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Figures=xii
Tables=xiii
Preface=xvii
Part I. Introduction and basic concepts=1
1. Introduction=3
References=6
2. Basic concepts=8
2.1. Stochastic processes=8
2.2. Some commonly used stationary models=11
2.3. Box-Jenkins methods=17
2.4. Integrated variables and cointegration=20
2.5. Spurious regression=28
2.6. Deterministic trend and stochastic trend=29
2.7. Detrending methods=32
2.8. VAR, ECM, and ADL=34
2.9. Unit root tests=37
2.10. Cointegration tests and ECM=39
2.11. Summary=41
References=42
Part II. Unit roots and cointegration=45
3. Unit roots=47
3.1. Introduction=47
3.2. Unit roots and Wiener processes=49
3.3. Unit root tests without a deterministic trend=60
3.4. DF test with a linear deterministic trend=65
3.5. Specification of deterministic trends=72
3.6. Unit root tests for a wide class of errors=74
3.7. Sargan-Bhargava and Bhargava tests=82
3.8. Variance ratio tests=86
3.9. Tests for TSP versus DSP=87
3.10. Forecasting from TS versus DS models=89
3.11. Summary and conclusions=92
References=92
4. Issues in unit root testing=98
4.1. Introduction=98
4.2. Size distortion and low power of unit root tests=100
4.3. Solutions to the problems of size and power=103
4.4. Problem of overdifferencing: MA roots=116
4.5. Tests with stationarity as =120
4.6. Confirmatory analysis=126
4.7. Frequency of observations and power of unit root tests=129
4.8. Other types of nonstationarity=131
4.9. Panel data unit root tests=133
4.10. Uncertain unit roots and the pre-testing problem=139
4.11. Other unit root tests=140
4.12. Median-unbiased estimation=141
4.13. Summary and conclusions=145
References=146
5. Estimation of cointegrated systems=155
5.1. Introduction=155
5.2. A general CI system=155
5.3. A two-variable model: Engle-Granger methods=156
5.4. A triangular system=160
5.5. System estimation methods=165
5.6. The identification problem=173
5.7. Finite sample evidence=175
5.8. Forecasting in cointegrated systems=184
5.9. Miscellaneous other problems=187
5.10. Summary and conclusions=191
References=191
6. Tests for cointegration=198
6.1. Introduction=198
6.2. Single equation methods: residual-based tests=198
6.3. Single equation methods: ECM tests=203
6.4. Tests with cointegration as =205
6.5. Multiple equation methods=211
6.6. Cointegration tests based on LCCA=222
6.7. Other tests for cointegration=226
6.8. Miscellaneous other problems=228
6.9. Of what use are cointegration tests?=233
6.10. Conclusions=241
References=242
7. Econometric modeling with integrated regressors=249
7.1. I(1) regressors not cointegrated=249
7.2. I(1) regressors cointegrated=250
7.3. Unbalanced equations=251
7.4. Lagged dependent variables: the ARDL model=252
7.5. Uncertain unit roots=254
7.6. Uncertain unit roots and cointegration=256
7.7. Summary and conclusions=258
References=258
Part III. Extensions of the basic model=261
8. The Bayesian analysis of stochastic trends=263
8.1. Introduction to Bayesian inference=264
8.2. The posterior distribution of an autoregressive parameter=266
8.3. Bayesian inference on the Nelson-Plosser data=268
8.4. The debate on the appropriate prior=271
8.5. Classical tests versus Bayesian tests=277
8.6. Priors and time units of measurement=277
8.7. On testing point hypotheses=278
8.8. Further comments on prior distributions=284
8.9. Bayesian inference on cointegrated systems=287
8.10. Bayesian long-run prediction=290
8.11. Conclusion=291
References=292
9. Fractional unit roots and fractional cointegration=296
9.1. Some definitions=296
9.2. Unit root tests against fractional alternatives=298
9.3. Estimation of ARFIMA models=300
9.4. Estimation of fractionally cointegrated models=302
9.5. Empirical relevance of fractional unit roots=303
9.6. Summary and conclusions=305
References=306
10. Small sample inference: bootstrap methods=309
10.1. Introduction=309
10.2. A review of the bootstrap approach=309
10.3. The AR(1) model=322
