Modelling nonlinear economic time series:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Oxford [u.a.]
Oxford Univ. Press
2010
|
| Ausgabe: | 1. publ. |
| Schriftenreihe: | Advanced texts in econometrics
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXVIII, 557 S. graph. Darst. |
| ISBN: | 9780199587155 9780199587148 |
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| 245 | 1 | 0 | |a Modelling nonlinear economic time series |c by Timo Teräsvirta ; Dag Tjøstheim and Clive W. J. Granger |
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| 264 | 1 | |a Oxford [u.a.] |b Oxford Univ. Press |c 2010 | |
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Datensatz im Suchindex
| _version_ | 1804143378764398592 |
|---|---|
| adam_text | Titel: Modelling nonlinear economic time series
Autor: Teräsvirta, Timo
Jahr: 2010
Contents
List of Figures xx
List of Tables xxiii
Acronyms and abbreviations xxvi
1 Concepts, models, and definitions 1
1.1 Defining nonlinearity 1
1.2 Where does nonlinearity come from? 2
1.3 Stationarity and nonstationarity 3
1.4 Invertibility 6
1.5 Trends 7
1.6 Seasonality 10
1.7 Conditional distributions 10
1.8 Wold s representation and Volterra expansion 11
1.9 Additive models 12
1.10 Spectral analysis 13
1.11 Chaos 14
2 Nonlinear models in economic theory 16
2.1 Disequilibrium models 16
2.2 Labour market models 18
2.2.1 Theory 18
2.2.2 Practice 20
2.3 Exchange rates in a target zone 22
2.3.1 Theory 22
2.3.2 Practice 24
2.4 Production theory 25
3 Parametric nonlinear models 28
3.1 General considerations 28
3.2 Switching regression models 32
3.2.1 Standard switching regression model 32
3.2.2 Vector threshold autoregressive model 34
3.3 Markov-switching regression models 35
3.4 Smooth transition regression models 37
3.4.1 Standard smooth transition regression model 37
3.4.2 Additive, multiple, and time-varying STR models 40
3.4.3 Vector smooth transition autoregressive model 41
3.5 Polynomial models 41
3.6 Artificial neural network models 43
3.7 Min-max models 45
3.8 Nonlinear moving average models 46
3.9 Bilinear models 47
3.10 Time-varying parameters and state space models 48
3.11 Random coefficient and volatility models 50
4 The nonparametric approach 52
4.1 Introduction 52
4.2 Autocovariance and spectrum 53
4.3 Density, conditional mean, and conditional variance 55
4.3.1 Non-Gaussian marginals 55
4.3.2 Conditional quantities 56
4.4 Dependence measures for nonlinear processes 57
4.4.1 Local measures of dependence 58
4.4.2 Global measures of dependence 60
4.4.3 Measures based on density and distribution functions 61
4.4.4 The copula 62
5 Testing linearity against parametric alternatives 65
5.1 Introduction 65
5.2 Consistent misspecification tests 66
5.3 Lagrange multiplier or score test 68
5.3.1 Standard case 68
5.3.2 Test in stages and a heteroskedasticity-robust version 70
5.3.3 Robustifying against conditional heteroskedasticity 71
5.4 Locally equivalent alternatives 72
5.5 Nonlinear model only identified under the alternative 73
5.5.1 Identification problem 73
5.5.2 General solution 74
5.5.3 Lagrange multiplier-type tests 77
5.5.4 Monte Carlo tests 80
5.5.5 Giving values to the nuisance parameters 82
5.6 Testing linearity against unspecified alternatives 83
5.6.1 Regression Specification Error Test 83
5.6.2 Tests based on expansions 84
5.7 Comparing parametric linearity tests using asymptotic
relative efficiency 85
5.7.1 Definition 85
5.7.2 An example 88
5.8 Which test to use? 90
6 Testing parameter constancy 92
6.1 General considerations 92
6.2 Generalizing the Chow test 93
6.2.1 Testing against a single break 93
6.2.2 Testing against multiple breaks 95
6.3 Lagrange multiplier type tests 97
6.3.1 Testing a stationary single-equation model 97
6.3.2 Testing a stationary vector autoregressive model 100
6.3.3 Testing a nonstationary vector autoregressive model 102
6.4 Tests based on recursive estimation of parameters 105
6.4.1 Cumulative sum tests 105
6.4.2 Moving sum tests 107
6.4.3 Fluctuation tests 108
6.4.4 Tests against stochastic parameters 109
6.4.5 Testing the constancy of cointegrating relationships 111
7 Nonparametric specification tests 113
7.1 Introduction 113
7.2 Nonparametric linearity tests 114
7.2.1 Nonparametric tests: the spectral domain 115
7.2.2 Testing linearity in the conditional mean
and conditional variance 116
7.2.3 Estimation 119
7.2.4 Asymptotic theory 120
7.2.5 Finite-sample properties and use of the asymptotics 121
7.2.6 A bootstrap approach to testing 122
7.3 Testing for specific functional forms 123
7.3.1 Tests based on residuals 124
