Handbook of Economic Forecasting, Volume 1

Volume 1

By: M D Intriligator, Kenneth Arrow, A G Timmermann, C W J Granger, G Elliott

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Hardcover | 30 May 2006 | Edition Number 1

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1070 Pages

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6 1/2 X 9 7/16 in Part 1. Forecasting methodology.
1. Bayes ian forecasting (J. Geweke, C. Whiteman).

2. Forecasti ng and decision theory (C.W.J.Granger, M.J. Machina).

3. Forecast evaluation (K.D. West).

4. Forecast combin ations (A. Timmermann).

5. Predictive density evaluati on (V. Corradi, N.R. Swanson).

Part 2. Forecast ing models.
6. Forecasting with VARMA models (H. Lutkepohl).

7. Forecasting with unobserved components t ime series models (A. Harvey).

8. Forecasting economic variables with nonlinear models (T. Terasvirta).

9. App roximate nonlinear forecasting models (H. White).

<STR ONG>Part 3. Forecasting with different data structures.
10. Forecasting with many predictors (J.H. Stock, M.W. Watson).< BR>
11. Forecasting with trending data (G. Elliott).
12. Forecasting with breaks (M.P. Clements, D.F. Hendry).
13. Forecasting seasonal time series (E. Ghysels, D.R. O

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Non-Fiction Economics Econometrics Economic Forecasting

