Non Parametric Estimation Of Forecast Distributions In Non Linear Non Gaussian State Space Models

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Non-parametric Estimation of Forecast Distributions in Non-linear, Non-gaussian State Space Models

Author : Jason Wei Jian Ng
Publisher :
ISBN 13 :
Total Pages : 376 pages
Book Rating : 4.:/5 (11 download)

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Book Synopsis Non-parametric Estimation of Forecast Distributions in Non-linear, Non-gaussian State Space Models by : Jason Wei Jian Ng

Download or read book Non-parametric Estimation of Forecast Distributions in Non-linear, Non-gaussian State Space Models written by Jason Wei Jian Ng and published by . This book was released on 2012 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Non-Gaussian time series variables are prevalent in the economic and finance spheres, with state space models often employed to analyze such variables and, ultimately, to produce forecasts. A review of the relevant literature reveals that existing methods are characterized by a reliance on (potentially incorrect) parametric assumptions and are often computationally expensive. The primary aim of this thesis is to develop a non-parametric approach to forecasting - within the state space framework - with computational ease an important focus. With a view to capturing all relevant information about the likely future values of the variable of interest, the approach is used to produce non-parametric estimates of the full forecast distribution over any time horizon. Simulation experiments are used to document the accuracy of the non-parametric method relative to both correctly and incorrectly specified parametric alternatives, in a variety of relevant settings. Applying a range of methods for evaluating and comparing distributional forecasts, the non-parametric method is shown to perform significantly better, overall, than misspecified parametric alternatives while remaining competitive with correctly specified parametric estimators. Focus is then given to the development of a new non-Gaussian state space model for observed realized volatility from which estimates of forecast distributions of future volatility are produced using the non-parametric method. In an empirical illustration, the non-parametric method is used to produce sequential estimates of the out-of-sample one-step-ahead forecast distribution of realized volatility on the S&P500 index during the recent financial crisis. A resampling technique for measuring sampling variation in an estimated forecast distribution is also demonstrated. The proposed filtering algorithm is further extended to cater, in particular, for multi-step-ahead forecasting and multivariate systems. A simulation-based version of the algorithm is also illustrated, with the algorithm in this form seen to be a computationally efficient alternative to existing particle filtering algorithms.

Time Series Analysis by State Space Methods

Author : James Durbin
Publisher : Oxford University Press
ISBN 13 : 9780198523543
Total Pages : 280 pages
Book Rating : 4.5/5 (235 download)

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Book Synopsis Time Series Analysis by State Space Methods by : James Durbin

Download or read book Time Series Analysis by State Space Methods written by James Durbin and published by Oxford University Press. This book was released on 2001-06-21 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: State space time series analysis emerged in the 1960s in engineering, but its applications have spread to other fields. Durbin (statistics, London School of Economics and Political Science) and Koopman (econometrics, Free U., Amsterdam) extol the virtues of such models over the main analytical system currently used for time series data, Box-Jenkins' ARIMA. What distinguishes state space time models is that they separately model components such as trend, seasonal, regression elements and disturbance terms. Part I focuses on traditional and new techniques based on the linear Gaussian model. Part II presents new material extending the state space model to non-Gaussian observations. c. Book News Inc.

Parameter Estimation for Nonlinear State Space Models

Author : Jessica Wong
Publisher :
ISBN 13 :
Total Pages : 182 pages
Book Rating : 4.:/5 (864 download)

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Book Synopsis Parameter Estimation for Nonlinear State Space Models by : Jessica Wong

Download or read book Parameter Estimation for Nonlinear State Space Models written by Jessica Wong and published by . This book was released on 2012 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: ABSTRACT: This thesis explores the methodology of state, and in particular, parameter estimation for time series datasets. Various approaches are investigated that are suitable for nonlinear models and non-Gaussian observations using state space models. The methodologies are applied to a dataset consisting of the historical lynx and hare populations, typically modeled by the Lotka- Volterra equations. With this model and the observed dataset, particle filtering and parameter estimation methods are implemented as a way to better predict the state of the system. Methods for parameter estimation considered include: maximum likelihood estimation, state augmented particle filtering, multiple iterative filtering and particle Markov chain Monte Carlo (PMCMC) methods. The specific advantages and disadvantages for each technique are discussed. However, in most cases, PMCMC is the preferred parameter estimation solution. It has the advantage over other approaches in that it can well approximate any posterior distribution from which inference can be made.

