The Structural Econometric Time Series Analysis Approach

Cover......Page 1
Half-title......Page 3
Title......Page 5
Copyright......Page 6
Contents......Page 7
Contributors......Page 11
Acknowledgments......Page 13
Introduction......Page 15
Part I The SEMTSA approach......Page 19
1 Introduction......Page 21
2 General formulation and analysis of a system of dynamic equations......Page 22
3 Algebraic analysis of a dynamic version of Haavelmo’s model......Page 27
4.1 Analyzes utilizing Box–Jenkins techniques......Page 32
4.2 Analyzes of final equations utilizing likelihood ratios and posterior odds......Page 38
5 Empirical analyses of transfer equations (3.14)–(3.15)......Page 46
6 Summary of results and implications for structural equations......Page 54
APPENDIX DATA SOURCES......Page 59
BIBLIOGRAPHY......Page 60
1 Introduction......Page 62
2.1 Overview of the traditional approach......Page 63
2.2 Statistical estimation problems......Page 67
2.3 Hypothesis testing and SEMs......Page 77
2.4 Prediction procedures for SEMs......Page 79
3 The SEMTSA approach......Page 80
4 Conclusions......Page 89
BIBLIOGRAPHY......Page 91
Comment (1979)......Page 97
Comment (1979)......Page 100
Comment (1979)......Page 102
Comment (1979)......Page 105
Rejoinder (1979)......Page 109
BIBLIOGRAPHY......Page 113
1 Introduction......Page 114
2.1 The traditional approach to econometric modeling......Page 115
2.2 Time series identification of dynamic econometric models......Page 116
2.3 Structural econometric modeling and time series analysis......Page 119
3.1 Specification of an initial model......Page 125
3.2 A limited information analysis of the initial model......Page 128
3.3 Diagnostic checking......Page 141
3.4 Dynamic properties and forecasting performance of the model......Page 150
4 Some tentative conclusions......Page 178
BIBLIOGRAPHY......Page 180
Comment (1983)......Page 183
Comment (1983)......Page 187
1 Marginalization, exogenous variables, and parameter stability......Page 190
3 Forecasting......Page 191
1 Introduction......Page 193
2 Background on the SEMTSA approach and its applications......Page 194
3 Model and variable selection procedures and results......Page 207
4 Aggregation and non-linearity......Page 212
5 Summary and conclusions......Page 214
BIBLIOGRAPHY......Page 215
1 Introduction......Page 219
2 Specification of and estimation and testing procedures for final equations......Page 221
3 Specification of and estimation and testing procedures for sets of transfer functions......Page 232
4.1 Single-equation estimation procedure......Page 237
4.2 Joint estimation of a set of structural equations......Page 240
4.3 Single-equation structural estimation reconsidered: two-step LIML......Page 244
5 Some concluding remarks......Page 247
BIBLIOGRAPHY......Page 248
1 Introduction......Page 251
2 Consistent estimation of a vector MA process......Page 252
3 Single structural equation estimation......Page 255
Concluding remarks......Page 257
BIBLIOGRAPHY......Page 258
Part II Selected applications......Page 259
1 Introduction......Page 261
2.1 Structural equations of S0......Page 262
2.2 Transfer functions (TFs) for S0, (2.11)......Page 264
2.3 Final equations for S0......Page 266
2.4 Empirical analyses of final equations of the initial model S0......Page 267
2.5 Empirical analyses of final equations using likelihood ratio tests and posterior odds ratios......Page 276
3 Formulation and analysis of variants of the initial model S0......Page 286
4 Empirical analysis of the transfer functions of S3......Page 295
5 Concluding remarks......Page 302
BIBLIOGRAPHY......Page 304
1 Time series and structural dynamic models......Page 306
2 Data......Page 309
3 Time series models......Page 311
4 Econometric models of inventory investment......Page 319
4.1 Raw (or purchased) materials inventory......Page 320
4.2 Finished goods inventory......Page 321
4.3 Goods in progress......Page 322
5 Results......Page 324
6 Conclusion......Page 330
BIBLIOGRAPHY......Page 331
1 Introduction......Page 333
2 Three models of hyperinflation......Page 334
3 Time series analysis......Page 339
4 Further time series analysis of model MC......Page 343
5 Conclusions......Page 345
7 Bayesian analysis of the first order moving average process......Page 346
BIBLIOGRAPHY......Page 348
1 Introduction......Page 350
2 Methodology for analyzing seasonal economic models......Page 355
2.1 Analysis of linear dynamic econometric models......Page 356
2.2 Seasonality in time series data......Page 359
2.3 An approach to the analysis of seasonality in structural models......Page 362
3.1 Model formulation......Page 365
3.2 Analysis of the transfer functions......Page 369
3.3 Analysis of the final equations......Page 371
3.4 Summary......Page 380
4.1 Analysis of the univariate time series......Page 381
4.2 Analysis of the transfer functions......Page 389
4.3 Summary of empirical findings......Page 391
5 Discussion......Page 392
APPENDIX A DERIVATIONS OF FEs AND TFs......Page 394
APPENDIX B SOURCES OF DATA......Page 398
BIBLIOGRAPHY......Page 399
Comment (1978)......Page 400
Comment and implications for policy-makers and model builders (1978)......Page 406
Response to discussants (1978)......Page 412
2 Changes in speculative prices in an efficient capital market: theory and evidence......Page 415
3 The Zellner–Palm consistency constraints......Page 416
4.1 Application to equilibrium models of asset pricing......Page 417
