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"Since the publication of his first book, Analysis of Financial Time Series, Ruey Tsay has become one of the most influential and prominent experts on the topic of time series. Different from the traditional and oftentimes complex approach to multivariate (MV) time series, this sequel book emphasizes structural specification, which results in simplified parsimonious VARMA modeling and, hence, eases comprehension. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-world empirical research. The book utilizes the freely available R software package to explore complex data and illustrate related computation and analyses in a user-friendly way. An author-maintained website features additional data sets in R, Matlab and Stata scripts so readers can create their own simulations and test their comprehension of the presented techniques"--
List of contents
Preface xv
Acknowledgements xvii
1 Multivariate Linear Time Series 1
1.1 Introduction, 1
1.2 Some Basic Concepts, 5
1.3 Cross-Covariance and Correlation Matrices, 8
1.4 Sample CCM, 9
1.5 Testing Zero Cross-Correlations, 12
1.6 Forecasting, 16
1.7 Model Representations, 18
1.8 Outline of the Book, 22
1.9 Software, 23
Exercises, 23
2 Stationary Vector Autoregressive Time Series 27
2.1 Introduction, 27
2.2 VAR(1) Models, 28
2.3 VAR(2) Models, 37
2.4 VAR(p) Models, 41
2.5 Estimation, 44
2.6 Order Selection, 61
2.7 Model Checking, 66
2.8 Linear Constraints, 80
2.9 Forecasting, 82
2.10 Impulse Response Functions, 89
2.11 Forecast Error Variance Decomposition, 96
2.12 Proofs, 98
Exercises, 100
3 Vector Autoregressive Moving-Average Time Series 105
3.1 Vector MA Models, 106
3.2 Specifying VMA Order, 112
3.3 Estimation of VMA Models, 113
3.4 Forecasting of VMA Models, 126
3.5 VARMA Models, 127
3.6 Implications of VARMA Models, 139
3.7 Linear Transforms of VARMA Processes, 141
3.8 Temporal Aggregation of VARMA Processes, 144
3.9 Likelihood Function of a VARMA Model, 146
3.10 Innovations Approach to Exact Likelihood Function, 155
3.11 Asymptotic Distribution of Maximum Likelihood Estimates, 160
3.12 Model Checking of Fitted VARMA Models, 163
3.13 Forecasting of VARMA Models, 164
3.14 Tentative Order Identification, 166
3.15 Empirical Analysis of VARMA Models, 176
3.16 Appendix, 192
Exercises, 194
4 Structural Specification of VARMA Models 199
4.1 The Kronecker Index Approach, 200
4.2 The Scalar Component Approach, 212
4.3 Statistics for Order Specification, 220
4.4 Finding Kronecker Indices, 222
4.5 Finding Scalar Component Models, 226
4.6 Estimation, 237
4.7 An Example, 245
4.8 Appendix: Canonical Correlation Analysis, 259
Exercises, 262
5 Unit-Root Nonstationary Processes 265
5.1 Univariate Unit-Root Processes, 266
5.2 Multivariate Unit-Root Processes, 279
5.3 Spurious Regressions, 290
5.4 Multivariate Exponential Smoothing, 291
5.5 Cointegration, 294
5.6 An Error-Correction Form, 297
5.7 Implications of Cointegrating Vectors, 300
5.8 Parameterization of Cointegrating Vectors, 302
5.9 Cointegration Tests, 303
5.10 Estimation of Error-Correction Models, 313
5.11 Applications, 319
5.12 Discussion, 326
5.13 Appendix, 327
Exercises, 328
6 Factor Models and Selected Topics 333
6.1 Seasonal Models, 333
6.2 Principal Component Analysis, 341
6.3 Use of Exogenous Variables, 345
6.4 Missing Values, 357
6.5 Factor Models, 364
6.6 Classification and Clustering Analysis, 386
Exercises, 394
7 Multivariate Volatility Models 399
7.1 Testing Conditional Heteroscedasticity, 401
7.2 Estimation of Multivariate Volatility Models, 407
7.3 Diagnostic Checks of Volatility Models, 409
7.4 Exponentially Weighted Moving Average, 414
7.5 BEKK Models, 417
7.6 Cholesky Decomposition and Volatility Modeling, 420
7.7 Dynamic Conditional Correlation Models, 428
7.8 Orthog
