterogeneous variance among countries. The test is derived under three special cases: (i) the number of countries and observations over time are fixed, (ii) observations over time are fixed and the number of countries tend to infinity, and (iii) first letting the number of observations over time tend to infinity and thereafter the number of countries. Small sample properties of the test show modest size distortions and satisfactory power being superior to the Im, Pesaran and Shin t-type of test. We also show clear improvements in power compared to a univariate unit root test allowing for non-linearities under the alternative hypothesis.Diss. Stockholm : Handelshögskolan, 2004
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Jin, Shusong. "Nonlinear time series modeling with application to finance and other fields." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B3199605X.
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MENDES, EDUARDO FONSECA. "MODELING NONLINEAR TIME SERIES WITH A TREE-STRUCTURED MIXTURE OF GAUSSIAN MODELS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9689@1.
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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIORNeste trabalho um novo modelo de mistura de distribuições é proposto, onde a estrutura da mistura é determinada por uma árvore de decisão com transição suave. Modelos baseados em mistura de distribuições são úteis para aproximar distribuições condicionais desconhecidas de dados multivariados. A estrutura em árvore leva a um modelo que é mais simples, e em alguns casos mais interpretável, do que os propostos anteriormente na literatura. Baseando-se no algoritmo de Esperança- Maximização (EM), foi derivado um estimador de quasi- máxima verossimilhança. Além disso, suas propriedades assintóticas são derivadas sob condições de regularidades. Uma estratégia de crescimento da árvore, do especifico para o geral, é também proposta para evitar possíveis problemas de identificação. Tanto a estimação quanto a estratégia de crescimento são avaliados em um experimento Monte Carlo, mostrando que a teoria ainda funciona para pequenas amostras. A habilidade de aproximação universal é ainda analisada em experimentos de simulação. Para concluir, duas aplicações com bases de dados reais são apresentadas.
In this work a new model of mixture of distributions is proposed, where the mixing structure is determined by a smooth transition tree architecture. Models based on mixture of distributions are useful in order to approximate unknown conditional distributions of multivariate data. The tree structure yields a model that is simpler, and in some cases more interpretable, than previous proposals in the literature. Based on the Expectation-Maximization (EM) algorithm a quasi-maximum likelihood estimator is derived and its asymptotic properties are derived under mild regularity conditions. In addition, a specific-to-general model building strategy is proposed in order to avoid possible identification problems. Both the estimation procedure and the model building strategy are evaluated in a Monte Carlo experiment, which give strong support for the theorydeveloped in small samples. The approximation capabilities of the model is also analyzed in a simulation experiment. Finally, two applications with real datasets are considered.
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Lee, Kian Lam. "Nonlinear time series modelling and prediction using polynomial and radial basis function expansions." Thesis, University of Sheffield, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.246940.
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Yang, Fuyu. "Bayesian inference in nonlinear univariate time series : investigation of GSTUR and SB models." Thesis, University of Leicester, 2009. http://hdl.handle.net/2381/4375.
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In the literature, many statistical models have been used to investigate the existence of a deterministic time trend, changing persistence and nonlinearity in macroeconomic and financial data. Good understanding of these properties in a univariate time series model is crucial when making forecasts. Forecasts are used in various ways, such as helping to control risks in financial institutions and to assist in setting monetary policies in central banks. Hence, evaluating the forecast capacities of statistical models, quantifying and reducing forecast uncertainties are the main concerns of forecast practitioners. In this thesis, we propose two flexible parametric models that allow for autoregressive parameters to be time varying. One is a novel Generalised Stochastic Unit Root (GSTUR) model and the other is a Stationary Bilinear (SR) model. Bayesian inference in these two models are developed using methods on the frontier of numerical analysis. Programs, including model estimation with Markov chain Monte Carlo (MCMC), model comparison with Bayes Factors, model forecasting and Forecast Model Averaging, are developed and made available to meet the demand of economic modelers. With an application to the S&P 500 series, we found strong evidences of a deterministic trend when we allow the persistence to change with time. By fitting the GSTUR model to monthly UK/US real exchange rate data, the Purchasing Power Parity (PPP) theory is revisited. Our findings of a changing persistence in the data suggest that the GSTUR model may reconcile the empirical findings of nonstationarity in real exchange rates with the PPP theory. The forecasting capacities of a group of nonlinear and linear models are evaluated with an application to UK inflation rates. We propose a GSTUR model to be applied with data, which contains as much information as possible, for forecasting near-term inflation rates.
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Dacco, Roberto. "Switching regimes and threshold effect : an empirical analysis." Thesis, Birkbeck (University of London), 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.243296.
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Gurung, Ai Bahadur. "Analysis and prediction of hydrometeorological time series by dynamical system approach." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31240203.
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Strikholm, Birgit. "Essays on nonlinear time series modelling och hypothesis testing." Doctoral thesis, Handelshögskolan i Stockholm, Ekonomisk Statistik (ES), 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-535.
