Representation Estimation And Forecasting Of The Multivariate Index Augmented Autoregressive Model

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Financial Mathematics, Volatility and Covariance Modelling

Author : Julien Chevallier
Publisher : Routledge
ISBN 13 : 1351669095
Total Pages : 381 pages
Book Rating : 4.3/5 (516 download)

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Book Synopsis Financial Mathematics, Volatility and Covariance Modelling by : Julien Chevallier

Download or read book Financial Mathematics, Volatility and Covariance Modelling written by Julien Chevallier and published by Routledge. This book was released on 2019-06-28 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an up-to-date series of advanced chapters on applied financial econometric techniques pertaining the various fields of commodities finance, mathematics & stochastics, international macroeconomics and financial econometrics. Financial Mathematics, Volatility and Covariance Modelling: Volume 2 provides a key repository on the current state of knowledge, the latest debates and recent literature on financial mathematics, volatility and covariance modelling. The first section is devoted to mathematical finance, stochastic modelling and control optimization. Chapters explore the recent financial crisis, the increase of uncertainty and volatility, and propose an alternative approach to deal with these issues. The second section covers financial volatility and covariance modelling and explores proposals for dealing with recent developments in financial econometrics This book will be useful to students and researchers in applied econometrics; academics and students seeking convenient access to an unfamiliar area. It will also be of great interest established researchers seeking a single repository on the current state of knowledge, current debates and relevant literature.

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.

Multiple Time Series Modeling Using the SAS VARMAX Procedure

Author : Anders Milhoj
Publisher : SAS Institute
ISBN 13 : 162959749X
Total Pages : 210 pages
Book Rating : 4.6/5 (295 download)

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Book Synopsis Multiple Time Series Modeling Using the SAS VARMAX Procedure by : Anders Milhoj

Download or read book Multiple Time Series Modeling Using the SAS VARMAX Procedure written by Anders Milhoj and published by SAS Institute. This book was released on 2016-01-11 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: Aimed at econometricians who have completed at least one course in time series modeling, this comprehensive book will teach you the time series analytical possibilities that SAS offers today. --

Bayesian Multivariate Time Series Methods for Empirical Macroeconomics

Author : Gary Koop
Publisher : Now Publishers Inc
ISBN 13 : 160198362X
Total Pages : 104 pages
Book Rating : 4.6/5 (19 download)

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Book Synopsis Bayesian Multivariate Time Series Methods for Empirical Macroeconomics by : Gary Koop

Download or read book Bayesian Multivariate Time Series Methods for Empirical Macroeconomics written by Gary Koop and published by Now Publishers Inc. This book was released on 2010 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Multivariate Time Series Methods for Empirical Macroeconomics provides a survey of the Bayesian methods used in modern empirical macroeconomics. These models have been developed to address the fact that most questions of interest to empirical macroeconomists involve several variables and must be addressed using multivariate time series methods. Many different multivariate time series models have been used in macroeconomics, but Vector Autoregressive (VAR) models have been among the most popular. Bayesian Multivariate Time Series Methods for Empirical Macroeconomics reviews and extends the Bayesian literature on VARs, TVP-VARs and TVP-FAVARs with a focus on the practitioner. The authors go beyond simply defining each model, but specify how to use them in practice, discuss the advantages and disadvantages of each and offer tips on when and why each model can be used.