7.3.2 Conditional mean and conditional variance testing 127
7.3.3 Continuous time 129
7.4 Selecting lags 129
7.5 Testing for additivity and interaction 133
7.5.1 Testing in additive models 133
7.5.2 A simulated example 136
7.6 Tests for partial linearity and semiparametric modelling 138
7.7 Tests of independence 140
7.7.1 Traditional tests 140
7.7.2 Rank correlation 141
7.7.3 Frequency based tests 143
7.7.4 BDStest 143
7.7.5 Distribution based tests of independence 145
7.7.6 Generalized spectrum and tests of independence 150
7.7.7 Density based tests of independence 153
7.7.8 Some examples of independence testing 158
8 Models of conditional heteroskedasticity 162
8.1 Autoregressive conditional heteroskedasticity 163
8.1.1 The ARCH model 163
8.2 The Generalized ARCH model 164
8.2.1 Why Generalized ARCH? 164
8.2.2 Families of univariate GARCH models 164
8.2.3 Nonlinear GARCH 167
8.2.4 Time-varying GARCH 169
8.2.5 Moment structure of first-order GARCH models 170
8.2.6 Moment structure of higher-order GARCH models 172
8.2.7 Integrated and fractionally Integrated GARCH 172
8.2.8 Stylized facts and the GARCH model 175
8.2.9 Building univariate GARCH models 178
8.3 Family of Exponential GARCH models 188
8.3.1 Moment structure of EGARCH models 189
8.3.2 Stylized facts and the EGARCH model 190
8.3.3 Building EGARCH models 191
8.4 The Autoregressive Stochastic Volatility model 196
8.4.1 Definition 196
8.4.2 Moment structure of ARSV models 197
8.4.3 Stylized facts and the stochastic volatility model 198
8.4.4 Estimation of ARSV models 198
8.4.5 Comparing the ARSV model with GARCH 199
8.5 GARCH-in-Mean model 199
8.6 Realized volatility 200
8.7 Multivariate GARCH models 202
8.7.1 General multivariate GARCH model 202
8.7.2 Link to random coefficient models 203
8.7.3 Constant Conditional Correlation GARCH 204
8.7.4 Testing the constant correlation assumption and the
Dynamic Conditional Correlation model 206
8.7.5 Other extensions to the CCC-GARCH model 209
8.7.6 The BEKK-GARCH model 211
8.7.7 Factor GARCH models 213
9 Time-varying parameters and state space models 219
9.1 Introduction 219
9.2 Linear state space models 221
9.3 Time-varying parameter models 223
9.4 Nonlinear state space models 224
9.4.1 Extended Kalman filter 225
9.4.2 Kitagawa s grid approximation 226
9.4.3 Monte Carlo methods 228
9.4.4 Particle filters 229
9.4.5 Approximating with a Gaussian density 231
9.5 Hidden Markov chains and regimes 235
9.5.1 Hidden Markov chains 235
9.5.2 Mixture models 238
9.6 Estimating parameters 242
9.6.1 Stationarity 242
9.6.2 Identification 245
9.6.3 Estimation in linear models 245
9.6.4 The nonlinear case 247
9.6.5 Estimation in hidden Markov and mixture models 250
10 Nonparametric models 252
10.1 Additive models 252
10.1.1 Estimation in purely additive models 255
10.1.2 Marginal integration 255
10.1.3 Backfitting and smoothed backfitting 257
10.1.4 Additive models with interactions 260
10.1.5 A simulated example 262
10.1.6 Nonparametric and additive estimation of the
conditional variance function 263
10.2 Some related models 269
10.2.1 Functional coefficient autoregressive models 269
10.2.2 Transformation of dependent variables and the ACE
algorithm 269
10.2.3 Regression trees, splines, and MARS 270
10.2.4 Quantile regression 270
10.3 Semiparametric models 272
10.3.1 Index models 273
10.3.2 Projection pursuit regression 274
10.3.3 Partially linear models 276
10.4 Robust and adaptive estimation 277
11 Nonlinear and nonstationary models 279
11.1 Long memory models 279
11.2 Linear unit root models 285
11.3 Vector autoregressive processes and linear cointegration 288
11.4 Nonlinear 1(1) processes 290
11.5 Nonlinear error correction models 293
11.6 Parametric nonlinear regression 297
11.7 Nonparametric estimation in a nonlinear cointegration type
framework 302
11.8 Stochastic unit root models 304
12 Algorithms for estimating parametric nonlinear models 307
12.1 Optimization without derivatives 308
12.1.1 Grid and line searches 308
12.1.2 Conjugate directions 309
12.1.3 Simulated annealing 311
12.1.4 Evolutionary algorithms 314
12.2 Methods requiring derivatives 317
12.2.1 Gradient methods 317
12.2.2 Variable metric methods 322
12.3 Other methods 324
12.3.1 EM algorithm 324
12.3.2 Sequential estimation for neural networks 326
13 Basic nonparametric estimates 329
13.1 Density estimation 329
13.1.1 Kernel estimation 329
13.1.2 Bias and variance reduction 331
13.1.3 Choice of bandwidth 333
13.1.4 Variable bandwidth and nearest neighbour estimation 333
13.1.5 Multivariate density estimation 334
