Applied economic forecasting using time series methods
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[2018]
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| 008 | 180426s2018 xx |||| |||| 00||| eng d | ||
| 020 | |a 9780190622015 |9 978-0-19-062201-5 | ||
| 035 | |a (OCoLC)1012608387 | ||
| 035 | |a (DE-599)BVBBV044921336 | ||
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| 100 | 1 | |a Ghysels, Eric |d 1956- |e Verfasser |0 (DE-588)130422134 |4 aut | |
| 245 | 1 | 0 | |a Applied economic forecasting using time series methods |c Eric Ghysels (University of North Carolina, Chapel Hill, United States), Massimiliano Marcellino (Bocconi University, Milan, Italy) |
| 264 | 1 | |a New York |b Oxford University Press |c [2018] | |
| 264 | 4 | |c © 2018 | |
| 300 | |a xviii, 597 Seiten |b Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 0 | 7 | |a Wirtschaft |0 (DE-588)4066399-1 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Prognose |0 (DE-588)4047390-9 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Zeitreihenanalyse |0 (DE-588)4067486-1 |2 gnd |9 rswk-swf |
| 653 | 0 | |a Economic forecasting / Mathematical models | |
| 653 | 0 | |a Economic forecasting / Statistical methods | |
| 653 | 0 | |a Economic forecasting / Mathematical models | |
| 653 | 0 | |a Economic forecasting / Statistical methods | |
| 689 | 0 | 0 | |a Wirtschaft |0 (DE-588)4066399-1 |D s |
| 689 | 0 | 1 | |a Prognose |0 (DE-588)4047390-9 |D s |
| 689 | 0 | 2 | |a Zeitreihenanalyse |0 (DE-588)4067486-1 |D s |
| 689 | 0 | |C b |5 DE-604 | |
| 700 | 1 | |a Marcellino, Massimiliano |d 1970- |e Verfasser |0 (DE-588)114655065 |4 aut | |
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| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-030314614 | |
Datensatz im Suchindex
| DE-BY-UBR_call_number | 40/QH 237 G427 |
|---|---|
| DE-BY-UBR_katkey | 6027564 |
| DE-BY-UBR_location | 40 |
| DE-BY-UBR_media_number | 069041420958 |
| _version_ | 1822697063762100224 |
| adam_text | Contents
Preface xv
Part I Forecasting with the Linear Regression Model
1 The Baseline Linear Regression Model 3
1.1 Introduction ................................................. 3
1.2 The basic specification....................................... 4
1.3 Parameter estimation ......................................... 6
1.4 Measures of model fit ........................................ 8
1.5 Constructing point forecasts................................. 10
1.6 Interval and density forecasts .............................. 13
1.7 Parameter testing ........................................... 15
1.8 Variable selection........................................... 18
1.9 Automated variable selection procedures...................... 21
1.9.1 Forward selection (FWD) ...............................22
1.9.2 Least angle regressions (LARS)....................... 22
1.9.3 LASSO and elastic net estimator (NET)................23
1.10 Multicollinearity .......................................... 24
1.11 Example using simulated data ............................... 26
1.11.1 Data simulation procedure............................. 26
1.12 Empirical examples ......................................... 32
1.12.1 Forecasting Euro area GDP growth..................... 32
1.12.2 Forecasting US GDP growth............................ 43
1.13 A hint of dynamics.......................................... 47
1.13.1 Revisiting GDP forecasting ............................48
v
VI
CONTENTS
1.13.2 Forecasting default risk...............................49
1.14 Concluding remarks ...........................................55
2 Model Mis-Specification 57
2.1 Introduction................................................. 57
2.2 Heteroskedastic and correlated errors........................ 57
2.2.1 The Generalized Least Squares (GLS) estimator and
the feasible GLS estimator.............................60
2.3 HAC estimators ...............................................64
2.4 Some tests for homoskedasticity and no correlation............66
2.5 Parameter instability.........................................69
2.5.1 The effects of parameter changes ....................70
2.5.2 Simple tests for parameter changes.....................71
2.5.3 Recursive methods......................................74
2.5.4 Dummy variables........................................76
2.5.5 Multiple breaks....................................... 78
2.6 Measurement error and real-time data ........................ 80
2.7 Instrumental variables........................................82
2.8 Examples using simulated data.................................84
2.9 Empirical examples .......................................... 94
2.9.1 Forecasting Euro area GDP growth.......................94
2.9.2 Forecasting US GDP growth.............................107
2.9.3 Default risk..........................................114
2.10 Concluding remarks ..........................................118
3 The Dynamic Linear Regression Model 119
3.1 Introduction.................................................119
3.2 Types of dynamic linear regression
models ......................................................120
3.3 Estimation and testing.......................................125
3.4 Model specification..........................................126
3.5 Forecasting with dynamic models..............................128