Time Series and Panel Data Econometrics

Author : M. Hashem Pesaran
Publisher : Oxford University Press
ISBN 13 : 0198736916
Total Pages : 1095 pages
Book Rating : 4.1/5 (987 download)

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Book Synopsis Time Series and Panel Data Econometrics by : M. Hashem Pesaran

Download or read book Time Series and Panel Data Econometrics written by M. Hashem Pesaran and published by Oxford University Press. This book was released on 2015 with total page 1095 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is concerned with recent developments in time series and panel data techniques for the analysis of macroeconomic and financial data. It provides a rigorous, nevertheless user-friendly, account of the time series techniques dealing with univariate and multivariate time series models, as well as panel data models. It is distinct from other time series texts in the sense that it also covers panel data models and attempts at a more coherent integration of time series, multivariate analysis, and panel data models. It builds on the author's extensive research in the areas of time series and panel data analysis and covers a wide variety of topics in one volume. Different parts of the book can be used as teaching material for a variety of courses in econometrics. It can also be used as reference manual. It begins with an overview of basic econometric and statistical techniques, and provides an account of stochastic processes, univariate and multivariate time series, tests for unit roots, cointegration, impulse response analysis, autoregressive conditional heteroskedasticity models, simultaneous equation models, vector autoregressions, causality, forecasting, multivariate volatility models, panel data models, aggregation and global vector autoregressive models (GVAR). The techniques are illustrated using Microfit 5 (Pesaran and Pesaran, 2009, OUP) with applications to real output, inflation, interest rates, exchange rates, and stock prices.

Elements of Forecasting

Author : Francis X. Diebold
Publisher : South-Western Pub
ISBN 13 : 9780324359046
Total Pages : 366 pages
Book Rating : 4.3/5 (59 download)

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Book Synopsis Elements of Forecasting by : Francis X. Diebold

Download or read book Elements of Forecasting written by Francis X. Diebold and published by South-Western Pub. This book was released on 2007 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: ELEMENTARY FORECASTING focuses on the core techniques of widest applicability. The author illustrates all methods with detailed real-world applications, many of them international in flavor, designed to mimic typical forecasting situations.

Modeling Financial Time Series with S-PLUS

Author : Eric Zivot
Publisher : Springer Science & Business Media
ISBN 13 : 0387217630
Total Pages : 632 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis Modeling Financial Time Series with S-PLUS by : Eric Zivot

Download or read book Modeling Financial Time Series with S-PLUS written by Eric Zivot and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 632 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Working Paper Series

Author :
Publisher :
ISBN 13 :
Total Pages : 556 pages
Book Rating : 4.F/5 ( download)

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Book Synopsis Working Paper Series by :

Download or read book Working Paper Series written by and published by . This book was released on 1998 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Multiple Time Series Models

Author : Patrick T. Brandt
Publisher : SAGE
ISBN 13 : 1412906563
Total Pages : 121 pages
Book Rating : 4.4/5 (129 download)

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Book Synopsis Multiple Time Series Models by : Patrick T. Brandt

Download or read book Multiple Time Series Models written by Patrick T. Brandt and published by SAGE. This book was released on 2007 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many analyses of time series data involve multiple, related variables. Modeling Multiple Time Series presents many specification choices and special challenges. This book reviews the main competing approaches to modeling multiple time series: simultaneous equations, ARIMA, error correction models, and vector autoregression. The text focuses on vector autoregression (VAR) models as a generalization of the other approaches mentioned. Specification, estimation, and inference using these models is discussed. The authors also review arguments for and against using multi-equation time series models. Two complete, worked examples show how VAR models can be employed. An appendix discusses software that can be used for multiple time series models and software code for replicating the examples is available. Key Features: * Offers a detailed comparison of different time series methods and approaches. * Includes a self-contained introduction to vector autoregression modeling. * Situates multiple time series modeling as a natural extension of commonly taught statistical models.