Introduction to the Series	p. v
Contents of the Handbook	p. vii
Forecasting Methodology
Bayesian Forecasting	p. 3
Abstract	p. 4
Keywords	p. 4
Introduction	p. 6
Bayesian inference and forecasting: A primer	p. 7
Posterior simulation methods	p. 25
'Twas not always so easy: A historical perspective	p. 41
Some Bayesian forecasting models	p. 53
Practical experience with Bayesian forecasts	p. 68
References	p. 73
Forecasting and Decision Theory	p. 81
Abstract	p. 82
Keywords	p. 82
Preface	p. 83
History of the field	p. 83
Forecasting with decision-based loss functions	p. 87
References	p. 98
Forecast Evaluation	p. 99
Abstract	p. 100
Keywords	p. 100
Introduction	p. 101
A brief history	p. 102
A small number of nonnested models, Part I	p. 104
A small number of nonnested models, Part II	p. 106
A small number of nonnested models, Part III	p. 111
A small number of models, nested: MPSE	p. 117
A small number of models, nested, Part II	p. 122
Summary on small number of models	p. 125
Large number of models	p. 125
Conclusions	p. 131
Acknowledgements	p. 132
References	p. 132
Forecast Combinations	p. 135
Abstract	p. 136
Keywords	p. 136
Introduction	p. 137
The forecast combination problem	p. 140
Estimation	p. 156
Time-varying and nonlinear combination methods	p. 165
Shrinkage methods	p. 170
Combination of interval and probability distribution forecasts	p. 176
Empirical evidence	p. 181
Conclusion	p. 193
Acknowledgements	p. 193
References	p. 194
Predictive Density Evaluation	p. 197
Abstract	p. 198
Keywords	p. 199
Introduction	p. 200
Estimation, specification testing, and model evaluation	p. 200
Testing for Correct Specification of Conditional Distributions	p. 207
Specification testing and model evaluation in-sample	p. 207
Specification testing and model selection out-of-sample	p. 220
Evaluation of (Multiple) Misspecified Predictive Models	p. 234
Pointwise comparison of (multiple) misspecified predictive models	p. 234
Comparison of (multiple) misspecified predictive density models	p. 253
Acknowledgements	p. 271
Appendices and References	p. 271
Assumptions	p. 271
Proofs	p. 275
References	p. 280
Forecasting Models
Forecasting with VARMA Models	p. 287
Abstract	p. 288
Keywords	p. 288
Introduction and overview	p. 289
VARMA processes	p. 292
Specifying and estimating VARMA models	p. 306
Forecasting with estimated processes	p. 316
Conclusions	p. 319
Acknowledgements	p. 321
References	p. 321
Forecasting with Unobserved Components Time Series Models	p. 327
Abstract	p. 330
Keywords	p. 330
Introduction	p. 331
Structural time series models	p. 335
ARIMA and autoregressive models	p. 348
Explanatory variables and interventions	p. 352
Seasonality	p. 355
State space form	p. 361
Multivariate models	p. 370
Continuous time	p. 383
Nonlinear and non-Gaussian models	p. 391
Stochastic volatility	p. 403
Conclusions	p. 406
Acknowledgements	p. 407
References	p. 408
Forecasting Economic Variables with Nonlinear Models	p. 413
Abstract	p. 414
Keywords	p. 415
Introduction	p. 416
Nonlinear models	p. 416
Building nonlinear models	p. 425
Forecasting with nonlinear models	p. 431
Forecast accuracy	p. 440
Lessons from a simulation study	p. 444
Empirical forecast comparisons	p. 445
Final remarks	p. 451
Acknowledgements	p. 452
References	p. 453
Approximate Nonlinear Forecasting Methods	p. 459
Abstract	p. 460
Keywords	p. 460
Introduction	p. 461
Linearity and nonlinearity	p. 463
Linear, nonlinear, and highly nonlinear approximation	p. 467
Artificial neural networks	p. 474
QuickNet	p. 476
Interpretational issues	p. 484
Empirical examples	p. 492
Summary and concluding remarks	p. 509
Acknowledgements	p. 510
References	p. 510
Forecasting with Particular Data Structures
Forecasting with Many Predictors	p. 515
Abstract	p. 516
Keywords	p. 516
Introduction	p. 517
The forecasting environment and pitfalls of standard forecasting methods	p. 518
Forecast combination	p. 520
Dynamic factor models and principal components analysis	p. 524
Bayesian model averaging	p. 535
Empirical Bayes methods	p. 542
Empirical illustration	p. 545
Discussion	p. 549
References	p. 550
Forecasting with Trending Data	p. 555
Abstract	p. 556
Keywords	p. 556
Introduction	p. 557
Model specification and estimation	p. 559
Univariate models	p. 563
Cointegration and short run forecasts	p. 581
Near cointegrating models	p. 586
Predicting noisy variables with trending regressors	p. 591
Forecast evaluation with unit or near unit roots	p. 596
Conclusion	p. 600
References	p. 601
Forecasting with Breaks	p. 605
Abstract	p. 606
Keywords	p. 606
Introduction	p. 607
Forecast-error taxonomies	p. 609
Breaks in variance	p. 614
Forecasting when there are breaks	p. 617
Detection of breaks	p. 622
Model estimation and specification	p. 627
Ad hoc forecasting devices	p. 631
Non-linear models	p. 635
Forecasting UK unemployment after three crises	p. 640
Concluding remarks	p. 648
Taxonomy derivations for Equation (10)	p. 648
Derivations for Section 4.3	p. 650
References	p. 651
Forecasting Seasonal Time Series	p. 659
Abstract	p. 660
Keywords	p. 661
Introduction	p. 662
Linear models	p. 664
Periodic models	p. 683
Other specifications	p. 691
Forecasting, seasonal adjustment and feedback	p. 701
Conclusion	p. 705
References	p. 706
Applications of Forecasting Methods
Survey Expectations	p. 715
Abstract	p. 716
Keywords	p. 716
Introduction	p. 717
Concepts and models of expectations formation	p. 720
Measurement of expectations: History and developments	p. 733
Uses of survey data in forecasting	p. 748
Uses of survey data in testing theories: Evidence on rationality of expectations	p. 754
Conclusions	p. 767
Acknowledgements	p. 768
Derivation of optimal forecasts under a 'Lin-Lin' cost function	p. 768
References to the main sources of expectational data	p. 769
References	p. 770
Volatility and Correlation Forecasting	p. 777
Abstract	p. 779
Keywords	p. 779
Introduction	p. 780
Uses of volatility forecasts	p. 786
Garch volatility	p. 798
Stochastic volatility	p. 814
Realized volatility	p. 830
Multivariate volatility and correlation	p. 839
Evaluating volatility forecasts	p. 853
Concluding remarks	p. 864
References	p. 865
Leading Indicators	p. 879
Abstract	p. 880
Keywords	p. 880
Introduction	p. 881
Selection of the target and leading variables	p. 884
Filtering and dating procedures	p. 887
Construction of nonmodel based composite indexes	p. 892
Construction of model based composite coincident indexes	p. 894
Construction of model based composite leading indexes	p. 901
Examples of composite coincident and leading indexes	p. 915
Other approaches for prediction with leading indicators	p. 925
Evaluation of leading indicators	p. 934
Review of the recent literature on the performance of leading indicators	p. 945
What have we learned?	p. 951
References	p. 952
Forecasting with REal-Time Macroeconomic Data	p. 961
Abstract	p. 962
Keywords	p. 962
An illustrative example: The index of leading indicators	p. 963
The real-time data set for macroeconomists	p. 964
Why are forecasts affected by data revisions?	p. 969
The literature on how data revisions affect forecasts	p. 974
Optimal forecasting when data are subject to revision	p. 978
Summary and suggestions for further research	p. 980
References	p. 981
Forecasting in Marketing	p. 983
Abstract	p. 984
Keywords	p. 984
Introduction	p. 985
Performance measures	p. 986
Models typical to marketing	p. 992
Deriving forecasts	p. 1003
Conclusion	p. 1009
References	p. 1010
Author Index	p. I-1
Subject Index	p. I-19
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Handbook of Economic Forecasting, Volume 1

Volume 1

At a Glance

Hardcover

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How to return your order

Defective items

You Can Find This Book In

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