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There seems to be a common understanding nowadays that the economy is nonlinear. Economic theory suggests features that can not be incorporated into linear frameworks, and over the decades a solid body of empirical evidence of nonlinearities in economic time series has been gathered. This thesis consists of four essays that have to do with various forms of nonlinear statistical inference. In the first chapter the problem of determining the number regimes in a threshold autoregressive (TAR) model is considered. Typically, the number of regimes (or thresholds) is assumed unknown and has to be determined from the data. The solution provided in the chapter first uses the smooth transition autoregressive (STAR) model with a fixed and rapid transition to approximate the TAR model. The number of thresholds is then determined using sequential misspecification tests developed for the STAR model. The main characteristic of the proposed method is that only standard statistical inference is used, as opposed to non-standard inference or computation intensive bootstrap-based methods. In the second chapter a similar idea is employed and the structural break model is approximated with a smoothly time-varying autoregressive model. By making the smooth changes in parameters rapid, the model is able to closely approximate the corresponding model with breaks in the parameter structure. This approximation makes the misspecification tests developed for the STR modelling framework available and they can be used for sequentially determining the number of breaks. Again, the method is computationally simple as all tests rely on standard statistical inference. There exists literature suggesting that business cycle fluctuations affect the pattern of seasonality in macroeconomic series. A question asked in the third chapter is whether other factors such as changes in institutions or technological change may have this effect as well. The time-varying smooth transition autoregressive (TV- STAR) models that can incorporate both types of change are used to model the (possible) changes in seasonal patterns and shed light on the hypothesis that institutional and technological changes (proxied by time) may have a stronger effect on seasonal patterns than business cycle. The TV-STAR testing framework is applied to nine quarterly industrial production series from the G7 countries, Finland and Sweden. These series display strong seasonal patterns and also contain the business cycle fluctuations. The empirical results of the chapter suggest that seasonal patterns in these series have been changing over time and, furthermore, that the business cycle fluctuations do not seem to be the main cause for this change. The last chapter of the thesis considers the possibility of testing for Granger causality in bivariate nonlinear systems when the exact form of the nonlinear relationship between variables is not known. The idea is to linearize the testing problem by approximating the nonlinear system by its Taylor expansion. The expansion is linear in parameters and one gets round the difficulty caused by the unknown functional form of the relationship under investigation.Diss. Stockholm : Handelshögskolan, 2004
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Addo, Peter Martey. "Modern approaches for nonlinear data analysis of economic and financial time series." Thesis, Paris 1, 2014. http://www.theses.fr/2014PA010033/document.
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L’axe principal de la thèse est centré sur des approches non-linéaires modernes d’analyse des données économiques et financières, avec une attention particulière sur les cycles économiques et les crises financières. Un consensus dans la littérature statistique et financière s’est établie autour du fait que les variables économiques ont un comportement non-linéaire au cours des différentes phases du cycle économique. En tant que tel, les approches/modèles non-linéaires sont requis pour saisir les caractéristiques du mécanisme de génération des données intrinsèquement asymétriques, que les modèles linéaires sont incapables de reproduire.À cet égard, la thèse propose une nouvelle approche interdisciplinaire et ouverte à l’analyse des systèmes économiques et financiers. La thèse présente des approches robustes aux valeurs extrêmes et à la non-stationnarité, applicables à la fois pour des petits et de grands échantillons, aussi bien pour des séries temporelles économiques que financières. La thèse fournit des procédures dites étape par étape dans l’analyse des indicateurs économiques et financiers en intégrant des concepts basés sur la méthode de substitution de données, des ondelettes, espace incorporation de phase, la m´méthode retard vecteur variance (DVV) et des récurrences parcelles. La thèse met aussi en avant des méthodes transparentes d’identification, de datation des points de retournement et de l´évaluation des impacts des crises économiques et financières. En particulier, la thèse fournit également une procédure pour anticiper les crises futures et ses conséquences.L’étude montre que l’intégration de ces techniques dans l’apprentissage de la structure et des interactions au sein et entre les variables économiques et financières sera très utile dans l’élaboration de politiques de crises, car elle facilite le choix des méthodes de traitement appropriées, suggérées par les données.En outre, une nouvelle procédure pour tester la linéarité et la racine unitaire dans un cadre non-linéaire est proposé par l’introduction d’un nouveau modèle – le modèle MT-STAR – qui a des propriétés similaires au modèle ESTAR mais réduit les effets des problèmes d’identification et peut aussi représenter l’asymétrie dans le mécanisme d’ajustement vers l’équilibre. Les distributions asymptotiques du test de racine unitaire proposées sont non-standards et sont calculées. La puissance du test est évaluée par simulation et quelques illustrations empiriques sur les taux de change réel montrent son efficacité. Enfin, la thèse développe des modèles multi-variés Self-Exciting Threshold Autoregressive avec des variables exogènes (MSETARX) et présente une méthode d’estimation paramétrique. La modélisation des modèles MSETARX et des problèmes engendrés par son estimation sont brièvement examinésThis thesis centers on introducing modern non-linear approaches for data analysis in economics and finance with special attention on business cycles and financial crisis. It is now well stated in the statistical and economic literature that major economic variables display non-linear behaviour over the different phases of the business cycle. As such, nonlinear approaches/models are required to capture the features of the data generating mechanism of inherently asymmetric realizations, since linear models are incapable of generating such behavior.In this respect, the thesis provides an interdisciplinary and open-minded approach to analyzing economic and financial systems in a novel way. The thesis presents approaches that are robust to extreme values, non-stationarity, applicable to both short and long data length, transparent and adaptive to any financial/economic time series. The thesis provides step-by-step procedures in analyzing economic/financial indicators by incorporating concepts based on surrogate data method, wavelets, phase space embedding, ’delay vector variance’ (DVV) method and recurrence plots. The thesis also centers on transparent ways of identifying, dating turning points, evaluating impact of economic and financial crisis. In particular, the thesis also provides a procedure on how to anticipate future crisis and the possible impact of such crisis. The thesis shows that the incorporation of these techniques in learning the structure and interactions within and between economic and financial variables will be very useful in policy-making, since it facilitates the selection of appropriate processing methods, suggested by the data itself.In addition, a novel procedure to test for linearity and unit root in a nonlinear framework is proposed by introducing a new model – the MT-STAR model – which has similar properties of the ESTAR model but reduces the effects of the identification problem and can also account for asymmetry in the adjustment mechanism towards equilibrium. The asymptotic distributions of the proposed unit root test is non-standard and is derived.The power of the test is evaluated through a simulation study and some empirical illustrations on real exchange rates show its accuracy. Finally, the thesis defines a multivariate Self–Exciting Threshold Autoregressive with eXogenous input (MSETARX) models and present an estimation procedure for the parameters. The modeling procedure for the MSETARX models and problems of estimation are briefly considered
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Troughton, Paul Thomas. "Simulation methods for linear and nonlinear time series models with application to distorted audio signals." Thesis, University of Cambridge, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.624586.
