Analysis of Integrated and Cointegrated Time Series with R

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Analysis of Integrated and Cointegrated Time Series with R.rar (1.37 MB, 下载次数: 5, 售价: 5 金钱)

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Contents

Preface to the Second Edition ................................. vii
Preface ........................................................ ix
List of Tables .................................................. xv
List of Figures .................................................xvii
List of R Code ................................................. xix
Part I Theoretical Concepts
1 Univariate Analysis of Stationary Time Series ............. 3
1.1 CharacteristicsofTimeSeries............................. 3
1.2 AR(p)TimeSeriesProcess ............................... 6
1.3 MA(q)TimeSeriesProcess............................... 10
1.4 ARMA(p, q)TimeSeriesProcess.......................... 14
Summary ................................................... 20
Exercises ................................................... 21
2 Multivariate Analysis of Stationary Time Series ........... 23
2.1 Overview............................................... 23
2.2 VectorAutoregressiveModels............................. 23
2.2.1 Specification, Assumptions, and Estimation........... 23
2.2.2 DiagnosticTests .................................. 28
2.2.3 CausalityAnalysis................................. 34
2.2.4 Forecasting....................................... 36
2.2.5 ImpulseResponseFunctions ....................... 37
2.2.6 ForecastErrorVarianceDecomposition .............. 41
2.3 StructuralVectorAutoregressiveModels ................... 43
2.3.1 SpecificationandAssumptions...................... 43
xii Contents
2.3.2 Estimation ....................................... 44
2.3.3 ImpulseResponseFunctions ........................ 47
2.3.4 ForecastErrorVarianceDecomposition .............. 48
Summary ................................................... 49
Exercises ................................................... 50
3 Non-stationary Time Series................................ 53
3.1 Trend- versus Di?erence-Stationary Series .................. 53
3.2 UnitRootProcesses ..................................... 55
3.3 Long-MemoryProcesses.................................. 62
Summary ................................................... 70
Exercises ................................................... 71
4 Cointegration.............................................. 73
4.1 SpuriousRegression ..................................... 73
4.2 Concept of Cointegration and Error-Correction Models....... 75
4.3 SystemsofCointegratedVariables......................... 78
Summary ................................................... 86
Exercises ................................................... 86
Part II Unit Root Tests
5 Testing for the Order of Integration ....................... 91
5.1 Dickey-FullerTest....................................... 91
5.2 Phillips-Perron Test ..................................... 94
5.3 Elliott-Rothenberg-Stock Test............................. 98
5.4 Schmidt-Phillips Test .................................... 100
5.5 Kwiatkowski-Phillips-Schmidt-Shin Test .................... 103
Summary ...................................................104
Exercises ...................................................105
6 Further Considerations ....................................107
6.1 Stable Autoregressive Processes with Structural Breaks ...... 107
6.2 SeasonalUnitRoots .....................................112
Summary ...................................................118
Exercises ...................................................118
Part III Cointegration
7 Single-Equation Methods ..................................121
7.1 Engle-GrangerTwo-StepProcedure........................121
7.2 Phillips-Ouliaris Method ................................. 123
Summary ...................................................126
Exercises ...................................................127
Contents xiii
8 Multiple-Equation Methods ...............................129
8.1 TheVectorError-CorrectionModel .......................129
8.1.1 SpecificationandAssumptions......................129
8.1.2 DeterminingtheCointegrationRank.................130
8.1.3 TestingforWeakExogenity ........................134
8.1.4 Testing Restrictions on ¦Â...........................136
8.2 VECMandStructuralShift...............................143
8.3 The Structural Vector Error-Correction Model .............. 145
Summary ...................................................158
Exercises ...................................................158
9 Appendix..................................................161
9.1 TimeSeriesData........................................161
9.2 Technicalities ...........................................162
9.3 CRANPackagesUsed ...................................163
10 Abbreviations, Nomenclature, and Symbols................165
References.....................................................169
Name Index ...................................................177
Function Index ................................................181
Subject Index .................................................185

genechip

Product Description:
The analysis of integrated and co-integrated time series can be considered as the main methodology employed in applied econometrics. This book not only introduces the reader to this topic but enables him to conduct the various unit root tests and co-integration methods on his own by utilizing the free statistical programming environment R. The book encompasses seasonal unit roots, fractional integration, coping with structural breaks, and multivariate time series models. The book is enriched by numerous programming examples to artificial and real data so that it is ideally suited as an accompanying text book to computer lab classes.
The second edition adds a discussion of vector auto-regressive, structural vector auto-regressive, and structural vector error-correction models. To analyze the interactions between the investigated variables, further impulse response function and forecast error variance decompositions are introduced as well as forecasting. The author explains how these model types relate to each other.

bioyulj

很好的资料，谢谢！