Bayesian Inference of State Space Models

Author : Kostas Triantafyllopoulos
Publisher : Springer Nature
ISBN 13 : 303076124X
Total Pages : 503 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Bayesian Inference of State Space Models by : Kostas Triantafyllopoulos

Download or read book Bayesian Inference of State Space Models written by Kostas Triantafyllopoulos and published by Springer Nature. This book was released on 2021-11-12 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Inference of State Space Models: Kalman Filtering and Beyond offers a comprehensive introduction to Bayesian estimation and forecasting for state space models. The celebrated Kalman filter, with its numerous extensions, takes centre stage in the book. Univariate and multivariate models, linear Gaussian, non-linear and non-Gaussian models are discussed with applications to signal processing, environmetrics, economics and systems engineering. Over the past years there has been a growing literature on Bayesian inference of state space models, focusing on multivariate models as well as on non-linear and non-Gaussian models. The availability of time series data in many fields of science and industry on the one hand, and the development of low-cost computational capabilities on the other, have resulted in a wealth of statistical methods aimed at parameter estimation and forecasting. This book brings together many of these methods, presenting an accessible and comprehensive introduction to state space models. A number of data sets from different disciplines are used to illustrate the methods and show how they are applied in practice. The R package BTSA, created for the book, includes many of the algorithms and examples presented. The book is essentially self-contained and includes a chapter summarising the prerequisites in undergraduate linear algebra, probability and statistics. An up-to-date and complete account of state space methods, illustrated by real-life data sets and R code, this textbook will appeal to a wide range of students and scientists, notably in the disciplines of statistics, systems engineering, signal processing, data science, finance and econometrics. With numerous exercises in each chapter, and prerequisite knowledge conveniently recalled, it is suitable for upper undergraduate and graduate courses.

Time Series Analysis by State Space Methods

Author : James Durbin
Publisher : OUP Oxford
ISBN 13 : 0191627194
Total Pages : 369 pages
Book Rating : 4.1/5 (916 download)

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Book Synopsis Time Series Analysis by State Space Methods by : James Durbin

Download or read book Time Series Analysis by State Space Methods written by James Durbin and published by OUP Oxford. This book was released on 2012-05-03 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new edition updates Durbin & Koopman's important text on the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbance terms, each of which is modelled separately. The techniques that emerge from this approach are very flexible and are capable of handling a much wider range of problems than the main analytical system currently in use for time series analysis, the Box-Jenkins ARIMA system. Additions to this second edition include the filtering of nonlinear and non-Gaussian series. Part I of the book obtains the mean and variance of the state, of a variable intended to measure the effect of an interaction and of regression coefficients, in terms of the observations. Part II extends the treatment to nonlinear and non-normal models. For these, analytical solutions are not available so methods are based on simulation.

State Space and Unobserved Component Models

Author : James Durbin
Publisher : Cambridge University Press
ISBN 13 : 9780521835954
Total Pages : 398 pages
Book Rating : 4.8/5 (359 download)

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Book Synopsis State Space and Unobserved Component Models by : James Durbin

Download or read book State Space and Unobserved Component Models written by James Durbin and published by Cambridge University Press. This book was released on 2004-06-10 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive overview of developments in the theory and application of state space modeling, first published in 2004.

Statistics And Finance: An Interface - Proceedings Of The Hong Kong International Workshop On Statistics In Finance

Author : Wai-sum Chan
Publisher : World Scientific
ISBN 13 : 1783261668
Total Pages : 396 pages
Book Rating : 4.7/5 (832 download)

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Book Synopsis Statistics And Finance: An Interface - Proceedings Of The Hong Kong International Workshop On Statistics In Finance by : Wai-sum Chan

Download or read book Statistics And Finance: An Interface - Proceedings Of The Hong Kong International Workshop On Statistics In Finance written by Wai-sum Chan and published by World Scientific. This book was released on 2000-04-28 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contents:Heavy-Tailed and Nonlinear Continuous-Time ARMA Models for Financial Time Series (P J Brockwell)Nonlinear State Space Model Approach to Financial Time Series with Time-Varying Variance (G Kitagawa & S Sato)Nonparametric Estimation and Bootstrap for Financial Time Series (J-P Kreiβ)A Note on Kernel Estimation in Integrated Time Series (Y-C Xia et al.)Stylized Facts on the Temporal and Distributional Properties of Absolute Returns: An Update (C W J Granger et al.)Volatility Computed by Time Series Operators at High Frequency (U A Müller)Missing Values in ARFIMA Models (W Palma)Second Order Tail Effects (C G de Vries)Bayesian Estimation of Stochastic Volatility Model via Scale Mixtures Distributions (S T B Choy & C M Chan)On a Smooth Transition Double Threshold Model (Y N Lee & W K Li)Interval Prediction of Financial Time Series (B Cheng & H Tong)A Decision Theoretic Approach to Forecast Evaluation (C W J Granger & M H Pesaran)Portfolio Management and Market Risk Quantification Using Neural Networks (J Franke)Detecting Structural Changes Using Genetic Programming with an Application to the Greater-China Stock Markets (X B Zhang et al.)and other papers Readership: Researchers in finance, time series analysis, economics and actuarial science, as well as investment bankers, stock market analysts and risk managers. Keywords:Proceedings;Workshop;Statistics;Finance;Hongkong (China)