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Han, Ngai Sze. "Goodness-of-fit test for non-linear time series model /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?MATH%202002%20HAN.
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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2002.Includes bibliographical references (leaves 45-48). Also available in electronic version. Access restricted to campus users.
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Zhou, Jia. "SMOOTH TRANSITION AUTOREGRESSIVE MODELS : A STUDY OF THE INDUSTRIAL PRODUCTION INDEX OF SWEDEN." Thesis, Uppsala University, Department of Statistics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-126752.
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In this paper, we study the industrial production index of Sweden from Jan, 2000 to latest Feb, 2010. We find out there is a structural break at time point Dec, 2007, when the global financial crisis burst out first in U.S then spread to Europe. To model the industrial production index, one of the business cycle indicators which may behave nonlinear feature suggests utilizing a smooth transition autoregressive (STAR) model. Following the procedures given by Teräsvirta (1994), we carry out the linearity test against the STAR model, determine the delay parameter and choose between the LSTAR model and the ESTAR model. The results from the estimated model suggest the STAR model is better performing than the linear autoregressive model.
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Belkhouja, Mustapha. "Modelling nonlinearities in long-memory time series : simulation and empirical studies." Thesis, Aix-Marseille 2, 2010. http://www.theses.fr/2010AIX24010/document.
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Cette thèse porte sur l'identification et l'estimation des ruptures structurelles pouvant affecter des données économiques et financières à mémoire longue. Notre étude s'est limitée dans les trois premiers chapitres au cadre univarié où nous avons modélisé la dépendance de long terme et les changements structurels simultanément et séparément au niveau de la moyenne ainsi que la volatilité. Dans un premier temps nous n'avons tenu compte que des sauts instantanés d'état ensuite nous nous sommes intéressés à la possibilité d'avoir des changements graduels et lisses au cours du temps grâce à des modèles nonlinéaires plus complexes. Par ailleurs, des expériences de simulation ont été menées dans le but d'offrir une analyse comparative des méthodes utilisées et d'attester de la robustesse des tests sous certaines conditions telle que la présence de la mémoire longue dans la série. Ce travail s'est achevé sur une extension aux modèles multivariés.Ces modèles permettent de rendre compte des mécanismes de propagation d'une variation d'une série sur l'autre et d'identifier les liens entre les variables ainsi que la nature des ces liens. Les interactions entre les différentes variables financières ont été analysées tant à court terme qu'à long terme. Bien que le concept du changement structurel n'a pas été abordé dans ce dernier chapitre, nous avons pris en compte l'effet d'asymétrie et de mémoire longue dans la modélisation de la volatilitéThis dissertation deals with the detection and the estimation of structural changes in long memory economic and financial time series. Within the rest three chapters we focused on the univariate case to model both the long range dependence and structural changes in the mean and the volatility of the examined series. In the beginning we just take into account abrupt regime switches but after we use more developed nonlinear models in order to capture the smooth time variations of the dynamics. Otherwise we analyse the efficiency of various techniques permitting to select the number of breaks and we assess the robustness of the used tests in a long memory environment via simulations. Last, this thesis was completed by an extension to multivariate models. These models allow us to detect the impact of some series on the others and identify the relationships among them. The interdependencies between the financial variables were studied and analysed both in the short and the long range. While structural changes were not considered in the last chapter, our multivariate model takes into account asymmetry effects and the long memory behaviour in the volatility
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Kilminster, Devin. "Modelling dynamical systems via behaviour criteria." University of Western Australia. Dept. of Mathematics and Statistics, 2002. http://theses.library.uwa.edu.au/adt-WU2003.0029.
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An important part of the study of dynamical systems is the fitting of models to time-series data. That is, given the data, a series of observations taken from a (not fully understood) system of interest, we would like to specify a model, a mathematical system which generates a sequence of “simulated” observations. Our aim is to obtain a “good” model — one that is in agreement with the data. We would like this agreement to be quantitative — not merely qualitative. The major subject of this thesis is the question of what good quantitative agreement means. Most approaches to this question could be described as “predictionist”. In the predictionist approach one builds models by attempting to answer the question, “given that the system is now here, where will it be next?” The quality of the model is judged by the degree to which the states of the model and the original system agree in the near future, conditioned on the present state of the model agreeing with that of the original system. Equivalently, the model is judged on its ability to make good short-term predictions on the original system. The main claim of this thesis is that prediction is often not the most appropriate criterion to apply when fitting models. We show, for example, that one can have models that, while able to make good predictions, have long term (or free-running) behaviour bearing little resemblance to that exhibited in the original time-series. We would hope to be able to use our models for a wide range of purposes other than just prediction — certainly we would like our models to exhibit good free-running behaviour. This thesis advocates a “behaviourist” approach, in which the criterion for a good model is that its long-term behaviour matches that exhibited by the data. We suggest that the behaviourist approach enjoys a certain robustness over the predictionist approaches. We show that good predictors can often be very poorly behaved, and suggest that well behaved models cannot perform too badly at the task of prediction. The thesis begins by comparing the predictionist and behaviourist approaches in the context of a number of simplified model-building problems. It then presents a simple theory for the understanding of the differences between the two approaches. Effective methods for the construction of well-behaved models are presented. Finally, these methods are applied to two real-world problems — modelling of the response of a voltage-clamped squid “giant” axon, and modelling of the “yearly sunspot number”.