3.6 Examples with simulated data ................................130
3.7 Empirical examples ..........................................132
3.7.1 Forecasting Euro area GDP growth......................133
3.7.2 Forecasting US GDP growth.............................138
CONTENTS vii
3.7.3 Default risk........................................142
3.8 Concluding remarks ........................................142
4 Forecast Evaluation and Combination 145
4.1 Introduction...............................................145
4.2 Unbiasedness and efficiency ...............................146
4.3 Evaluation of fixed event forecasts .......................149
4.4 Tests of predictive accuracy...............................150
4.5 Forecast comparison tests .................................152
4.6 The combination of forecasts ..............................154
4.7 Forecast encompassing......................................156
4.8 Evaluation and combination of density forecasts............157
4.8.1 Evaluation..........................................158
4.8.2 Comparison..........................................159
4.8.3 Combination ........................................159
4.9 Examples using simulated data..............................160
4.10 Empirical examples .......................................164
4.10.1 Forecasting Euro area GDP growth....................164
4.10.2 Forecasting US GDP growth...........................167
4.10.3 Default risk........................................169
4.11 Concluding remarks ..................................170
Part II Forecasting with Time Series Models
5 Univariate Time Series Models 173
5.1 Introduction...............................................173
5.2 Representation ............................................174
5.2.1 Autoregressive processes............................176
5.2.2 Moving average processes ...........................180
5.2.3 ARMA processes......................................181
5.2.4 Integrated processes ...............................183
5.2.5 ARIMA processes.....................................184
5.3 Model specification.......................................185
5.3.1 AC/PAC based specification......................... 185
5.3.2 Testing based specification.........................186
5.3.3 Testing for ARCH ...................................187
Vlll
CONTENTS
5.3.4 Specification with information criteria ..............187
5.4 Estimation ...................................................189
5.5 Unit root tests ..............................................190
5.6 Diagnostic checking ..........................................195
5.7 Forecasting, known parameters ................................196
5.7.1 General formula.......................................196
5.7.2 Some examples.........................................197
5.7.3 Some additional comments..............................201
5.8 Forecasting, estimated parameters ............................202
5.9 Multi-steps (or direct) estimation............................204
5.10 Permanent-transitory decomposition ..........................207
5.10.1 The Beveridge Nelson decomposition....................207
5.10.2 The Hodrick-Prescott filter...........................209
5.11 Exponential smoothing .......................................209
5.12 Seasonality .................................................211
5.12.1 Deterministic seasonality.............................212
5.12.2 Stochastic seasonality................................213
5.13 Examples with simulated data ...............................214
5.13.1 Stationary ARMA processes.............................214
5.13.2 Estimation: Full sample analysis .....................218
5.13.3 Estimation: Subsample analysis........................220
5.13.4 Forecasting...........................................227
5.14 Empirical examples ..........................................228
5.14.1 Modeling and forecasting the US federal funds rate . . 228
5.14.2 Modeling and forecasting US inventories...............240
5.15 Concluding remarks ..........................................251
6 VAR Models 253
6.1 Representation ...............................................254
6.2 Specification of the model....................................256
6.3 Estimation ...................................................257
6.4 Diagnostic checks.............................................260
6.5 Forecasting ..................................................261
6.6 Impulse response functions ...................................263
6.7 Forecast error variance decomposition ........................268
6.8 Structural VARs with long-run restrictions 269
6.9 VAR models with simulated data................................271
CONTENTS
IX
6.10 Empirical examples ...........................................282
6.10.1 GDP growth in the Euro area............................282
6.10.2 Monetary transmission mechanism .......................289
6.11 Concluding remarks ...........................................300
7 Error Correction Models 301
7.1 Introduction..................................................301
7.2 Spurious regressions .........................................302
7.3 Cointegration and error correction models.....................303
7.4 Engle and Granger cointegration test..........................305
