Parameter Estimation for All-pass Time Series Models PDF Download

Author: Margaret Elizabeth Andrews
Publisher:
ISBN:
Category : Time-series analysis
Languages : en
Pages : 278

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Author:
Publisher:
ISBN: 9789036107174
Category :
Languages : en
Pages : 0

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This thesis concerns parameter estimation for observation-driven time-series models. In particular, the focus is on deriving asymptotic properties of (quasi) maximum likelihood estimators for the parameters of (quasi) score-driven models. Before moving to this novel research, Chapter 2 offers an accessible introduction to score-driven models, with a focus on time-varying conditional location and scale models. Then, Chapter 3 establishes conditions for consistency and asymptotic normality of the maximum likelihood estimator for a general class of stationary score-driven models, which is the first main contribution of the thesis. The asymptotic results are global and are also derived under potential misspecification. Importantly, the conditions are formulated in terms of the basic building blocks of score-driven models, which allows anyone to apply them to their own score-driven models of choice. The other main contribution is the proposal of two novel unit-root non-stationary (quasi) score-driven location models and the derivation of the asymptotic properties of the proposed estimators of these models in Chapters 4 and 5. Thus far, no rigorous asymptotic theory was available for non-stationary score-driven models of this type. In particular, Chapter 4 concerns a univariate score-driven location model, with a unit root location process, and with innovations from a mixture of normals distribution. This distribution offers considerable flexibility, and has not been considered for score-driven models before. We establish consistency and asymptotic normality of the maximum likelihood estimator, and examine the model's filtering ability in a Monte Carlo simulation study and an application to electricity spot prices. Chapter 5 considers a multivariate model where the observations are driven by a common univariate quasi score-driven location process with unit root dynamics. We propose a two-step estimation procedure, where the loading coefficients are estimated in the first step and the remaining parameters are estimated in the second step through quasi maximum likelihood estimation. We establish consistency of this two-step estimator and use a Monte Carlo simulation study to investigate its small-sample properties. To illustrate the model's use in practice, we consider an empirical application to diesel prices in different markets.

Author: Mohd Salihin Bin Ngadiman
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Author: James Vere Beck
Publisher: James Beck
ISBN: 9780471061182
Category : Mathematics
Languages : en
Pages : 540

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Introduction to and survey of parameter estimation; Probability; Introduction to statistics; Parameter estimation methods; Introduction to linear estimation; Matrix analysis for linear parameter estimation; Minimization of sum of squares functions for models nonlinear in parameters; Design of optimal experiments.

Author: Tina Qin
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Author: S. B. Ngadiman
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Category :
Languages : en
Pages :

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Author: Jitkomut Songsiri
Publisher:
ISBN:
Category :
Languages : en
Pages : 224

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Author: James Durbin
Publisher: Oxford University Press
ISBN: 9780198523543
Category : Business & Economics
Languages : en
Pages : 280

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Book Description
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.

Author: Julia Brodsky
Publisher:
ISBN: 9780591599701
Category :
Languages : en
Pages : 120

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Author: William W. S. Wei
Publisher: Addison-Wesley Longman
ISBN:
Category : Mathematics
Languages : en
Pages : 648

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Book Description
With its broad coverage of methodology, this comprehensive book is a useful learning and reference tool for those in applied sciences where analysis and research of time series is useful. Its plentiful examples show the operational details and purpose of a variety of univariate and multivariate time series methods. Numerous figures, tables and real-life time series data sets illustrate the models and methods useful for analyzing, modeling, and forecasting data collected sequentially in time. The text also offers a balanced treatment between theory and applications. Overview. Fundamental Concepts. Stationary Time Series Models. Nonstationary Time Series Models. Forecasting. Model Identification. Parameter Estimation, Diagnostic Checking, and Model Selection. Seasonal Time Series Models. Testing for a Unit Root. Intervention Analysis and Outlier Detection. Fourier Analysis. Spectral Theory of Stationary Processes. Estimation of the Spectrum. Transfer Function Models. Time Series Regression and GARCH Models. Vector Time Series Models. More on Vector Time Series. State Space Models and the Kalman Filter. Long Memory and Nonlinear Processes. Aggregation and Systematic Sampling in Time Series. For all readers interested in time series analysis.