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Katsiampa, Paraskevi. "Nonlinear exponential autoregressive time series models with conditional heteroskedastic errors with applications to economics and finance." Thesis, Loughborough University, 2015. https://dspace.lboro.ac.uk/2134/18432.
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The analysis of time series has long been the subject of interest in different fields. For decades time series were analysed with linear models, which have many advantages. Nevertheless, an issue which has been raised is whether there exist other models that can explain and forecast real data better than linear ones. In this thesis, new nonlinear time series models are suggested, which consist of a nonlinear conditional mean model, such as an ExpAR or an Extended ExpAR, and a nonlinear conditional variance model, such as an ARCH or a GARCH. Since new models are introduced, simulated series of the new models are presented, as it is important in order to see what characteristics real data which could be explained by them should have. In addition, the models are applied to various stationary and nonstationary economic and financial time series and are compared to the classic AR-ARCH and AR-GARCH models, in terms of fitting and forecasting. It is shown that, although it is difficult to beat the AR-ARCH and AR-GARCH models, the ExpAR and Extended ExpAR models and their special cases, combined with conditional heteroscedastic errors, can be useful tools in fitting, describing and forecasting nonlinear behaviour in financial and economic time series, and can provide some improvement in terms of both fitting and forecasting compared to the AR-ARCH and AR-GARCH models.
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Lundbergh, Stefan. "Modelling economic high-frequency time series." Doctoral thesis, Handelshögskolan i Stockholm, Ekonomisk Statistik (ES), 1999. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-637.
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Mohajer, Maryam. "Nonlinear time series analysis of electrical activity in a slice model of epilepsy." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ46205.pdf.
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VELLOSO, MARIA LUIZA FERNANDES. "TIME SERIES MODEL WITH NEURAL COEFFICIENTS FOR NONLINEAR PROCESSES IN MEAN AND VARIANCE." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1999. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8103@1.
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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOEsta tese apresenta uma nova classe de modelos não lineares inspirada no modelo ARN, apresentado por Mellem, 1997. Os modelos definidos nesta classe são aditivos com coeficientes variáveis modelados por redes neurais e, tanto a média quanto a variância condicionais, são modeladas explicitamente. Neste trabalho podem ser identificadas quatro partes principais: um estudo sobre os modelos mais comuns encontrados na literatura de séries temporais; um estudo sobre redes neurais, focalizando a rede backpropagation; a definição do modelo proposto e os métodos utilizados na estimação dos parâmetros e o estudo de casos. Modelos aditivos têm sido escolha preferencial na modelagem não linear: paramétrica ou não paramétrica, de média ou de variância condicional. Além disso, tanto a idéia de modelos de coeficientes variáveis quanto a de modelos híbridos. que reúnem paradigmas diferentes, não é novidade. Por esta razão, foi traçado um panorama dos modelos não lineares mais encontrados na literatura de séries temporais, focalizando-se naqueles que tinham relacionamento mais estreito com a classe de modelos proposta neste trabalho. No estudo sobre redes neurais, além da apresentação de seus conceitos básicos, analisou- se a rede backpropagation, ponto de partida para a modelagem dos coeficientes variáveis. Esta escolha deveu- se à constatação da predominância e constância no uso desta rede, ou de suas variantes, nos estudos e aplicações em séries temporais. Demonstrou-se que os modelos propostos são aproximadores universais e podem ser utilizados para modelar a variância condicional de uma série temporal. Foram desenvolvidos algoritmos, a partir dos métodos de mínimos quadrados e de máxima verossimilhança, para a estimação dos pesos, através da adaptação do algoritmo de backpropagation à esta nova classe de modelos. Embora tenham sido sugeridos outros algoritmos de otimização, este mostrou-se suficientemente apropriado para os casos testados neste trabalho. O estudo de casos foi dividido em duas partes: testes com séries sintéticas e testes com séries reais. Estas últimas, normalmente, utilizadas como benchmarking por analistas de séries temporais não lineares. Para auxiliar na identificação das variáveis do modelo, foram utilizadas regressões de lag não paramétricas. Os resultados obtidos foram comparados com outras modelagens e foram superiores ou, no mínimo, equivalentes. Além disso, é mostrado que o modelo híbrido proposto engloba vários destes outros modelos.