Essentials of Time Series for Financial Applications

Author : Massimo Guidolin
Publisher : Academic Press
ISBN 13 : 0128134100
Total Pages : 435 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Essentials of Time Series for Financial Applications by : Massimo Guidolin

Download or read book Essentials of Time Series for Financial Applications written by Massimo Guidolin and published by Academic Press. This book was released on 2018-05-29 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: Essentials of Time Series for Financial Applications serves as an agile reference for upper level students and practitioners who desire a formal, easy-to-follow introduction to the most important time series methods applied in financial applications (pricing, asset management, quant strategies, and risk management). Real-life data and examples developed with EViews illustrate the links between the formal apparatus and the applications. The examples either directly exploit the tools that EViews makes available or use programs that by employing EViews implement specific topics or techniques. The book balances a formal framework with as few proofs as possible against many examples that support its central ideas. Boxes are used throughout to remind readers of technical aspects and definitions and to present examples in a compact fashion, with full details (workout files) available in an on-line appendix. The more advanced chapters provide discussion sections that refer to more advanced textbooks or detailed proofs. - Provides practical, hands-on examples in time-series econometrics - Presents a more application-oriented, less technical book on financial econometrics - Offers rigorous coverage, including technical aspects and references for the proofs, despite being an introduction - Features examples worked out in EViews (9 or higher)

Diffusion Indexes

Author : James H. Stock
Publisher :
ISBN 13 :
Total Pages : 80 pages
Book Rating : 4.3/5 (243 download)

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Book Synopsis Diffusion Indexes by : James H. Stock

Download or read book Diffusion Indexes written by James H. Stock and published by . This book was released on 1998 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper considers forecasting a single time series using more predictors than there are time series observations. The approach is to construct a relatively few indexes, akin to diffusion indexes, which are weighted averages of the predictors, using an approximate dynamic factor model. Estimation is discussed for balanced and unbalanced panels. The estimated dynamic factors are (uniformly) consistent, even in the presence of time varying parameters and/or data contamination, and forecasts based on the estimated factors are efficient. In an application to forecasting U.S. inflation and industrial production using 224 monthly time series, these forecasts outperform various state-of-the-art benchmark models.

Signal Processing for Neuroscientists

Author : Wim van Drongelen
Publisher : Elsevier
ISBN 13 : 008046775X
Total Pages : 319 pages
Book Rating : 4.0/5 (84 download)

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Book Synopsis Signal Processing for Neuroscientists by : Wim van Drongelen

Download or read book Signal Processing for Neuroscientists written by Wim van Drongelen and published by Elsevier. This book was released on 2006-12-18 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: Signal Processing for Neuroscientists introduces analysis techniques primarily aimed at neuroscientists and biomedical engineering students with a reasonable but modest background in mathematics, physics, and computer programming. The focus of this text is on what can be considered the 'golden trio' in the signal processing field: averaging, Fourier analysis, and filtering. Techniques such as convolution, correlation, coherence, and wavelet analysis are considered in the context of time and frequency domain analysis. The whole spectrum of signal analysis is covered, ranging from data acquisition to data processing; and from the mathematical background of the analysis to the practical application of processing algorithms. Overall, the approach to the mathematics is informal with a focus on basic understanding of the methods and their interrelationships rather than detailed proofs or derivations. One of the principle goals is to provide the reader with the background required to understand the principles of commercially available analyses software, and to allow him/her to construct his/her own analysis tools in an environment such as MATLAB®. - Multiple color illustrations are integrated in the text - Includes an introduction to biomedical signals, noise characteristics, and recording techniques - Basics and background for more advanced topics can be found in extensive notes and appendices - A Companion Website hosts the MATLAB scripts and several data files: http://www.elsevierdirect.com/companion.jsp?ISBN=9780123708670

Introduction to Time Series Forecasting With Python

Author : Jason Brownlee
Publisher : Machine Learning Mastery
ISBN 13 :
Total Pages : 359 pages
Book Rating : 4./5 ( download)

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Book Synopsis Introduction to Time Series Forecasting With Python by : Jason Brownlee

Download or read book Introduction to Time Series Forecasting With Python written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2017-02-16 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: Time series forecasting is different from other machine learning problems. The key difference is the fixed sequence of observations and the constraints and additional structure this provides. In this Ebook, finally cut through the math and specialized methods for time series forecasting. Using clear explanations, standard Python libraries and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement forecasting models for time series data.