Time Series Analysis and Its Applications

Author : Robert H. Shumway
Publisher :
ISBN 13 : 9781475732627
Total Pages : 568 pages
Book Rating : 4.7/5 (326 download)

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Book Synopsis Time Series Analysis and Its Applications by : Robert H. Shumway

Download or read book Time Series Analysis and Its Applications written by Robert H. Shumway and published by . This book was released on 2014-01-15 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Engineering Applications of Neural Networks

Author : Lazaros Iliadis
Publisher : Springer Nature
ISBN 13 : 3031342046
Total Pages : 636 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis Engineering Applications of Neural Networks by : Lazaros Iliadis

Download or read book Engineering Applications of Neural Networks written by Lazaros Iliadis and published by Springer Nature. This book was released on 2023-06-06 with total page 636 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 24th International Conference on Engineering Applications of Neural Networks, EANN 2023, held in León, Spain, in June 2023. The 41 revised full papers and 8 revised short papers presented were carefully reviewed and selected from 125 submissions. The papers are organized in topical sections on artificial intelligence - computational methods - ethology; classification - filtering - genetic algorithms; complex dynamic networks' optimization/ graph neural networks; convolutional neural networks/spiking neural networks; deep learning modeling; deep/machine learning in engineering; LEARNING (reinforcemet - federated - adversarial - transfer); natural language - recommendation systems.

Deterministic Sampling for Nonlinear Dynamic State Estimation

Author : Gilitschenski, Igor
Publisher : KIT Scientific Publishing
ISBN 13 : 3731504731
Total Pages : 198 pages
Book Rating : 4.7/5 (315 download)

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Book Synopsis Deterministic Sampling for Nonlinear Dynamic State Estimation by : Gilitschenski, Igor

Download or read book Deterministic Sampling for Nonlinear Dynamic State Estimation written by Gilitschenski, Igor and published by KIT Scientific Publishing. This book was released on 2016-04-19 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of this work is improving existing and suggesting novel filtering algorithms for nonlinear dynamic state estimation. Nonlinearity is considered in two ways: First, propagation is improved by proposing novel methods for approximating continuous probability distributions by discrete distributions defined on the same continuous domain. Second, nonlinear underlying domains are considered by proposing novel filters that inherently take the underlying geometry of these domains into account.

Non gaussian state space models for count data: the durbin and koopman methodology

Author :
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (181 download)

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Book Synopsis Non gaussian state space models for count data: the durbin and koopman methodology by :

Download or read book Non gaussian state space models for count data: the durbin and koopman methodology written by and published by . This book was released on 1902 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: O objetivo desta tese é o de apresentar e investigar a metodologia de Durbin e Koopman (DK) usada para estimar o espaço de estado de modelos de séries temporais não-Gaussianos, dentro do contexto de modelos estruturais. A abordagem de DK está baseada na avaliação da verossimilhança usando uma eficiente simulação de Monte Carlo, por meio de amostragem por importância e técnicas de redução de variância, tais como variáveis antitéticas e variáveis de controle. Ela também integra conhecidas técnicas existentes no caso Gaussiano tais como o Filtro de Kalman Siavizado e o algoritmo de simulação suavizada. Uma vez que os hiperparâmetros do modelo são estimados, o estado, que contém as componentes do modelo, é estimado pela avaliação da moda a posteriori. Propomos então aproximações para avaliar a média e a variância da distribuição preditiva. São consideradas aplicações usando o modelo de Poisson.

Dynamic Linear Models with R

Author : Giovanni Petris
Publisher : Springer Science & Business Media
ISBN 13 : 0387772383
Total Pages : 258 pages
Book Rating : 4.3/5 (877 download)

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Book Synopsis Dynamic Linear Models with R by : Giovanni Petris

Download or read book Dynamic Linear Models with R written by Giovanni Petris and published by Springer Science & Business Media. This book was released on 2009-06-12 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms. The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online. No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.