A class of nonlinear additive varyng coefficient models is introduced in this thesis, inspired by ARN model, presented by Mellem, 1997. the coefficients are explicitly modelled. This work is divided in four major parts: a study of most common models in the time series literature; a study of neural networks, focused in backpropagation network; the presentation of the proposed models and the methods used for parameter estimation: and the case studies. Additive models has been the preferencial choice in nonlinear modelling: idea of varyng coefficient and of hybrid models, aren`t news. Hence, the models in the time series literature were analysed, assentialy those closely related with the class of models proposed in this work. Sinse the predominance and constancy in the use of backpropagation network, or its variants, in time series studies and applications, was confirmed by this work, this network was analyzed with more details. This work demonstrated that the proposed models are universal aproximators and could model explicity conditional variance. Moreover, gradient calculus and algorithms for the weight estimation were developed based on the main estimation methods: least mean squares and maximum likelihood. Even though other gradient calculus and otimization algorithms have been sugested, this one was sufficiently adequate for the studied cases. The case studies were divided in two parts: tests with synthetic series and for the nonlinear time series analysts. The obtained results were compared with other models and were superior or, at least, equivalent. Also, these results confirmed that the proposed hybrid model encompass several of the others models
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Tonguc, Ozlem. "Wheat Price Dynamics In Turkey: A Nonlinear Analysis." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612357/index.pdf.
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Wheat is an extremely important agricultural commodity, due to its crucial role in everyday nutrition, food security, and in terms of incomes of a large body of farmers worldwide. This study examines the dynamics of wheat prices in Turkey in a framework that allows for regime switching. Due to their simplicity, threshold autoregressive (TAR) models are used to capture the effects of factors such as transaction costs and other institutional arrangements that generate discontinuous adjustment to equilibrium price level. The results are compared with standard linear model estimations. Results indicate that there is strong evidence for asymmetric adjustment of wheat prices in Turkey to the equilibrium price, hence models allowing for regime switching are more preferable over the linear ones. However, the diagnostics of the TAR model reveal that specification of a TAR model allowing for more than two regimes, or a smooth transition autoregressive (STAR) model that allows for smooth transition through a continuum of regimes might be more appropriate.
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Singleton, Michael David. "Nonlinear Hierarchical Models for Longitudinal Experimental Infection Studies." UKnowledge, 2015. http://uknowledge.uky.edu/epb_etds/7.
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Experimental infection (EI) studies, involving the intentional inoculation of animal or human subjects with an infectious agent under controlled conditions, have a long history in infectious disease research. Longitudinal infection response data often arise in EI studies designed to demonstrate vaccine efficacy, explore disease etiology, pathogenesis and transmission, or understand the host immune response to infection. Viral loads, antibody titers, symptom scores and body temperature are a few of the outcome variables commonly studied. Longitudinal EI data are inherently nonlinear, often with single-peaked response trajectories with a common pre- and post-infection baseline. Such data are frequently analyzed with statistical methods that are inefficient and arguably inappropriate, such as repeated measures analysis of variance (RM-ANOVA). Newer statistical approaches may offer substantial gains in accuracy and precision of parameter estimation and power. We propose an alternative approach to modeling single-peaked, longitudinal EI data that incorporates recent developments in nonlinear hierarchical models and Bayesian statistics. We begin by introducing a nonlinear mixed model (NLMM) for a symmetric infection response variable. We employ a standard NLMM assuming normally distributed errors and a Gaussian mean response function. The parameters of the model correspond directly to biologically meaningful properties of the infection response, including baseline, peak intensity, time to peak and spread. Through Monte Carlo simulation studies we demonstrate that the model outperforms RM-ANOVA on most measures of parameter estimation and power. Next we generalize the symmetric NLMM to allow modeling of variables with asymmetric time course. We implement the asymmetric model as a Bayesian nonlinear hierarchical model (NLHM) and discuss advantages of the Bayesian approach. Two illustrative applications are provided. Finally we consider modeling of viral load. For several reasons, a normal-errors model is not appropriate for viral load. We propose and illustrate a Bayesian NLHM with the individual responses at each time point modeled as a Poisson random variable with the means across time points related through a Tricube mean response function. We conclude with discussion of limitations and open questions, and a brief survey of broader applications of these models.
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Jahan, Nusrat. "Applying goodness-of-fit techniques in testing time series Gaussianity and linearity." Diss., Mississippi State : Mississippi State University, 2006. http://sun.library.msstate.edu/ETD-db/ETD-browse/browse.
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Dalderop, Jeroen Wilhelmus Paulus. "Essays on nonparametric estimation of asset pricing models." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/277966.
