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Time Series with Long Memory [Kõva köide]

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  • Formaat: Hardback, 392 pages, kõrgus x laius: 234x156 mm, kaal: 682 g, numerous tables and figures
  • Sari: Advanced Texts in Econometrics
  • Ilmumisaeg: 28-Aug-2003
  • Kirjastus: Oxford University Press Inc
  • ISBN-10: 0199257299
  • ISBN-13: 9780199257294
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Time Series with Long MemorySuurem pilt
  • Formaat: Hardback, 392 pages, kõrgus x laius: 234x156 mm, kaal: 682 g, numerous tables and figures
  • Sari: Advanced Texts in Econometrics
  • Ilmumisaeg: 28-Aug-2003
  • Kirjastus: Oxford University Press Inc
  • ISBN-10: 0199257299
  • ISBN-13: 9780199257294
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780199257294.html
Märksõnad:
Time Series with Long Memory comprises a collection on time series analysis. Long memory time series are characterized by a strong dependence between distant events. Various methods and their theoretical properties are discussed with empirical applications. The methods constitute a very
flexible approach to analyzing time series data arising in economics, finance and other fields.
Introduction 1(2)
References 3(1)
1. Long-memory Time Series 4(29)
P.M. Robinson
1.1 Introduction
4(2)
1.2 Parametric Modelling and Inference
6(6)
1.3 Semiparametric Modelling and Inference
12(4)
1.4 Stochastic Volatility Models
16(3)
1.5 Nonstationary Long Memory
19(3)
1.6 Inference on Regression and Cointegration Models
22(3)
References
25(8)
2. On Large-sample Estimation for the Mean of a Stationary Random Sequence 33(16)
Rolf K. Adenstedt
2.1 Introduction
33(2)
2.2 Uniqueness of the BLUE
35(1)
2.3 Preliminaries, Notation, and Definitions
35(1)
2.4 Asymptotic Behaviour of the Minimum Variance
36(2)
2.5 Application to Certain Spectral Densities
38(3)
2.6 The Optimal Polynomials for fα(λ)
41(1)
2.7 Overestimating the Zero Order
42(2)
2.8 Underestimating the Zero Order
44(3)
2.9 Remarks
47(1)
References
47(2)
3. An Introduction to Long-memory Time Series Models and Fractional Differencing 49(16)
C.W.J. Granger and Roselyne Joyeux
3.1 On Differencing Time Series
49(1)
3.2 Time Series Properties
50(6)
3.3 Forecasting and Estimation of d
56(3)
3.4 Practical Experience
59(4)
Appendix 3.1 The d = 0 Case
63(1)
References
64(1)
4. Large-sample Properties of Parameter Estimates for Strongly Dependent Stationary Gaussian Time Series 65(17)
Robert Fox and Murad S. Tagqu
4.1 Introduction
65(3)
4.2 Statements of the Theorems
68(3)
4.3 Proofs of Theorems 1 and 2
71(7)
4.4 Proof of Theorem 3
78(2)
References
80(2)
5. Long-term Memory in Stock Market Prices 82(37)
Andrew W. Lo
5.1 Introduction
82(2)
5.2 Long-range Versus Short-range Dependence
84(4)
5.3 The Rescaled Range Statistic
88(9)
5.4 R/S Analysis for Stock Market Returns
97(5)
5.5 Size and Power
102(7)
5.6 Conclusion
109(1)
Appendix 5.1
109(2)
Notes
111(3)
References
114(5)
6. The Estimation and Application of Long-memory Time Series Models 119(19)
John Geweke and Susan Porter-Hudak
6.1 Introduction
119(2)
6.2 The Equivalence of General Fractional Gaussian Noise and General Integrated Series
121(2)
6.3 A Simple Estimation Procedure for General Integrated Series
123(3)
