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Estimation in Conditionally Heteroscedastic Time Series Models

). This monograph concentrates on mathematical statistical problems associated with fitting conditionally heteroscedastic time series models to;

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ARCH Models and Financial Applications

constraints on the parameters. In this con- text, ARCH (Autoregressive Conditionally Heteroscedastic) models, introduced by Engle (1982), arise as an;

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A Course in Time Series Analysis

New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time;

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Nonlinear Time Series

methods in model estimation, specification testing, and selection of time series data. After a brief introduction, the book examines;

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Nonlinear Time Series

methods in model estimation, specification testing, and selection of time series data. After a brief introduction, the book examines;

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Time Series Models

another level from the first book, focusing on the estimation, testing, and specification of both univariate and multivariate time series models;

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Estimation and Prediction for Certain Models of Spatial Time Series

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Estimation and Prediction for Certain Models of Spatial Time Series

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Introduction To Statistical Time Series

estimation Regression, trend, and seasonality Unit root and explosive time series To accommodate a wide variety of readers, review material;

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Interpolation and Extrapolation Optimal Designs 2

This book considers various extensions of the topics treated in the first volume of this series, in relation to the class of models and the;

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Modelling Nonlinear Economic Time Series

-economic data. Since estimation of nonlinear time series models is carried out using numerical algorithms, the book contains a chapter on;

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Introduction to Time Series Modeling

In time series modeling, the behavior of a certain phenomenon is expressed in relation to the past values of itself and other covariates;

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Hidden Markov Models for Time Series

Reveals How HMMs Can Be Used as General-Purpose Time Series Models Implements all methods in R

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Unobserved Components and Time Series Econometrics

of unobserved components time series models; and time series econometrics and estimation and testing. These types of time series models have seen wide;

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Time Series Modeling of Neuroscience Data

Recent advances in brain science measurement technology have given researchers access to very large-scale time series data such as EEG/MEG;

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Economic Time Series

, introducing, applying, and comparing various seasonally periodic models Section II examines the estimation of time series components when models for;

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Option Pricing and Estimation of Financial Models with R

covariation estimation, change point analysis, asymptotic expansion and classification of financial time series from a statistical viewpoint. The book;

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Introduction to Time Series Modeling with Applications in R

likelihood method, recursive estimation for state-space models and model selection by AIC. Along with the standard stationary time series models;

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Econometric Modelling with Time Series

This book provides a general framework for specifying, estimating and testing time series econometric models. Special emphasis is given to;

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Econometric Modelling with Time Series

This book provides a general framework for specifying, estimating and testing time series econometric models. Special emphasis is given to;

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Time Series In High Dimensions

Factor models have become the most successful tool in the analysis and forecasting of high-dimensional time series. This monograph provides;

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Recent Advances in Estimating Nonlinear Models

estimation techniques, offering new methods and insights into nonlinear time series analysis. It features cutting-edge research from leading;

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Non-Linear Time Series

This book offers a useful combination of probabilistic and statistical tools for analyzing nonlinear time series. Key features of the book;

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International Symposia in Economic Theory and Econometrics

selection models, (ii) nonparametric and semi-parametric approaches to qualitative and sample selection models, and (iii) nonlinear estimation;

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Nonlinear Statistical Modeling

selection models, (ii) nonparametric and semi-parametric approaches to qualitative and sample selection models, and (iii) nonlinear estimation;

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Estimation of Spatial Panels

well as dynamic models. Estimation of Spatial Panels provides some recent developments on the specification and estimation of such models. The;

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Handbook Of Applied Econometrics And Statistical Inference

, time series analysis, Bayesian inference, and distribution theory for applications in statistics, economics, medicine, biology, engineering;

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