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This thesis studies the use of nonparametric econometric methods to reconcile the empirical behaviour of financial asset prices with theoretical valuation models. The confrontation of economic theory with asset price data requires various functional form assumptions about the preferences and beliefs of investors. Nonparametric methods provide a flexible class of models that can prevent misspecification of agents’ utility functions or the distribution of asset returns. Evidence for potential nonlinearity is seen in the presence of non-Gaussian distributions and excessive volatility of stock returns, or non-monotonic stochastic discount factors in option prices. More robust model specifications are therefore likely to contribute to risk management and return predictability, and lend credibility to economists’ assertions. Each of the chapters in this thesis relaxes certain functional form assumptions that seem most important for understanding certain asset price data. Chapter 1 focuses on the state-price density in option prices, which confounds the nonlinearity in both the preferences and the beliefs of investors. To understand both sources of nonlinearity in equity prices, Chapter 2 introduces a semiparametric generalization of the standard representative agent consumption-based asset pricing model. Chapter 3 returns to option prices to understand the relative importance of changes in the distribution of returns and in the shape of the pricing kernel. More specifically, Chapter 1 studies the use of noisy high-frequency data to estimate the time-varying state-price density implicit in European option prices. A dynamic kernel estimator of the conditional pricing function and its derivatives is proposed that can be used for model-free risk measurement. Infill asymptotic theory is derived that applies when the pricing function is either smoothly varying or driven by diffusive state variables. Trading times and moneyness levels are modelled by marked point processes to capture intraday trading patterns. A simulation study investigates the performance of the estimator using an iterated plug-in bandwidth in various scenarios. Empirical results using S&P 500 E-mini European option quotes finds significant time-variation at intraday frequencies. An application towards delta- and minimum variance-hedging further illustrates the use of the estimator. Chapter 2 proposes a semiparametric asset pricing model to measure how consumption and dividend policies depend on unobserved state variables, such as economic uncertainty and risk aversion. Under a flexible specification of the stochastic discount factor, the state variables are recovered from cross-sections of asset prices and volatility proxies, and the shape of the policy functions is identified from the pricing functions. The model leads to closed-form price-dividend ratios under polynomial approximations of the unknown functions and affine state variable dynamics. In the empirical application uncertainty and risk aversion are separately identified from size-sorted stock portfolios exploiting the heterogeneous impact of uncertainty on dividend policy across small and large firms. I find an asymmetric and convex response in consumption (-) and dividend growth (+) towards uncertainty shocks, which together with moderate uncertainty aversion, can generate large leverage effects and divergence between macroeconomic and stock market volatility. Chapter 3 studies the nonparametric identification and estimation of projected pricing kernels implicit in the pricing of options, the underlying asset, and a riskfree bond. The sieve minimum-distance estimator based on conditional moment restrictions avoids the need to compute ratios of estimated risk-neutral and physical densities, and leads to stable estimates even in regions with low probability mass. The conditional empirical likelihood (CEL) variant of the estimator is used to extract implied densities that satisfy the pricing restrictions while incorporating the forwardlooking information from option prices. Moreover, I introduce density combinations in the CEL framework to measure the relative importance of changes in the physical return distribution and in the pricing kernel. The nonlinear dynamic pricing kernels can be used to understand return predictability, and provide model-free quantities that can be compared against those implied by structural asset pricing models.
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Lopez, Daneri Martin Eduardo. "Essays on income taxation and idiosyncratic risk." Diss., University of Iowa, 2012. https://ir.uiowa.edu/etd/3342.
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I study the role of heterogeneity and idiosyncratic risk in Macroeconomics, and their implications on problems of income taxation. In the first chapter, I study the effects of redistributive taxation in an incomplete market economy with heterogeneous agents and idiosyncratic risk. I focus on the role of distortions in labor supply decisions and the interplay of heterogeneity and uninsurable idiosyncratic shocks, conducting the first general equilibrium analysis of a Negative Income Tax (NIT). I show that a NIT is a serious candidate to replace the current income tax in the United States. I find that the optimal NIT has a marginal tax rate of 28% and a transfer of 10% of per capita GDP, roughly $4600.
The welfare gains of replacing the current US income tax with a NIT are equivalent to a 6.3% increase in annual consumption in every state of the world. Low-ability agents, in the bottom quintile of the productivity distribution, benefit the most, while high-ability agents are worse off. A consequence of the reform is that the composition of the labor force changes, with high-productivity agents working more, in relative terms, than low-productivity agents. Finally, I find that the riskier the economy, the higher the welfare gains of the NIT as a provider of public insurance.
In the second chapter, I study labor income dynamics over the life cycle and introduce a novel methodology that can detect the presence of patterns in the idiosyncratic earnings shocks and recognize economic forces in action. Using a sample from the Panel Study of Income Dynamics (PSID), I estimate a Bayesian Logistic Smoothed Transition Autoregressive model of order 1 (LSTAR(1)) with a rich level of heterogeneity in the innovations. I find that there is a life-cycle pattern in the earning shocks: before the age 29, young workers experience shocks with higher variance and a positive probability of lower persistence than older workers. A comparison with conventional models shows that an incorrect model specification introduces bias in the estimates. The proposed model can be easily approximated with a discrete Markov process. This means that this model can be used by macroeconomists to calibrate income processes.
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Kurskoy, Yu S., O. S. Hnatenko, Yu P. Machekhin, and M. V. Neofitnyy. "Topological Model of Laser Emission Parameters Research." Thesis, CAOL, 2019. http://openarchive.nure.ua/handle/document/15100.
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The research paper presents a model for studying both the parameters and dynamics of laser light as a nonlinear dynamic system. The model provides for the measurement of the values of physical quantities by non-linear metrology methods and the analysis of the research findings with topological tools. The model is based on the assumption of interval values of the measured values and the possibility of changing the stationary dynamics into the random one. The model contains an experiment scheme and a procedure for evaluating measurement results. The peculiarity of the model lies in its systemic approach and suitability for measuring and researching stationary and chaotic modes. The model provides for the measurement of the emission parameter values intervals in various modes, of their stability values and time series prediction. Classification of the system dynamics is performed using the fractal dimension. The model can be used both to ensure the stability of the laser light parameters, and to obtain and control random emission.
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Khan, Shiraj. "Nonlinear dependence and extremes in hydrology and climate." [Tampa, Fla.] : University of South Florida, 2007. http://purl.fcla.edu/usf/dc/et/SFE0002142.
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Rech, Gianluigi. "Modelling and forecasting economic time series with single hidden-layer feedforward autoregressive artificial neural networks." Doctoral thesis, Handelshögskolan i Stockholm, Ekonomisk Statistik (ES), 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-591.