Theory and Methods of Statistics

Author : P.K. Bhattacharya
Publisher : Academic Press
ISBN 13 : 0128041234
Total Pages : 546 pages
Book Rating : 4.1/5 (28 download)

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Book Synopsis Theory and Methods of Statistics by : P.K. Bhattacharya

Download or read book Theory and Methods of Statistics written by P.K. Bhattacharya and published by Academic Press. This book was released on 2016-06-23 with total page 546 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theory and Methods of Statistics covers essential topics for advanced graduate students and professional research statisticians. This comprehensive resource covers many important areas in one manageable volume, including core subjects such as probability theory, mathematical statistics, and linear models, and various special topics, including nonparametrics, curve estimation, multivariate analysis, time series, and resampling. The book presents subjects such as "maximum likelihood and sufficiency," and is written with an intuitive, heuristic approach to build reader comprehension. It also includes many probability inequalities that are not only useful in the context of this text, but also as a resource for investigating convergence of statistical procedures. - Codifies foundational information in many core areas of statistics into a comprehensive and definitive resource - Serves as an excellent text for select master's and PhD programs, as well as a professional reference - Integrates numerous examples to illustrate advanced concepts - Includes many probability inequalities useful for investigating convergence of statistical procedures

Applied Multivariate Statistical Analysis

Author : Wolfgang Karl Härdle
Publisher : Springer Nature
ISBN 13 : 3031638336
Total Pages : 611 pages
Book Rating : 4.0/5 (316 download)

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Book Synopsis Applied Multivariate Statistical Analysis by : Wolfgang Karl Härdle

Download or read book Applied Multivariate Statistical Analysis written by Wolfgang Karl Härdle and published by Springer Nature. This book was released on with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Dynamic Factor Models

Author : Jörg Breitung
Publisher :
ISBN 13 : 9783865580979
Total Pages : 29 pages
Book Rating : 4.5/5 (89 download)

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Book Synopsis Dynamic Factor Models by : Jörg Breitung

Download or read book Dynamic Factor Models written by Jörg Breitung and published by . This book was released on 2005 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Personalized Predictive Modeling in Type 1 Diabetes

Author : Eleni I. Georga
Publisher : Academic Press
ISBN 13 : 0128051469
Total Pages : 253 pages
Book Rating : 4.1/5 (28 download)

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Book Synopsis Personalized Predictive Modeling in Type 1 Diabetes by : Eleni I. Georga

Download or read book Personalized Predictive Modeling in Type 1 Diabetes written by Eleni I. Georga and published by Academic Press. This book was released on 2017-12-11 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: Personalized Predictive Modeling in Diabetes features state-of-the-art methodologies and algorithmic approaches which have been applied to predictive modeling of glucose concentration, ranging from simple autoregressive models of the CGM time series to multivariate nonlinear regression techniques of machine learning. Developments in the field have been analyzed with respect to: (i) feature set (univariate or multivariate), (ii) regression technique (linear or non-linear), (iii) learning mechanism (batch or sequential), (iv) development and testing procedure and (v) scaling properties. In addition, simulation models of meal-derived glucose absorption and insulin dynamics and kinetics are covered, as an integral part of glucose predictive models. This book will help engineers and clinicians to: select a regression technique which can capture both linear and non-linear dynamics in glucose metabolism in diabetes, and which exhibits good generalization performance under stationary and non-stationary conditions; ensure the scalability of the optimization algorithm (learning mechanism) with respect to the size of the dataset, provided that multiple days of patient monitoring are needed to obtain a reliable predictive model; select a features set which efficiently represents both spatial and temporal dependencies between the input variables and the glucose concentration; select simulation models of subcutaneous insulin absorption and meal absorption; identify an appropriate validation procedure, and identify realistic performance measures. Describes fundamentals of modeling techniques as applied to glucose control Covers model selection process and model validation Offers computer code on a companion website to show implementation of models and algorithms Features the latest developments in the field of diabetes predictive modeling