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Non-Linear Time Series Models in Empirical Finance

Although many of the models commonly used in empirical finance are linear, the nature of financial data suggests that non-linear models are;

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Non-Linear Time Series Models in Empirical Finance

Although many of the models commonly used in empirical finance are linear, the nature of financial data suggests that non-linear models are;

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Non-Gaussian Autoregressive-Type Time Series

-series models into different groups such as linear stationary models with non-Gaussian innovations, linear stationary models with non-Gaussian;

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Linear Models and TimeSeries Analysis

A comprehensive and timely edition on an emerging new trend in time series Linear Models and Time-Series Analysis: Regression, ANOVA;

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Handbook of Empirical Economics and Finance

inferential issues that transpire in the analysis of cross-sectional, time series, and panel data-based empirical models in economics, finance, and;

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Handbook of Empirical Economics and Finance

inferential issues that transpire in the analysis of cross-sectional, time series, and panel data-based empirical models in economics, finance, and;

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State-Space Models

theoretical developments of the models and their applications in economics and finance. The book includes nonlinear and non-Gaussian time series;

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State-Space Models

theoretical developments of the models and their applications in economics and finance. The book includes nonlinear and non-Gaussian time series;

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Business Cycle Fluctuations & Economic Policy

countries are analysed using non-linear time series and switching time series models as well as in-sample and jack-knife out-of-sample forecasts;

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Recent Advances in Estimating Nonlinear Models

number of series exhibit nonlinear dynamics as opposed to the alternative--linear dynamics. Incorporating these concepts involves deriving and;

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Nonlinear Models in Mathematical Finance

This book provides an overview on the current state-of-the-art research on non-linear option pricing. Non-linear models are becoming more;

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Multivariate Time Series Analysis

econometric models and their applications to real-world empirical research. Differing from the traditional approach to multivariate time series, the;

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Time Series Techniques for Economists

of time series analysis: univariate models, multivariate models, and non-linear models. In each case the basic theory is outlined and then;

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Time Series Modelling in Earth Sciences

) extensions, the text also draws attention to non-linear methods, as well as state-space, dynamic linear, wavelet, volatility and long memory models;

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Stochastic Models for Time Series

This book presents essential tools for modelling non-linear time series. The first part of the book describes the main standard tools;

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Statistical Inference for Financial Engineering

analyzing financial time series data. In order to describe the actual financial data, various stochastic processes, e.g. non-Gaussian linear;

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Non-Linear Time Series

should have attended a prior course on linear time series, and a good grasp of simulation-based inferential methods is recommended. This book;

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Unobserved Components and Time Series Econometrics

methodological perspectives. It also presents empirical studies where the UC time series methodology is adopted. Drawing on the intellectual influence;

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Statistics for Finance

cover various statistical and mathematical techniques, including linear and nonlinear time series analysis, stochastic calculus models;

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Statistics for Finance

various statistical and mathematical techniques, including linear and nonlinear time series analysis, stochastic calculus models, stochastic;

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Non-Linear Time Series

of linear time series is now well established, that of non-linear time series is still a rapidly developing subject. This book, now available;

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Time Series Analysis

dozen academic honors and awards. His research interests include time series analysis, prediction theory, state space systems, linear models;

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Applied Econometric Times Series

-of-sample forecasting techniques, non-linear time-series models, Monte Carlo analysis, and bootstrapping. Numerous examples from fields ranging from;

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Nonlinear Time Series Analysis

and methods and review the improvements upon linear time series models. The need for this book is based on the recent developments;

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Quantitative Financial Economics

comprehensive introduction to models of economic behaviour in financial markets, focusing on discrete time series analysis. It covers the most recent;

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Time Series Modeling of Neuroscience Data

approach, in which dynamic models of all kinds, such as linear/nonlinear differential equation models and time series models, are used for;

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A Primer on Linear Models

of the general linear model, including multiple regression models, one-way ANOVA, mixed-effects models, and time series models. It then;

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