Analysis of integrated and cointegrated time series with R / Bernhard Pfaff.

Format:

Book

Author/Creator:

Pfaff, Bernhard.

Series:

Use R!

Language:

English

Subjects (All):

Time-series analysis--Computer programs.

Time-series analysis.

R (Computer program language).

Physical Description:

xx, 188 pages : illustrations ; 24 cm.

Edition:

Second edition.

Place of Publication:

New York : Springer, [2008]

Summary:

The analysis of integrated and cointegrated 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 cointegration 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 autoregressive, structural vector autoregressive, 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.

Contents:

Part I Theoretical Concepts

1 Univariate Analysis of Stationary Time Series 3

1.1 Characteristics of Time Series 3

1.2 AR(p) Time Series Process 6

1.3 MA(q) Time Series Process 10

1.4 ARMA(p, q) Time Series Process 14

2 Multivariate Analysis of Stationary Time Series 23

2.2 Vector Autoregressive Models 23

2.2.1 Specification, Assumptions, and Estimation 23

2.2.2 Diagnostic Tests 28

2.2.3 Causality Analysis 34

2.2.4 Forecasting 36

2.2.5 Impulse Response Functions 37

2.2.6 Forecast Error Variance Decomposition 41

2.3 Structural Vector Autoregressive Models 43

2.3.1 Specification and Assumptions 43

2.3.2 Estimation 44

2.3.3 Impulse Response Functions 47

2.3.4 Forecast Error Variance Decomposition 48

3 Non-stationary Time Series 53

3.1 Trend- versus Difference-Stationary Series 53

3.2 Unit Root Processes 55

3.3 Long-Memory Processes 62

4 Cointegration 73

4.1 Spurious Regression 73

4.2 Concept of Cointegration and Error-Correction Models 75

4.3 Systems of Cointegrated Variables 78

Part II Unit Root Tests

5 Testing for the Order of Integration 91

5.1 Dickey-Fuller Test 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

6 Further Considerations 107

6.1 Stable Autoregressive Processes with Structural Breaks 107

6.2 Seasonal Unit Roots 112

Part III Cointegration

7 Single-Equation Methods 121

7.1 Engle-Granger Two-Step Procedure 121

7.2 Phillips-Ouliaris Method 123

8 Multiple-Equation Methods 129

8.1 The Vector Error-Correction Model 129

8.1.1 Specification and Assumptions 129

8.1.2 Determining the Cointegration Rank 130

8.1.3 Testing for Weak Exogenity 134

8.1.4 Testing Restrictions on [beta] 136

8.2 VECM and Structural Shift 143

8.3 The Structural Vector Error-Correction Model 145

9.1 Time Series Data 161

9.2 Technicalities 162

9.3 CRAN Packages Used 163

10 Abbreviations, Nomenclature, and Symbols 165.

Notes:

Includes bibliographical references (pages [169]-175) and indexes.

ISBN:

0387759662

9780387759661

OCLC:

233263153

Publisher Number:

99954820901