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Modelling Nonlinear Economic Time Series

Author : Timo Teräsvirta
Publisher : OUP Oxford
Page : 592 pages
File Size : 47,10 MB
Release : 2010-12-16
Category : Business & Economics
ISBN : 9780199587148

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This book contains an extensive up-to-date overview of nonlinear time series models and their application to modelling economic relationships. It considers nonlinear models in stationary and nonstationary frameworks, and both parametric and nonparametric models are discussed. The book contains examples of nonlinear models in economic theory and presents the most common nonlinear time series models. Importantly, it shows the reader how to apply these models in practice. For thispurpose, the building of various nonlinear models with its three stages of model building: specification, estimation and evaluation, is discussed in detail and is illustrated by several examples involving both economic and non-economic data. Since estimation of nonlinear time series models is carried outusing numerical algorithms, the book contains a chapter on estimating parametric nonlinear models and another on estimating nonparametric ones.Forecasting is a major reason for building time series models, linear or nonlinear. The book contains a discussion on forecasting with nonlinear models, both parametric and nonparametric, and considers numerical techniques necessary for computing multi-period forecasts from them. The main focus of the book is on models of the conditional mean, but models of the conditional variance, mainly those of autoregressive conditional heteroskedasticity, receive attention as well. A separate chapter isdevoted to state space models. As a whole, the book is an indispensable tool for researchers interested in nonlinear time series and is also suitable for teaching courses in econometrics and time series analysis.

Modelling Nonlinear Economic Time Series

Author : Timo Teräsvirta
Publisher :
Page : 557 pages
File Size : 45,52 MB
Release : 2010
Category : Econometric models
ISBN : 9780191595387

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A comprehensive assessment of many recent developments in the modelling of time series, this text introduces various nonlinear models and discusses their practical use, encouraging the reader to apply nonlinear models to their practical modelling problems.

Non-Linear Time Series Models in Empirical Finance

Author : Philip Hans Franses
Publisher : Cambridge University Press
Page : 299 pages
File Size : 32,7 MB
Release : 2000-07-27
Category : Business & Economics
ISBN : 0521770416

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This 2000 volume reviews non-linear time series models, and their applications to financial markets.

Nonlinear Economic Models

Author : John Creedy
Publisher : Edward Elgar Publishing
Page : 312 pages
File Size : 49,87 MB
Release : 1997
Category : Business & Economics
ISBN :

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A sequel to Creedy and Martin's (eds.) Chaos and Nonlinear Models (1994). Compiles recent developments in such techniques as cross- sectional studies of income distribution and discrete choice models, time series models of exchange rate dynamics and jump processes, and artificial neural networks and genetic algorithms of financial markets. Also considers the development of theoretical models and estimating and testing methods, with a wide range of applications in microeconomics, macroeconomics, labor, and finance. Annotation copyrighted by Book News, Inc., Portland, OR

Modelling Financial Time Series

Author : Stephen J. Taylor
Publisher : World Scientific
Page : 297 pages
File Size : 10,51 MB
Release : 2008
Category : Business & Economics
ISBN : 9812770852

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This book contains several innovative models for the prices of financial assets. First published in 1986, it is a classic text in the area of financial econometrics. It presents ARCH and stochastic volatility models that are often used and cited in academic research and are applied by quantitative analysts in many banks. Another often-cited contribution of the first edition is the documentation of statistical characteristics of financial returns, which are referred to as stylized facts. This second edition takes into account the remarkable progress made by empirical researchers during the past two decades from 1986 to 2006. In the new Preface, the author summarizes this progress in two key areas: firstly, measuring, modelling and forecasting volatility; and secondly, detecting and exploiting price trends. Sample Chapter(s). Chapter 1: Introduction (1,134 KB). Contents: Features of Financial Returns; Modelling Price Volatility; Forecasting Standard Deviations; The Accuracy of Autocorrelation Estimates; Testing the Random Walk Hypothesis; Forecasting Trends in Prices; Evidence Against the Efficiency of Futures Markets; Valuing Options; Appendix: A Computer Program for Modelling Financial Time Series. Readership: Academic researchers in finance & economics; quantitative analysts.

Nonlinear Time Series

Author : Jianqing Fan
Publisher : Springer Science & Business Media
Page : 565 pages
File Size : 39,79 MB
Release : 2008-09-11
Category : Mathematics
ISBN : 0387693955

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This is the first book that integrates useful parametric and nonparametric techniques with time series modeling and prediction, the two important goals of time series analysis. Such a book will benefit researchers and practitioners in various fields such as econometricians, meteorologists, biologists, among others who wish to learn useful time series methods within a short period of time. The book also intends to serve as a reference or text book for graduate students in statistics and econometrics.