7.5 Johansen cointegration test ..................................306
7.6 MA representations of cointegrated
processes ....................................................308
7.7 Forecasting in the presence of
cointegration ................................................311
7.8 The effects of stochastic trends on
forecasts ....................................................312
7.9 Example using simulated series ...............................313
7.10 Empirical examples ...........................................323
7.10.1 Term structure of UK interest rates....................323
7.10.2 Composite coincident and leading indexes ..............334
7.10.3 Predicting coincident and leading indices in the period
before the crisis......................................334
7.10.4 Predicting coincident and leading indices
during the financial crisis............................340
7.11 Concluding remarks ...........................................342
8 Bayesian VAR Models 345
8.1 Introduction..................................................345
8.2 A primer on Bayesian econometrics.............................346
8.2.1 Parameters as random variables.........................347
8.2.2 From prior to posterior distribution...................347
8.2.3 An example: The posterior of the mean when the
variance is known......................................348
8.2.4 The Bayesian linear regression model...................350
8.3 Baseline Bayesian VAR case....................................353
X
CONTENTS
8.3.1 Baseline BVAR specification............................353
8.3.2 Prior parameterizations ...............................354
8.4 Forecasting with the BVAR.....................................356
8.4.1 The proper method.....................................356
8.4.2 The pseudo-iterated approach..........................357
8.4.3 The direct forecasting approach.......................358
8.5 Example with simulated data.................................359
8.6 Empirical examples ...........................................361
8.6.1 GDP growth in the Euro area............................361
8.6.2 Multi-country inflation rates..........................362
8.7 Concluding remarks ...........................................364
Part III TAR, Markov Switching, and State Space
Models
9 TAR and STAR Models 369
9.1 Introduction..................................................369
9.2 Specification ................................................371
9.3 Estimation ...................................................373
9.4 Testing for STAR and TAR......................................374
9.5 Diagnostic tests .............................................376
9.6 Forecasting ..................................................376
9.6.1 Point forecasts ..................................376
9.6.2 Interval forecasts.....................................378
9.7 Artificial neural networks ...................................379
9.8 Example: Simulated data ......................................382
9.8.1 Testing for linearity versus non-linearity.............382
9.8.2 Estimation.............................................384
9.8.3 Forecasting............................................386
9.8.4 Two-steps ahead forecasting............................388
9.9 Example: Forecasting industrial
production growth ............................................392
9.10 Concluding remarks ...........................................397
10 Markov Switching Models 399
10.1 Introduction................................................399
CONTENTS
xi
10.2 Markov chains .............................................400
10.3 Mixture of i.i.d. distributions ...........................403
10.4 Markov switching dynamic models ...........................406
10.5 Empirical example: Simulated data..........................410
10.6 Empirical example: Industrial production ..................413
10.7 Concluding remarks ........................................417
11 State Space Models 419
11.1 Introduction...............................................419
11.2 Models in state space form ................................420
11.2.1 An ARMA(2,2) model..................................421
11.2.2 A time-varying parameters (TVP) regression model . . 422
11.2.3 A model with stochastic volatility................423
11.2.4 A dynamic factor model..............................424
11.2.5 Unobserved component models ........................425
11.3 The Kalman filter .........................................426
11.3.1 The key equations of the Kalman filter............427
11.3.2 The iterative procedure.............................429
11.3.3 Some derivations and additional results...........430
11.4 Example with simulated data: The TVP regression model . . 432
11.5 Empirical examples ........................................440
11.5.1 Forecasting US GDP growth with a TVP model .... 440
11.5.2 Forecasting GDP growth with dynamic factor models . 444
11.6 Concluding remarks ........................................449
Part IV Mixed Frequency, Large Datasets, and
Volatility
12 Models for Mixed-Frequency Data 453
12.1 Introduction...............................................453
12.2 Bridge equations ..........................................455
12.3 MIDAS Regressions..........................................459
12.3.1 The basic MIDAS regression model....................460