4.3 Additional considerations......Page 419
5 Conclusions......Page 420
BIBLIOGRAPHY......Page 421
1 Introduction......Page 423
2 A stylized exchange rate model and empirical analysis......Page 425
3 Interpretations and implications......Page 430
4 Summary and conclusion......Page 432
BIBLIOGRAPHY......Page 433
1 Introduction......Page 436
2 Univariate models implied by a vector autoregressive model......Page 438
3 A case study: the series and univariate analysis......Page 439
4 Testing for cointegration......Page 444
5 The vector autoregressive model (VAR)......Page 445
6 Implied univariate models in the VAR and comparison with the estimated ARIMA models: ad hoc comparison......Page 449
7 Comparison of the implied and estimated univariate ARIMA models: a test procedure......Page 454
8 An economic application......Page 457
9 Summary and conclusions......Page 461
APPENDIX A UNIVARIATE ARIMA MODELS IMPLIED BY A VECTOR AUTOREGRESSIVE MODEL......Page 463
APPENDIX B A COMMENT ON THE PRECISION OF THE FREQUENCY AND MODULUS OF THE ROOTS IN AN ESTIMATED AUTOREGRESSIVE MODEL......Page 465
APPENDIX C COMMON ROOTS TEST: COMPUTATION OF THE MATRICES......Page 469
BIBLIOGRAPHY......Page 471
Part III Macroeconomic forecasting and modeling......Page 473
1 Introduction......Page 475
2 Analyzes of annual data for nine countries......Page 476
2.1 Individual country models......Page 478
2.2 Forecasts based on pooled international data......Page 484
2.3 Comparison with OECD forecasts......Page 491
3 Forecasting quarterly output growth rates......Page 496
4 Summary of results and concluding remarks......Page 498
BIBLIOGRAPHY......Page 501
1 Introduction......Page 503
2.1 Model description......Page 504
2.2 Derivation and description of shrinkage forecasts......Page 506
2.3 Elaboration of the AR(3)LI model......Page 509
3 Data......Page 510
4.1 Forecasting results for an expanded data set......Page 512
4.2 Forecasting using a world output growth rate variable......Page 517
4.3 Comparisons with OECD forecast RMSEs......Page 520
5 Summary and concluding remarks......Page 522
BIBLIOGRAPHY......Page 523
1 Introduction......Page 524
2.1 Forecasting turning points......Page 525
2.2 Point forecasts using different loss functions......Page 527
2.3 Turning point and point forecasting combined......Page 530
2.4 Summary and additional considerations......Page 532
3 Data and applications......Page 535
4 Summary and concluding remarks......Page 540
BIBLIOGRAPHY......Page 541
1 Introduction......Page 546
2 Models and methods......Page 547
2.1 Optimal turning point forecasts......Page 550
3 Description of data......Page 553
4 Results of forecasting turning points, eighteen countries, 1974–1986......Page 555
5 Summary and concluding remarks......Page 571
A.1 Fixed parameter models......Page 572
A.2 Time-varying parameter (TVP) models......Page 573
BIBLIOGRAPHY......Page 575
1 Introduction......Page 577
2 To combine or not to combine forecasts?......Page 578
3.1 Posterior odds and choosing models......Page 580
3.2 Combining and choosing among individual and combined forecasts......Page 581
4 Models, methods, and their performance in forecasting......Page 585
4.1 Models and forecasts......Page 586
4.3 Empirical results......Page 589
5 Summary and concluding remarks......Page 596
APPENDIX A POSTERIOR ODDS FOR FIXED VERSUS TIME-VARYING PARAMETER MODELS......Page 600
B.1 Unpooled case......Page 601
B.2 A pooled case......Page 603
B.3 Values for hyperparameters (φ, Σ)......Page 605
BIBLIOGRAPHY......Page 606
18 Pooling in dynamic panel data models: an application to forecasting GDP growth rates (2000)......Page 608
1 The model and the forecasting procedures......Page 610
2.1 The models......Page 614
2.2 Properties of pooling restriction tests......Page 619
2.3 Analyzes of international data......Page 623
3 Conclusions......Page 625
APPENDIX DATA......Page 627
BIBLIOGRAPHY......Page 628
19 Forecasting turning points in countries’ output growth rates: a response to Milton Friedman (1999)......Page 630
BIBLIOGRAPHY......Page 633
Part IV Disaggregation, forecasting, and modeling......Page 635
1 Introduction......Page 637
2 Analysis of the data......Page 639
3 The Bayesian pooling technique......Page 643
4 A comparison of the forecasts......Page 646
5 Conclusions......Page 651
Total payroll......Page 652
Housing permits dollar valuation......Page 653
BIBLIOGRAPHY......Page 654
1 Introduction......Page 655
2 Models and methods......Page 657
3 The results from the forecasting experiments......Page 663
4 Conclusions......Page 669
(1) Total employment (EMP)......Page 670
(5) Index of 12 leading economic indicators (LEAD)......Page 671
BIBLIOGRAPHY......Page 672
1 Models......Page 674
2 Experiments......Page 677
APPENDIX ESTIMATION RESULTS......Page 681
BIBLIOGRAPHY......Page 683
1 Introduction......Page 685
2 A competitive Marshallian sector model of an economy......Page 687
3 Sector model based on a generalized production function......Page 690
BIBLIOGRAPHY......Page 692
1 Introduction......Page 695
2 The Marshallian macroeconomic model (MMM)......Page 697
3.1 Notation and equations......Page 700
3.2 Estimation techniques......Page 702
3.3 Forecasting techniques......Page 704
4 Discussion of data and forecasting results......Page 706
5 Summary and conclusions......Page 720
BIBLIOGRAPHY......Page 722
Subject index......Page 725
Author index......Page 730