13.2 Nonparametric regression estimation 334
13.2.1 Kernel regression estimation 335
13.2.2 Local polynomial estimation 337
13.2.3 Bias, convolution, and higher-order kernels 338
13.2.4 Nearest neighbour estimation 339
13.2.5 Splines and MARS 341
13.2.6 Series expansion 341
13.2.7 Choice of bandwidth for nonparametric regression 342
14 Forecasting from nonlinear models 344
14.1 Introduction 344
14.2 Conditional mean forecasts from parametric models 345
14.2.1 Analytical point forecasts 345
14.2.2 Numerical techniques in forecasting 347
14.3 Forecasting with nonparametric models 351
14.4 Forecast accuracy 354
14.5 The usefulness of forecasts from nonlinear models 356
14.6 Forecasting volatility 361
14.7 Overview of forecasting from nonlinear models 362
15 Nonlinear impulse responses 364
15.1 Generalized impulse response function 364
15.2 Graphical representation 367
16 Building nonlinear models 370
16.1 General considerations 370
16.2 Nonparametric and semiparametric models 371
16.3 Building smooth transition regression models 375
16.3.1 The three stages of the modelling procedure 375
16.3.2 Specification 376
16.3.3 Estimation of parameters 380
16.3.4 Evaluation 381
16.3.5 Graphical tools for characterizing the dynamic
behaviour of the STAR model 389
16.3.6 Examples 390
16.4 Building switching regression models 418
16.4.1 Specification 419
16.4.2 Estimation and evaluation 422
16.4.3 Examples 423
16.5 Building artificial neural network models 434
16.5.1 Specification 435
16.5.2 Estimation 437
16.5.3 Evaluation 438
16.5.4 Alternative modelling approaches 439
16.5.5 Examples 439
16.6 Two forecast comparisons 445
16.6.1 Forecasting Wolf s annual sunspot numbers 445
16.6.2 Forecasting the monthly US unemployment rate 448
17 Other topics 452
17.1 Aggregation 452
17.2 Seasonality 458
17.2.1 Time-varying seasonality 458
17.2.2 Temporal aggregation and time-varying seasonality 463
17.2.3 Nonlinear filters in seasonal adjustment 464
17.3 Outliers and nonlinearity 465
17.3.1 What is an outlier? 465
17.3.2 Model-based definitions 466
Bibliography 470
Author Index 537
General Index 549
|
| any_adam_object | 1 |
| author | Teräsvirta, Timo 1941- Tjøstheim, Dag Granger, C. W. J. 1934-2009 |
| author_GND | (DE-588)121069087 (DE-588)120941104 |
| author_facet | Teräsvirta, Timo 1941- Tjøstheim, Dag Granger, C. W. J. 1934-2009 |
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| dewey-full | 330.015195 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 330 - Economics |
| dewey-raw | 330.015195 |
| dewey-search | 330.015195 |
| dewey-sort | 3330.015195 |
| dewey-tens | 330 - Economics |
| discipline | Informatik Mathematik Wirtschaftswissenschaften |
| edition | 1. publ. |
| format | Book |
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| spelling | Teräsvirta, Timo 1941- Verfasser (DE-588)121069087 aut Modelling nonlinear economic time series by Timo Teräsvirta ; Dag Tjøstheim and Clive W. J. Granger 1. publ. Oxford [u.a.] Oxford Univ. Press 2010 XXVIII, 557 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Advanced texts in econometrics Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Multivariate Analyse (DE-588)4040708-1 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 s Multivariate Analyse (DE-588)4040708-1 s b DE-604 Tjøstheim, Dag Verfasser aut Granger, C. W. J. 1934-2009 Verfasser (DE-588)120941104 aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020645089&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Teräsvirta, Timo 1941- Tjøstheim, Dag Granger, C. W. J. 1934-2009 Modelling nonlinear economic time series Zeitreihenanalyse (DE-588)4067486-1 gnd Multivariate Analyse (DE-588)4040708-1 gnd |
| subject_GND | (DE-588)4067486-1 (DE-588)4040708-1 |
| title | Modelling nonlinear economic time series |
| title_auth | Modelling nonlinear economic time series |
| title_exact_search | Modelling nonlinear economic time series |
| title_full | Modelling nonlinear economic time series by Timo Teräsvirta ; Dag Tjøstheim and Clive W. J. Granger |
| title_fullStr | Modelling nonlinear economic time series by Timo Teräsvirta ; Dag Tjøstheim and Clive W. J. Granger |
| title_full_unstemmed | Modelling nonlinear economic time series by Timo Teräsvirta ; Dag Tjøstheim and Clive W. J. Granger |
| title_short | Modelling nonlinear economic time series |
| title_sort | modelling nonlinear economic time series |
| topic | Zeitreihenanalyse (DE-588)4067486-1 gnd Multivariate Analyse (DE-588)4040708-1 gnd |
| topic_facet | Zeitreihenanalyse Multivariate Analyse |
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