Introduction to Modern Time Series Analysis (eBook)

Preface 5
Contents 7
1 Introduction and Basics 10
1.1 The Historical Development of Time Series Analysis 11
1.2 Graphical Representations of Economic Time Series 14
1.3 Ergodicity and Stationarity Stationarity Stationarity 21
1.4 The Wold Decomposition 30
References 31
2 Univariate Stationary Processes 35
2.1 Autoregressive Processes 35
2.2 Moving Average Processes 65
2.3 Mixed Processes 75
2.4 Forecasting 83
2.5 The Relation between Econometric Models and ARMA Processes 95
References 96
3 Granger Causality 100
3.1 The Definition of Granger Causality 102
3.2 Characterisations of Causal Relations in Bivariate Models 104
3.3 Causality Tests 109
3.4 Applying Causality Tests in a Multivariate Setting 121
3.5 Concluding Remarks 125
References 127
4 Vector Autoregressive Processes 131
4.1 Representation of the System 133
4.2 Granger Causality 142
4.3 Impulse Response Analysis 144
4.4 Variance Decomposition 150
4.5 Concluding Remarks 155
References 156
5 Nonstationary Processes 158
5.1 Forms of Nonstationarity 158
5.2 Trend Elimination 164
5.3 Unit Root Tests 168
5.4 Decomposition of Time Series 185
5.5 Further Developments 192
5.6 Deterministic versus Stochastic Trends in Economic Time Series 196
References 199
6 Cointegration 204
6.1 Definition and Properties of Cointegrated Processes 208
6.2 Cointegration in Single Equation Models: Representation, Estimation and Testing 210
6.3 Cointegration in Vector Autoregressive Models 223
6.4 Cointegration and Economic Theory 239
References 240
7 Autoregressive Conditional Heteroskedasticity 245
7.1 ARCH Models 249
7.2 Generalised ARCH Models 256
7.3 Estimation and Testing 263
7.4 ARCH/GARCH Models as Instruments of Financial Market Analysis 265
References 267
Index of Names and Authors 270
Subject Index 274