{"product_id":"multivariate-time-series-analysis-with-r-and-financial-applications-hardback-9781118617908","title":"Multivariate Time Series Analysis; With R and Financial Applications (Hardback) 9781118617908","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMultivariate Time Series Analysis\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eWith R and Financial Applications\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eRuey S. Tsay (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781118617908, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 28 January 2014\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e520 pages\u003cbr\u003e23.6 x 16 x 3.3 cm, 0.862 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eAn accessible guide to the multivariate time series tools used in numerous real-world applications\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eMultivariate Time Series Analysis: With R and Financial Applications\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eDiffering 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. \u003ci\u003eMultivariate Time Series Analysis: With R and Financial\u003c\/i\u003e \u003ci\u003eApplications\u003c\/i\u003e 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:\u003c\/p\u003e \u003cp\u003e• Over 300 examples and exercises to reinforce the presented content\u003c\/p\u003e \u003cp\u003e• User-friendly R subroutines and research presented throughout to demonstrate modern applications\u003c\/p\u003e \u003cp\u003e• Numerous datasets and subroutines to provide readers with a deeper understanding of the material\u003c\/p\u003e \u003cp\u003e\u003ci\u003eMultivariate Time Series Analysis\u003c\/i\u003e 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003eAcknowledgements xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Multivariate Linear Time Series 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction, 1\u003c\/p\u003e \u003cp\u003e1.2 Some Basic Concepts, 5\u003c\/p\u003e \u003cp\u003e1.3 Cross-Covariance and Correlation Matrices, 8\u003c\/p\u003e \u003cp\u003e1.4 Sample CCM, 9\u003c\/p\u003e \u003cp\u003e1.5 Testing Zero Cross-Correlations, 12\u003c\/p\u003e \u003cp\u003e1.6 Forecasting, 16\u003c\/p\u003e \u003cp\u003e1.7 Model Representations, 18\u003c\/p\u003e \u003cp\u003e1.8 Outline of the Book, 22\u003c\/p\u003e \u003cp\u003e1.9 Software, 23\u003c\/p\u003e \u003cp\u003eExercises, 23\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Stationary Vector Autoregressive Time Series 27\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction, 27\u003c\/p\u003e \u003cp\u003e2.2 VAR(1) Models, 28\u003c\/p\u003e \u003cp\u003e2.3 VAR(2) Models, 37\u003c\/p\u003e \u003cp\u003e2.4 VAR(p) Models, 41\u003c\/p\u003e \u003cp\u003e2.5 Estimation, 44\u003c\/p\u003e \u003cp\u003e2.6 Order Selection, 61\u003c\/p\u003e \u003cp\u003e2.7 Model Checking, 66\u003c\/p\u003e \u003cp\u003e2.8 Linear Constraints, 80\u003c\/p\u003e \u003cp\u003e2.9 Forecasting, 82\u003c\/p\u003e \u003cp\u003e2.10 Impulse Response Functions, 89\u003c\/p\u003e \u003cp\u003e2.11 Forecast Error Variance Decomposition, 96\u003c\/p\u003e \u003cp\u003e2.12 Proofs, 98\u003c\/p\u003e \u003cp\u003eExercises, 100\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Vector Autoregressive Moving-Average Time Series 105\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Vector MA Models, 106\u003c\/p\u003e \u003cp\u003e3.2 Specifying VMA Order, 112\u003c\/p\u003e \u003cp\u003e3.3 Estimation of VMA Models, 113\u003c\/p\u003e \u003cp\u003e3.4 Forecasting of VMA Models, 126\u003c\/p\u003e \u003cp\u003e3.5 VARMA Models, 127\u003c\/p\u003e \u003cp\u003e3.6 Implications of VARMA Models, 139\u003c\/p\u003e \u003cp\u003e3.7 Linear Transforms of VARMA Processes, 141\u003c\/p\u003e \u003cp\u003e3.8 Temporal Aggregation of VARMA Processes, 144\u003c\/p\u003e \u003cp\u003e3.9 Likelihood Function of a VARMA Model, 146\u003c\/p\u003e \u003cp\u003e3.10 Innovations Approach to Exact Likelihood Function, 155\u003c\/p\u003e \u003cp\u003e3.11 Asymptotic Distribution of Maximum Likelihood