6.4 Simulation Results
126(4)
6.5 Forecasting with Long-memory Models
130(6)
References
136(2)
7. Gaussian Semiparametric Estimation of Long-range Dependence 138(37)
P.M. Robinson
7.1 Introduction
138(2)
7.2 Semiparametric Gaussian Estimate
140(1)
7.3 Consistency of Estimates
141(8)
7.4 Asymptotic Normality of Estimates
149(12)
7.5 Technical Lemmas
161(2)
7.6 Numerical Work
163(11)
References
174(1)
8. Testing for Strong Serial Correlation and Dynamic Conditional Heteroskedasticity in Multiple Regression 175(16)
P.M. Robinson
8.1 Introduction
175(2)
8.2 Testing for Serial Correlation
177(4)
8.3 Testing for Dynamic Conditional Heteroskedasticity
181(4)
8.4 Simultaneous Testing for Serial Correlation and Dynamic Conditional Heteroskedasticity
185(2)
8.5 Long-memory Examples
187(2)
References
189(2)
9. The Detection and Estimation of Long Memory in Stochastic Volatility 191(23)
F. Jay Breidt, Nuno Grato, and Pedro de Lima
9.1 Introduction
191(1)
9.2 Models of Persistence in Volatility
192(4)
9.3 Evidence of Long Memory in Volatility
196(6)
9.4 Estimating an LMSV Model
202(6)
9.5 Conclusions
208(1)
Appendix 9.1 Proof of Strong Consistency for Spectral-likelihood Estimators
209(2)
Appendix 9.2 Autocovariance Function of Log Squares under EGARCH
211(1)
Notes
211(1)
References
212(2)
10. Efficient Tests of Nonstationary Hypotheses 214(37)
P.M. Robinson
10.1 Introduction
214(3)
10.2 Null and Alternative Hypotheses
217(2)
10.3 Score Test Under White Noise
219(1)
10.4 Distribution Theory Under White Noise
220(1)
10.5 Score Test Under Weak Autocorrelation
221(2)
10.6 Distribution Theory Under Weak Autocorrelation
223(1)
10.7 Empirical Illustration
224(1)
10.8 Finite-Sample Performance and Comparison
225(12)
10.9 Final Comments
237(1)
Appendix 10.1 Derivation of Score Statistic R
238(1)
Appendix 10.2 Proof of Theorem 1
239(4)
Appendix 10.3 Proof of Theorem 2
243(2)
Appendix 10.4 Proof of Theorem 3
245(4)
References
249(2)
11. Estimation of the Memory Parameter for Nonstationary or Noninvertible Fractionally Integrated Processes 251(27)
Clifford M. Hurvich and Bonnie K. Ray
11.1 Introduction
251(3)
11.2 Theoretical Results
254(6)
11.3 Empirical Results
260(9)
Appendix 11.1
269(7)
References
276(2)
12. Limit Theorems for Regressions with Unequal and Dependent Errors 278(27)
Friedjielm Eicken
12.1 Introduction (Notations)
278(1)
12.2 Independent Nonidentically Distributed Errors
279(6)
12.3 Dependent Errors (Regression for Time Series)
285(19)
References
304(1)
13. Time Series Regression with Long-range Dependence 305(29)
P.M. Robinson and F.J. Hidalgo
13.1 Introduction
305(4)
13.2 Central Limit Theorems
309(4)
13.3 Proof of Theorem 1
313(10)
13.4 Feasible Generalized Least Squares
323(2)
13.5 Nonlinear Regression Models
325(4)
13.6 Monte Carlo Simulations
329(3)
References
332(2)
14. Semiparametric Frequency Domain Analysis of Fractional Cointegration 334(41)
P.M. Robinson and D. Marinucci
14.1 Introduction
334(3)
14.2 Frequency Domain Least Squares
337(2)
14.3 Stationary Cointegration
339(2)
14.4 The Averaged Periodogram in Nonstationary Environments
341(2)
14.5 Nonstationary Fractional Cointegration
343(7)
14.6 Monte Carlo Evidence
350(1)
14.7 Empirical Examples
351(9)
14.8 Final Comments
360(1)
Appendix 14.1
361(10)
References
371(4)
Index 375