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This dissertation consists of 3 essays In the first essay, A Simple Variable Selection Technique for Nonlinear Models, written in cooperation with Timo Teräsvirta and Rolf Tschernig, I propose a variable selection method based on a polynomial expansion of the unknown regression function and an appropriate model selection criterion. The hypothesis of linearity is tested by a Lagrange multiplier test based on this polynomial expansion. If rejected, a kth order general polynomial is used as a base for estimating all submodels by ordinary least squares. The combination of regressors leading to the lowest value of the model selection criterion is selected. The second essay, Modelling and Forecasting Economic Time Series with Single Hidden-layer Feedforward Autoregressive Artificial Neural Networks, proposes an unified framework for artificial neural network modelling. Linearity is tested and the selection of regressors performed by the methodology developed in essay I. The number of hidden units is detected by a procedure based on a sequence of Lagrange multiplier (LM) tests. Serial correlation of errors and parameter constancy are checked by LM tests as well. A Monte-Carlo study, the two classical series of the lynx and the sunspots, and an application on the monthly S&P 500 index return series are used to demonstrate the performance of the overall procedure. In the third essay, Forecasting with Artificial Neural Network Models (in cooperation with Marcelo Medeiros), the methodology developed in essay II, the most popular methods for artificial neural network estimation, and the linear autoregressive model are compared by forecasting performance on 30 time series from different subject areas. Early stopping, pruning, information criterion pruning, cross-validation pruning, weight decay, and Bayesian regularization are considered. The findings are that 1) the linear models very often outperform the neural network ones and 2) the modelling approach to neural networks developed in this thesis stands up well with in comparison when compared to the other neural network modelling methods considered here.Diss. Stockholm : Handelshögskolan, 2002. Spikblad saknas
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Henter, Gustav Eje. "Probabilistic Sequence Models with Speech and Language Applications." Doctoral thesis, KTH, Kommunikationsteori, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-134693.
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Series data, sequences of measured values, are ubiquitous. Whenever observations are made along a path in space or time, a data sequence results. To comprehend nature and shape it to our will, or to make informed decisions based on what we know, we need methods to make sense of such data. Of particular interest are probabilistic descriptions, which enable us to represent uncertainty and random variation inherent to the world around us. This thesis presents and expands upon some tools for creating probabilistic models of sequences, with an eye towards applications involving speech and language. Modelling speech and language is not only of use for creating listening, reading, talking, and writing machines---for instance allowing human-friendly interfaces to future computational intelligences and smart devices of today---but probabilistic models may also ultimately tell us something about ourselves and the world we occupy. The central theme of the thesis is the creation of new or improved models more appropriate for our intended applications, by weakening limiting and questionable assumptions made by standard modelling techniques. One contribution of this thesis examines causal-state splitting reconstruction (CSSR), an algorithm for learning discrete-valued sequence models whose states are minimal sufficient statistics for prediction. Unlike many traditional techniques, CSSR does not require the number of process states to be specified a priori, but builds a pattern vocabulary from data alone, making it applicable for language acquisition and the identification of stochastic grammars. A paper in the thesis shows that CSSR handles noise and errors expected in natural data poorly, but that the learner can be extended in a simple manner to yield more robust and stable results also in the presence of corruptions. Even when the complexities of language are put aside, challenges remain. The seemingly simple task of accurately describing human speech signals, so that natural synthetic speech can be generated, has proved difficult, as humans are highly attuned to what speech should sound like. Two papers in the thesis therefore study nonparametric techniques suitable for improved acoustic modelling of speech for synthesis applications. Each of the two papers targets a known-incorrect assumption of established methods, based on the hypothesis that nonparametric techniques can better represent and recreate essential characteristics of natural speech. In the first paper of the pair, Gaussian process dynamical models (GPDMs), nonlinear, continuous state-space dynamical models based on Gaussian processes, are shown to better replicate voiced speech, without traditional dynamical features or assumptions that cepstral parameters follow linear autoregressive processes. Additional dimensions of the state-space are able to represent other salient signal aspects such as prosodic variation. The second paper, meanwhile, introduces KDE-HMMs, asymptotically-consistent Markov models for continuous-valued data based on kernel density estimation, that additionally have been extended with a fixed-cardinality discrete hidden state. This construction is shown to provide improved probabilistic descriptions of nonlinear time series, compared to reference models from different paradigms. The hidden state can be used to control process output, making KDE-HMMs compelling as a probabilistic alternative to hybrid speech-synthesis approaches. A final paper of the thesis discusses how models can be improved even when one is restricted to a fundamentally imperfect model class. Minimum entropy rate simplification (MERS), an information-theoretic scheme for postprocessing models for generative applications involving both speech and text, is introduced. MERS reduces the entropy rate of a model while remaining as close as possible to the starting model. This is shown to produce simplified models that concentrate on the most common and characteristic behaviours, and provides a continuum of simplifications between the original model and zero-entropy, completely predictable output. As the tails of fitted distributions may be inflated by noise or empirical variability that a model has failed to capture, MERS's ability to concentrate on high-probability output is also demonstrated to be useful for denoising models trained on disturbed data.QC 20131128
ACORNS: Acquisition of Communication and Recognition Skills
LISTA – The Listening Talker
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Smejkalová, Veronika. "Aproximace prostorově distribuovaných hierarchicky strukturovaných dat." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2018. http://www.nusl.cz/ntk/nusl-392841.