Estimates, 160\u003c\/p\u003e \u003cp\u003e3.12 Model Checking of Fitted VARMA Models, 163\u003c\/p\u003e \u003cp\u003e3.13 Forecasting of VARMA Models, 164\u003c\/p\u003e \u003cp\u003e3.14 Tentative Order Identification, 166\u003c\/p\u003e \u003cp\u003e3.15 Empirical Analysis of VARMA Models, 176\u003c\/p\u003e \u003cp\u003e3.16 Appendix, 192\u003c\/p\u003e \u003cp\u003eExercises, 194\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Structural Specification of VARMA Models 199\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 The Kronecker Index Approach, 200\u003c\/p\u003e \u003cp\u003e4.2 The Scalar Component Approach, 212\u003c\/p\u003e \u003cp\u003e4.3 Statistics for Order Specification, 220\u003c\/p\u003e \u003cp\u003e4.4 Finding Kronecker Indices, 222\u003c\/p\u003e \u003cp\u003e4.5 Finding Scalar Component Models, 226\u003c\/p\u003e \u003cp\u003e4.6 Estimation, 237\u003c\/p\u003e \u003cp\u003e4.7 An Example, 245\u003c\/p\u003e \u003cp\u003e4.8 Appendix: Canonical Correlation Analysis, 259\u003c\/p\u003e \u003cp\u003eExercises, 262\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Unit-Root Nonstationary Processes 265\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Univariate Unit-Root Processes, 266\u003c\/p\u003e \u003cp\u003e5.2 Multivariate Unit-Root Processes, 279\u003c\/p\u003e \u003cp\u003e5.3 Spurious Regressions, 290\u003c\/p\u003e \u003cp\u003e5.4 Multivariate Exponential Smoothing, 291\u003c\/p\u003e \u003cp\u003e5.5 Cointegration, 294\u003c\/p\u003e \u003cp\u003e5.6 An Error-Correction Form, 297\u003c\/p\u003e \u003cp\u003e5.7 Implications of Cointegrating Vectors, 300\u003c\/p\u003e \u003cp\u003e5.8 Parameterization of Cointegrating Vectors, 302\u003c\/p\u003e \u003cp\u003e5.9 Cointegration Tests, 303\u003c\/p\u003e \u003cp\u003e5.10 Estimation of Error-Correction Models, 313\u003c\/p\u003e \u003cp\u003e5.11 Applications, 319\u003c\/p\u003e \u003cp\u003e5.12 Discussion, 326\u003c\/p\u003e \u003cp\u003e5.13 Appendix, 327\u003c\/p\u003e \u003cp\u003eExercises, 328\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Factor Models and Selected Topics 333\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Seasonal Models, 333\u003c\/p\u003e \u003cp\u003e6.2 Principal Component Analysis, 341\u003c\/p\u003e \u003cp\u003e6.3 Use of Exogenous Variables, 345\u003c\/p\u003e \u003cp\u003e6.4 Missing Values, 357\u003c\/p\u003e \u003cp\u003e6.5 Factor Models, 364\u003c\/p\u003e \u003cp\u003e6.6 Classification and Clustering Analysis, 386\u003c\/p\u003e \u003cp\u003eExercises, 394\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Multivariate Volatility Models 399\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Testing Conditional Heteroscedasticity, 401\u003c\/p\u003e \u003cp\u003e7.2 Estimation of Multivariate Volatility Models, 407\u003c\/p\u003e \u003cp\u003e7.3 Diagnostic Checks of Volatility Models, 409\u003c\/p\u003e \u003cp\u003e7.4 Exponentially Weighted Moving Average, 414\u003c\/p\u003e \u003cp\u003e7.5 BEKK Models, 417\u003c\/p\u003e \u003cp\u003e7.6 Cholesky Decomposition and Volatility Modeling, 420\u003c\/p\u003e \u003cp\u003e7.7 Dynamic Conditional Correlation Models, 428\u003c\/p\u003e \u003cp\u003e7.8 Orthogonal Transformation, 434\u003c\/p\u003e \u003cp\u003e7.9 Copula-Based Models, 443\u003c\/p\u003e \u003cp\u003e7.10 Principal Volatility Components, 454\u003c\/p\u003e \u003cp\u003eExercises, 461\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A Review of Mathematics and Statistics 465\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Review of Vectors and Matrices, 465\u003c\/p\u003e \u003cp\u003eA.2 Least-Squares Estimation, 477\u003c\/p\u003e \u003cp\u003eA.3 Multivariate Normal Distributions, 478\u003c\/p\u003e \u003cp\u003eA.4 Multivariate Student-t Distribution, 479\u003c\/p\u003e \u003cp\u003eA.5 Wishart and Inverted Wishart Distributions, 480\u003c\/p\u003e \u003cp\u003eA.6 Vector and Matrix Differentials, 481\u003c\/p\u003e \u003cp\u003eIndex 489\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand 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