Unit Roots, Cointegration, and Structural Change

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Cambridge University Press, 1998 - Business & Economics - 505 pages
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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.
 

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Contents

Basic concepts
8
22 Some commonly used stationary models
11
23 BoxJenkins methods
17
24 Integrated variables and cointegration
20
25 Spurious regression
28
26 Deterministic trend and stochastic trend
29
27 Detrending methods
32
28 VAR ECM and ADL
34
89 Bayesian inference on cointegrated systems
287
810 Bayesian longrun prediction
290
811 Conclusion
291
References
292
Fractional unit roots and fractional cointegration
296
92 Unit root tests against fractional alternatives
298
93 Estimation of ARFIMA models
300
94 Estimation of fractionally cointegrated models
302

29 Unit root tests
37
210 Cointegration tests and ECM
39
211 Summary
41
References
42
Unit roots and cointegration
45
Unit roots
47
32 Unit roots and Wiener processes
49
33 Unit root tests without a deterministic trend
60
34 DF test with a linear deterministic trend
65
35 Specification of deterministic trends
72
36 Unit root tests for a wide class of errors
74
37 SarganBhargava and Bhargava tests
82
38 Variance ratio tests
86
39 Tests for TSP versus DSP
87
310 Forecasting from TS versus DS models
89
311 Summary and conclusions
92
Issues in unit root testing
98
42 Size distortion and low power of unit root tests
100
43 Solutions to the problems of size and power
103
MA roots
116
45 Tests with stationarity as null
120
46 Confirmatory analysis
126
47 Frequency of observations and power of unit root tests
129
48 Other types of nonstationarity
131
49 Panel data unit root tests
133
410 Uncertain unit roots and the pretesting problem
139
411 Other unit root tests
140
412 Medianunbiased estimation
141
413 Summary and conclusions
145
References
146
Estimation of cointegrated systems
155
EngleGranger methods
156
54 A triangular system
160
55 System estimation methods
165
56 The identification problem
173
57 Finite sample evidence
175
58 Forecasting in cointegrated systems
184
59 Miscellaneous other problems
187
510 Summary and conclusions
191
Tests for cointegration
198
ECM tests
203
64 Tests with cointegration as null
205
65 Multiple equation methods
211
66 Cointegration tests based on LCCA
222
67 Other tests for cointegration
226
68 Miscellaneous other problems
228
69 Of what use are cointegration tests?
233
610 Conclusions
241
References
242
Econometric modeling with integrated regressors
249
72 I1 regressors cointegrated
250
73 Unbalanced equations
251
the ARDL model
252
75 Uncertain unit roots
254
76 Uncertain unit roots and cointegration
256
77 Summary and conclusions
258
Extensions of the basic model
261
The Bayesian analysis of stochastic trends
263
81 Introduction to Bayesian inference
264
82 The posterior distribution of an autoregressive parameter
266
83 Bayesian inference on the NelsonPlosser data
268
84 The debate on the appropriate prior
271
85 Classical tests versus Bayesian tests
277
87 On testing point null hypotheses
278
88 Further comments on prior distributions
284
95 Empirical relevance of fractional unit roots
303
96 Summary and conclusions
305
References
306
Small sample inference bootstrap methods
309
103 The AR1 model
322
104 Bootstrapping unit root tests
325
105 The moving block bootstrap and extensions
328
106 Issues in bootstrapping cointegrating regressions
332
107 Miscellaneous other applications
335
108 Conclusions
336
Cointegrated systems with I2 variables
342
112 Cointegration analysis with I2 and I1 variables
348
113 Empirical applications
355
114 Summary and conclusions
358
References
359
Seasonal unit roots and seasonal cointegration
362
121 Effect of seasonal adjustment
364
122 Seasonal integration
365
123 Tests for seasonal unit roots
366
124 The unobserved component model
371
125 Seasonal cointegration
375
126 Estimation of seasonally cointegrated systems
376
127 Empirical evidence
378
128 Periodic autoregression and periodic integration
379
129 Periodic cointegration and seasonal cointegration
381
1211 Conclusion
382
References
383
Structural change
387
Structural change unit roots and cointegration
389
131 Tests for structural change
390
133 Tests with unknown break points
391
134 A summary assessment
398
135 Tests for unit roots under structural change
399
136 The Bayesian approach
402
137 A summary assessment of the empirical work
407
138 Effect of structural change on cointegration tests
410
139 Tests for structural change in cointegrated relationships
411
1310 Miscellaneous other issues
414
1311 Practical conclusions
416
References
418
Outliers and unit roots
425
143 Effects of outliers on unit root tests
428
144 Outlier detection
437
145 Robust unit root tests
440
146 Robust estimation of cointegrating regressions
445
147 Outliers and seasonal unit roots
448
References
449
Regime switching models and structural time series models
454
152 The Markov switching regression model
455
153 The Hamilton model
457
154 On the usefulness of the MSR model
460
155 Extensions of the MSR model
463
156 Gradual regime switching models
466
157 A model with parameters following a random walk
469
158 A general statespace model
470
159 Derivation of the Kalman filter
472
1510 Harveys structural time series model 1989
475
1511 Further comments on structural time series models
477
1512 Summary and conclusions
479
Future directions
486
References
488
A brief guide to asymptotic theory
490
Author index
492
Subject index
500
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About the author (1998)

G. S. Maddala is a University Eminent Professor of Economics at Ohio State University. He attended Andhra University, Bombay University and the University of Chicago. Maddala taught at the University of Florida, the University of Rochester, Stanford University, and Cornell University. He contributed to The Handbook of Econometrics and authored Introduction of Econometrics.

Kim is Professor of Economics at Sung Kyun Kwan University, Seoul, Korea.

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