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The forecast of the waste production is an important information for planning in waste management. The historical data often consists of short time series, therefore traditional prognostic approaches fail. The mathematical model for forecasting of future waste production based on spatially distributed data with hierarchically structure is suggested in this thesis. The approach is based on principles of regression analysis with final balance to ensure the compliance of aggregated data values. The selection of the regression function is a part of mathematical model for high-quality description of data trend. In addition, outlier values are cleared, which occur abundantly in the database. The emphasis is on decomposition of extensive model into subtasks, which lead to a simpler implementation. The output of this thesis is tool tested within case study on municipal waste production data in the Czech Republic.
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Alegria, Elvis Omar Jara 1986. "Estimação On-Line de parâmetros dependentes do estado (State Dependent Parameter - SDP) em modelos de regressão não lineares." [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/258834.
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Orientador: Celso Pascoli BotturaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
Made available in DSpace on 2018-08-27T02:15:32Z (GMT). No. of bitstreams: 1 Alegria_ElvisOmarJara_M.pdf: 5581682 bytes, checksum: cd5b08b04c7ba4bcd505ab00e5335ffc (MD5) Previous issue date: 2015
Resumo: Este trabalho é sobre a identificação recursiva em tempo real das dependências parâmetro-estado em modelos de regressão de series temporais estocásticas. O descobrimento dessas dependências é útil para obter uma nova, e mais acurada, estrutura do modelo. Os métodos recursivos convencionais de estimação de parâmetros variantes no tempo, não conseguem bons resultados quando os modelos apresentam parâmetros dependentes do estado (SDP) pois eles tem comportamento altamente não linear e inclusive caótico. Nossa proposta está baseada no estudo de Peter Young para SDPs no caso Off-Line. É discutido o método que ele propõe para reduzir a entropia das séries nos modelos com SDP e para isto se apresenta umas transformações dos dados. São propostas mudanças no seu algoritmo Off-Line que o fazem mais rápido, eficiente e manejável para a implementação do modo On-Line. Finalmente, três exemplos numéricos são mostrados para validar as nossas propostas e a sua aplicação na área de detecção de falhas paramétricas. Todas as funções foram implementadas no MATLAB e conformam um toolbox para identificação de SDP em modelos de regressão
Abstract: This work is about the identification of the dependency among parameters and states in regression models of stochastic time series. The discovery of that dependency can be useful to obtain a more accurate model structure. Conventional recursive algorithms for estimation of Time Variable Parameters do not provide good results in models with state-dependent parameters (SDP) because these may have highly non-linear and even chaotic behavior. This work is based on Peter Young's studies about Off-Line SDP. Young's methods to data entropy reduction are discussed and some data transformations are proposed for this. Later, are proposed some changes on the Off-Line algorithm in order to improve its velocity, accuracy, and tractability to generate the On-Line version. Finally, three numeric examples to validate our proposal are shown. All the functions were implemented in MATLAB and conform a Toolbox to the SDP identification in regression models
Mestrado
Automação
Mestre em Engenharia Elétrica
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Lima, Maria Mabel de Barros. "Modelação do interesse de vídeos de música medido pelo número de procuras na internet via Google Trends." Master's thesis, Instituto Superior de Economia e Gestão, 2014. http://hdl.handle.net/10400.5/7649.
Full textAbstract:
Mestrado em Econometria Aplicada e PrevisãoO mercado da música mundial continua a se expandir em novos mercados e criar novos negócios, atraindo cada vez mais usuários para os serviços de música sob o formato digital. A receita gerada pela indústria da música digital apresentou um crescimento de 4,3% de 2012 para 2013 (de US$ 5.6 bi para US$ 5.9 bi), já representa 39% da receita total gerada no mercado mundial. Para uma melhor compreensão da natureza do ciclo de vida do formato digital da música emergente, buscou-se estudar os vídeos de música da internet, dado a sua importância na indústria da música, por ser um dispositivo de marketing destinado, principalmente, a promover as vendas de gravações de música, por ser um importante contributo para a comercialização da música popular e dado a ausência de literatura de caráter qualitativo e quantitativo subjacente. Esta dissertação pretende propor um modelo capaz de descrever a dinâmica dos vídeos de música ao longo do tempo, ou seja, de como se dá o interesse coletivo por um determinado vídeo de música. A base empírica deste estudo consiste em séries temporais de vídeos de música (dados semanais) relacionando frequências de busca disponíveis a partir do Google Trends. Empiricamente avaliou-se o desempenho do modelo proposto, usando métodos de estimação não lineares de séries temporais. Os resultados obtidos permitem distinguir os vídeos de música de internet de curta duração de outros mais duradores.
The global music business continues to expand into new markets and create new business, attracting more and more users to digital format music services. The revenues generated by the digital music industry grew by 4.3% from 2012 to 2013 (US$ 5.6 billion to US$ 5.9 billion), already represents 39% of total revenues generated by the global music market. Internet music videos are a pervasive phenomenon on the Web, they typically consist in a short film made to advertise a popular song that spread through network. In order to contribute to a better understanding of the nature of the life cycle of internet music videos, given its importance in the music industry and in particular plausible models that would explain their temporal dynamics have not previously been reported. Our aim in this paper is thus to develop meaningful and interpretable model that describes the dynamics of music videos over time, i.e., how collective attention to internet music videos evolves over time, and how relate with their life cycle. The empirical basis of our study consists of time series of music videos relating frequencies available search from Google Trends. We conduct an empirical illustration to assess the performance of our model using nonlinear time series models. The results of the empirical illustration indicate to distinguish short and "long" life cycle's internet music videos.