new introduction to multiple time series analysis

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Introduction To Multiple Time Series Analysis

Author : Helmut Lütkepohl
ISBN : 0387569405
Genre : Science
File Size : 90. 69 MB
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Introduction To Multiple Time Series Analysis

Author : Helmut Lütkepohl
ISBN : 9783662026915
Genre : Business & Economics
File Size : 28. 79 MB
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Introduction To Multiple Time Series Analysis

Author : Helmut Lütkepohl
ISBN : 3662026929
Genre : Business & Economics
File Size : 71. 70 MB
Format : PDF, Kindle
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New Introduction To Multiple Time Series Analysis

Author : Helmut Lütkepohl
ISBN : 3540262393
Genre : Business & Economics
File Size : 25. 37 MB
Format : PDF
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This is the new and totally revised edition of Lütkepohl’s classic 1991 work. It provides a detailed introduction to the main steps of analyzing multiple time series, model specification, estimation, model checking, and for using the models for economic analysis and forecasting. The book now includes new chapters on cointegration analysis, structural vector autoregressions, cointegrated VARMA processes and multivariate ARCH models. The book bridges the gap to the difficult technical literature on the topic. It is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it.

Introduction To Modern Time Series Analysis

Author : Gebhard Kirchgässner
ISBN : 9783642334368
Genre : Business & Economics
File Size : 45. 73 MB
Format : PDF, ePub
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This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series, bridging the gap between methods and realistic applications. It presents the most important approaches to the analysis of time series, which may be stationary or nonstationary. Modelling and forecasting univariate time series is the starting point. For multiple stationary time series, Granger causality tests and vector autogressive models are presented. As the modelling of nonstationary uni- or multivariate time series is most important for real applied work, unit root and cointegration analysis as well as vector error correction models are a central topic. Tools for analysing nonstationary data are then transferred to the panel framework. Modelling the (multivariate) volatility of financial time series with autogressive conditional heteroskedastic models is also treated.

Structural Vector Autoregressive Analysis

Author : Lutz Kilian
ISBN : 9781108195287
Genre : Business & Economics
File Size : 83. 70 MB
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Structural vector autoregressive (VAR) models are important tools for empirical work in macroeconomics, finance, and related fields. This book not only reviews the many alternative structural VAR approaches discussed in the literature, but also highlights their pros and cons in practice. It provides guidance to empirical researchers as to the most appropriate modeling choices, methods of estimating, and evaluating structural VAR models. The book traces the evolution of the structural VAR methodology and contrasts it with other common methodologies, including dynamic stochastic general equilibrium (DSGE) models. It is intended as a bridge between the often quite technical econometric literature on structural VAR modeling and the needs of empirical researchers. The focus is not on providing the most rigorous theoretical arguments, but on enhancing the reader's understanding of the methods in question and their assumptions. Empirical examples are provided for illustration.

Introduction To Time Series Analysis And Forecasting

Author : Douglas C. Montgomery
ISBN : 9781118745151
Genre : Mathematics
File Size : 77. 2 MB
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Praise for the First Edition "…[t]he book is great for readers who need to apply the methods and models presented but have little background in mathematics and statistics." -MAA Reviews Thoroughly updated throughout, Introduction to Time Series Analysis and Forecasting, Second Edition presents the underlying theories of time series analysis that are needed to analyze time-oriented data and construct real-world short- to medium-term statistical forecasts. Authored by highly-experienced academics and professionals in engineering statistics, the Second Edition features discussions on both popular and modern time series methodologies as well as an introduction to Bayesian methods in forecasting. Introduction to Time Series Analysis and Forecasting, Second Edition also includes: Over 300 exercises from diverse disciplines including health care, environmental studies, engineering, and finance More than 50 programming algorithms using JMP®, SAS®, and R that illustrate the theory and practicality of forecasting techniques in the context of time-oriented data New material on frequency domain and spatial temporal data analysis Expanded coverage of the variogram and spectrum with applications as well as transfer and intervention model functions A supplementary website featuring PowerPoint® slides, data sets, and select solutions to the problems Introduction to Time Series Analysis and Forecasting, Second Edition is an ideal textbook upper-undergraduate and graduate-levels courses in forecasting and time series. The book is also an excellent reference for practitioners and researchers who need to model and analyze time series data to generate forecasts.

Analysis Of Integrated And Cointegrated Time Series With R

Author : Bernhard Pfaff
ISBN : 9780387759678
Genre : Business & Economics
File Size : 68. 87 MB
Format : PDF, Docs
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This book is designed for self study. The reader can apply the theoretical concepts directly within R by following the examples.

Time Series Analysis

Author : James Douglas Hamilton
ISBN : 0691042896
Genre : Business & Economics
File Size : 44. 15 MB
Format : PDF, ePub
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Difference equations; Lag operators; Stationary ARMA processes; Forecasting; Maximum likelihood estimation; Spectral analysis; Asymptotic distribution theory; Linear regression models; Linear systems of simultaneous equations; Covariance-stationary vector processes; Vector autoregressions; Bayesian analysis; The Kalman Filter; Generalized method of moments; Models of nonstationary time series; Processes with deterministic time trends; Univariate processes with unit roots; Unit roots in multivariate time series; Cointegration; Full-information maximum likelihood analysis of cointegrated systems; Time series models of heteroskedasticity; Modeling time series with changes in regime; Mathematical review; Statistical tables; Answers to selected exercises; Greek letters and mathematical symbols used in the text.

Multivariate Time Series Analysis

Author : Ruey S. Tsay
ISBN : 1118617762
Genre : Mathematics
File Size : 28. 97 MB
Format : PDF, Docs
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An accessible guide to the multivariate time series tools used in numerous real-world applications Multivariate Time Series Analysis: With R and Financial Applications is the much anticipated sequel coming from one of the most influential and prominent experts on the topic of time series. Through a fundamental balance of theory and methodology, the book supplies readers with a comprehensible approach to financial econometric models and their applications to real-world empirical research. Differing from the traditional approach to multivariate time series, the book focuses on reader comprehension by emphasizing structural specification, which results in simplified parsimonious VAR MA modeling. Multivariate Time Series Analysis: With R and Financial Applications utilizes the freely available R software package to explore complex data and illustrate related computation and analyses. Featuring the techniques and methodology of multivariate linear time series, stationary VAR models, VAR MA time series and models, unitroot process, factor models, and factor-augmented VAR models, the book includes: • Over 300 examples and exercises to reinforce the presented content • User-friendly R subroutines and research presented throughout to demonstrate modern applications • Numerous datasets and subroutines to provide readers with a deeper understanding of the material Multivariate Time Series Analysis is an ideal textbook for graduate-level courses on time series and quantitative finance and upper-undergraduate level statistics courses in time series. The book is also an indispensable reference for researchers and practitioners in business, finance, and econometrics.

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