12.3.2 The AR-MIDAS and ADL-MIDAS models...................463
12.3.3 Slope or no slope?..................................463
12.3.4 U-MIDAS and MIDAS with step functions ..............465
cii CONTENTS
12.3.5 Extensions of the MIDAS regression model.............466
12.4 Mixed-frequency VAR .................................470
12.4.1 Parameter-driven approach.............................471
12.4.2 Observation-driven approach...........................474
12.5 Example: Nowcasting with simulated data....................476
12.5.1 The bridge equation approach..........................477
12.5.2 The MIDAS regression approach ........................480
12.6 Example: Nowcasting US GDP growth.........................482
12.6.1 Predicting the worst trough of the crisis ............483
12.6.2 Predicting mild positive and negative growth rates . . 493
12.6.3 Predicting GDP growth rates, 2007-2012 .............. 496
12.7 Concluding remarks .........................................501
13 Models for Large Datasets 503
13.1 Introduction................................................503
13.2 Factor models ..............................................504
13.3 The three pass regression filter............................509
13.4 Large unbalanced datasets...................................512
13.4.1 Mixed-frequency factor models.........................512
13.4.2 Mixed-frequency three pass regression filter..........516
13.4.3 Missing observations and ragged edges.................519
13.5 Example with simulated data.................................520
13.6 Empirical example: Forecasting GDP growth ..................521
13.7 Concluding remarks .........................................527
14 Forecasting Volatility 531
14.1 Introduction................................................531
14.2 ARCH-type models ...........................................532
14.2.1 Model specifications .................................533
14.2.2 Estimation............................................537
14.3 MIDAS regressions and volatility forecasting ...............539
14.3.1 Realized volatility...................................540
14.3.2 Realized volatility and MIDAS regressions.............541
14.3.3 HAR models............................................541
14.3.4 Direct versus iterated volatility forecasting.........542
14.3.5 Variations on the theme of MIDAS regressions..........544
CONTENTS xiii
14.4 GARCH models again....................................545
14.5 Volatility forecasting evaluation.....................546
14.6 Forecasting S P 500 index volatility..................548
14.6.1 ARCH-type models ..............................549
14.6.2 Realized volatility ...........................551
14.7 Concluding remarks ...................................555
Bibliography 559
Subject Index 587
Author Index 592
|
| any_adam_object | 1 |
| author | Ghysels, Eric 1956- Marcellino, Massimiliano 1970- |
| author_GND | (DE-588)130422134 (DE-588)114655065 |
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| bvnumber | BV044921336 |
| classification_rvk | QH 237 |
| ctrlnum | (OCoLC)1012608387 (DE-599)BVBBV044921336 |
| discipline | Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV044921336 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-24T06:24:44Z |
| institution | BVB |
| isbn | 9780190622015 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030314614 |
| oclc_num | 1012608387 |
| open_access_boolean | |
| owner | DE-384 DE-355 DE-BY-UBR DE-739 DE-11 DE-N2 DE-523 DE-20 |
| owner_facet | DE-384 DE-355 DE-BY-UBR DE-739 DE-11 DE-N2 DE-523 DE-20 |
| physical | xviii, 597 Seiten Diagramme |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Oxford University Press |
| record_format | marc |
| spellingShingle | Ghysels, Eric 1956- Marcellino, Massimiliano 1970- Applied economic forecasting using time series methods Wirtschaft (DE-588)4066399-1 gnd Prognose (DE-588)4047390-9 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
| subject_GND | (DE-588)4066399-1 (DE-588)4047390-9 (DE-588)4067486-1 |
| title | Applied economic forecasting using time series methods |
| title_auth | Applied economic forecasting using time series methods |
| title_exact_search | Applied economic forecasting using time series methods |
| title_full | Applied economic forecasting using time series methods Eric Ghysels (University of North Carolina, Chapel Hill, United States), Massimiliano Marcellino (Bocconi University, Milan, Italy) |
| title_fullStr | Applied economic forecasting using time series methods Eric Ghysels (University of North Carolina, Chapel Hill, United States), Massimiliano Marcellino (Bocconi University, Milan, Italy) |
| title_full_unstemmed | Applied economic forecasting using time series methods Eric Ghysels (University of North Carolina, Chapel Hill, United States), Massimiliano Marcellino (Bocconi University, Milan, Italy) |
| title_short | Applied economic forecasting using time series methods |
| title_sort | applied economic forecasting using time series methods |
| topic | Wirtschaft (DE-588)4066399-1 gnd Prognose (DE-588)4047390-9 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
| topic_facet | Wirtschaft Prognose Zeitreihenanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030314614&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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