10.4. Bootstrapping unit root tests=325
10.5. The moving block bootstrap and extensions=328
10.6. Issues in bootstrapping cointegrating regressions=332
10.7. Miscellaneous other applications=335
10.8. Conclusions=336
References=336
11. Cointegrated systems with I(2) variables=342
11.1. Determination of the order of differencing=342
11.2. Cointegration analysis with I(2) and I(1) variables=348
11.3. Empirical applications=355
11.4. Summary and conclusions=358
References=359
12. Seasonal unit roots and seasonal cointegration=362
12.1. Effect of seasonal adjustment=364
12.2. Seasonal integration=365
12.3. Tests for seasonal unit roots=366
12.4. The unobserved component model=371
12.5. Seasonal cointegration=375
12.6. Estimation of seasonally cointegrated systems=376
12.7. Empirical evidence=378
12.8. Periodic autoregression and periodic integration=379
12.9. Periodic cointegration and seasonal cointegration=381
12.10. Time aggregation and systematic sampling=381
12.11. Conclusion=382
References=383
Part IV. Structural change=387
13. Structural change, unit roots, and cointegration=389
13.1. Tests for structural change=390
13.2. Tests with known break points=390
13.3. Tests with unknown break points=391
13.4. A summary assessment=398
13.5. Tests for unit roots under structural change=399
13.6. The Bayesian approach=402
13.7. A summary assessment of the empirical work=407
13.8. Effect of structural change on cointegration tests=410
13.9. Tests for structural change in cointegrated relationships=411
13.10. Miscellaneous other issues=414
13.11. Practical conclusions=416
References=418
14. Outliers and unit roots=425
14.1. Introduction=425
14.2. Different types of outliers in time series models=425
14.3. Effects of outliers on unit root tests=428
14.4. Outlier detection=437
14.5. Robust unit root tests=440
14.6. Robust estimation of cointegrating regressions=445
14.7. Outliers and seasonal unit roots=448
14.8. Conclusions=448
References=449
15. Regime switching models and structural time series models=454
15.1. The switching regression model=454
15.2. The Markov switching regression model=455
15.3. The Hamilton model=457
15.4. On the usefulness of the MSR model=460
15.5. Extensions of the MSR model=463
15.6. Gradual regime switching models=466
15.7. A model with parameters following a random walk=469
15.8. A general state-space model=470
15.9. Derivation of the Kalman filter=472
15.10. Harvey's structural time series model (1989)=475
15.11. Further comments on structural time series models=477
15.12. Summary and conclusions=479
References=479
16. Future directions=486
References=488
Appendix 1: A brief guide to asymptotic theory=490
Author index=492
Subject index=500
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출판사 책소개
Time series analysis has undergone many changes in recent years with the advent of unit roots and cointegration. Maddala and Kim present a comprehensive review of these important developments and examine structural change. The volume provides an analysis of unit root tests, problems with unit root testing, estimation of cointegration systems, cointegration tests, and econometric estimation with integrated regressors. The authors also present the Bayesian approach to these problems and bootstrap methods for small-sample inference. The chapters on structural change discuss the problems of unit root tests and cointegration under structural change, outliers and robust methods, the Markov-switching model and Harvey's structural time series model. Unit Roots, Cointegration and Structural Change is a major contribution to Themes in Modern Econometrics, of interest both to specialists and graduate and upper-undergraduate students.
A comprehensive review of unit roots, cointegration and structural change from a best-selling author.
A comprehensive review of unit roots, cointegration and structural change from a best-selling author.
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