Modeling Financial Time Series with S-PLUS

Author : Eric Zivot
Publisher : Springer Science & Business Media
Page : 632 pages
File Size : 24,90 MB
Release : 2013-11-11
Category : Business & Economics
ISBN : 0387217630

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The field of financial econometrics has exploded over the last decade This book represents an integration of theory, methods, and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics. This is the first book to show the power of S-PLUS for the analysis of time series data. It is written for researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance. Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts. This Second Edition is updated to cover S+FinMetrics 2.0 and includes new chapters on copulas, nonlinear regime switching models, continuous-time financial models, generalized method of moments, semi-nonparametric conditional density models, and the efficient method of moments. Eric Zivot is an associate professor and Gary Waterman Distinguished Scholar in the Economics Department, and adjunct associate professor of finance in the Business School at the University of Washington. He regularly teaches courses on econometric theory, financial econometrics and time series econometrics, and is the recipient of the Henry T. Buechel Award for Outstanding Teaching. He is an associate editor of Studies in Nonlinear Dynamics and Econometrics. He has published papers in the leading econometrics journals, including Econometrica, Econometric Theory, the Journal of Business and Economic Statistics, Journal of Econometrics, and the Review of Economics and Statistics. Jiahui Wang is an employee of Ronin Capital LLC. He received a Ph.D. in Economics from the University of Washington in 1997. He has published in leading econometrics journals such as Econometrica and Journal of Business and Economic Statistics, and is the Principal Investigator of National Science Foundation SBIR grants. In 2002 Dr. Wang was selected as one of the "2000 Outstanding Scholars of the 21st Century" by International Biographical Centre.

Nonlinear Time Series Analysis with R

Author : Ray Huffaker
Publisher : Oxford University Press
Page : 312 pages
File Size : 45,29 MB
Release : 2017-10-20
Category : Mathematics
ISBN : 0191085790

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Nonlinear Time Series Analysis with R provides a practical guide to emerging empirical techniques allowing practitioners to diagnose whether highly fluctuating and random appearing data are most likely driven by random or deterministic dynamic forces. It joins the chorus of voices recommending 'getting to know your data' as an essential preliminary evidentiary step in modelling. Time series are often highly fluctuating with a random appearance. Observed volatility is commonly attributed to exogenous random shocks to stable real-world systems. However, breakthroughs in nonlinear dynamics raise another possibility: highly complex dynamics can emerge endogenously from astoundingly parsimonious deterministic nonlinear models. Nonlinear Time Series Analysis (NLTS) is a collection of empirical tools designed to aid practitioners detect whether stochastic or deterministic dynamics most likely drive observed complexity. Practitioners become 'data detectives' accumulating hard empirical evidence supporting their modelling approach. This book is targeted to professionals and graduate students in engineering and the biophysical and social sciences. Its major objectives are to help non-mathematicians — with limited knowledge of nonlinear dynamics — to become operational in NLTS; and in this way to pave the way for NLTS to be adopted in the conventional empirical toolbox and core coursework of the targeted disciplines. Consistent with modern trends in university instruction, the book makes readers active learners with hands-on computer experiments in R code directing them through NLTS methods and helping them understand the underlying logic (please see www.marco.bittelli.com). The computer code is explained in detail so that readers can adjust it for use in their own work. The book also provides readers with an explicit framework — condensed from sound empirical practices recommended in the literature — that details a step-by-step procedure for applying NLTS in real-world data diagnostics.

Nonlinear Econometric Modeling in Time Series

Author : William A. Barnett
Publisher : Cambridge University Press
Page : 248 pages
File Size : 47,22 MB
Release : 2000-05-22
Category : Business & Economics
ISBN : 9780521594240

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This book presents some of the more recent developments in nonlinear time series, including Bayesian analysis and cointegration tests.

Nonlinear Financial Econometrics: Forecasting Models, Computational and Bayesian Models

Author : G. Gregoriou
Publisher : Springer
Page : 216 pages
File Size : 45,94 MB
Release : 2010-12-21
Category : Business & Economics
ISBN : 0230295223

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This book investigates several competing forecasting models for interest rates, financial returns, and realized volatility, addresses the usefulness of nonlinear models for hedging purposes, and proposes new computational techniques to estimate financial processes.