Introduction to Time Series and Forecasting

Author : Peter J. Brockwell
Publisher : Springer Science & Business Media
ISBN 13 : 1475725264
Total Pages : 429 pages
Book Rating : 4.4/5 (757 download)

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Book Synopsis Introduction to Time Series and Forecasting by : Peter J. Brockwell

Download or read book Introduction to Time Series and Forecasting written by Peter J. Brockwell and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: Some of the key mathematical results are stated without proof in order to make the underlying theory acccessible to a wider audience. The book assumes a knowledge only of basic calculus, matrix algebra, and elementary statistics. The emphasis is on methods and the analysis of data sets. The logic and tools of model-building for stationary and non-stationary time series are developed in detail and numerous exercises, many of which make use of the included computer package, provide the reader with ample opportunity to develop skills in this area. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Additional topics include harmonic regression, the Burg and Hannan-Rissanen algorithms, unit roots, regression with ARMA errors, structural models, the EM algorithm, generalized state-space models with applications to time series of count data, exponential smoothing, the Holt-Winters and ARAR forecasting algorithms, transfer function models and intervention analysis. Brief introducitons are also given to cointegration and to non-linear, continuous-time and long-memory models. The time series package included in the back of the book is a slightly modified version of the package ITSM, published separately as ITSM for Windows, by Springer-Verlag, 1994. It does not handle such large data sets as ITSM for Windows, but like the latter, runs on IBM-PC compatible computers under either DOS or Windows (version 3.1 or later). The programs are all menu-driven so that the reader can immediately apply the techniques in the book to time series data, with a minimal investment of time in the computational and algorithmic aspects of the analysis.

The Oxford Handbook of Economic Forecasting

Author : Michael P. Clements
Publisher : OUP USA
ISBN 13 : 0195398645
Total Pages : 732 pages
Book Rating : 4.1/5 (953 download)

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Book Synopsis The Oxford Handbook of Economic Forecasting by : Michael P. Clements

Download or read book The Oxford Handbook of Economic Forecasting written by Michael P. Clements and published by OUP USA. This book was released on 2011-07-08 with total page 732 pages. Available in PDF, EPUB and Kindle. Book excerpt: Greater data availability has been coupled with developments in statistical theory and economic theory to allow more elaborate and complicated models to be entertained. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models.

A Companion to Economic Forecasting

Author : Michael P. Clements
Publisher : John Wiley & Sons
ISBN 13 : 140517191X
Total Pages : 616 pages
Book Rating : 4.4/5 (51 download)

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Book Synopsis A Companion to Economic Forecasting by : Michael P. Clements

Download or read book A Companion to Economic Forecasting written by Michael P. Clements and published by John Wiley & Sons. This book was released on 2008-04-15 with total page 616 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Companion to Economic Forecasting provides an accessible and comprehensive account of recent developments in economic forecasting. Each of the chapters has been specially written by an expert in the field, bringing together in a single volume a range of contrasting approaches and views. Uniquely surveying forecasting in a single volume, the Companion provides a comprehensive account of the leading approaches and modeling strategies that are routinely employed.

Data Assimilation

Author : Geir Evensen
Publisher : Springer Science & Business Media
ISBN 13 : 3540383018
Total Pages : 285 pages
Book Rating : 4.5/5 (43 download)

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Book Synopsis Data Assimilation by : Geir Evensen

Download or read book Data Assimilation written by Geir Evensen and published by Springer Science & Business Media. This book was released on 2006-12-22 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews popular data-assimilation methods, such as weak and strong constraint variational methods, ensemble filters and smoothers. The author shows how different methods can be derived from a common theoretical basis, as well as how they differ or are related to each other, and which properties characterize them, using several examples. Readers will appreciate the included introductory material and detailed derivations in the text, and a supplemental web site.

Nonlinear Data Assimilation

Author : Peter Jan Van Leeuwen
Publisher : Springer
ISBN 13 : 3319183478
Total Pages : 130 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Nonlinear Data Assimilation by : Peter Jan Van Leeuwen

Download or read book Nonlinear Data Assimilation written by Peter Jan Van Leeuwen and published by Springer. This book was released on 2015-07-22 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains two review articles on nonlinear data assimilation that deal with closely related topics but were written and can be read independently. Both contributions focus on so-called particle filters. The first contribution by Jan van Leeuwen focuses on the potential of proposal densities. It discusses the issues with present-day particle filters and explorers new ideas for proposal densities to solve them, converging to particle filters that work well in systems of any dimension, closing the contribution with a high-dimensional example. The second contribution by Cheng and Reich discusses a unified framework for ensemble-transform particle filters. This allows one to bridge successful ensemble Kalman filters with fully nonlinear particle filters, and allows a proper introduction of localization in particle filters, which has been lacking up to now.