Essays in Econometrics: Collected Papers of Clive W. J. Granger (Econometric Society Monographs Vol 1)

Cover......Page 1
Half-title......Page 3
Series-title......Page 5
Title......Page 7
Copyright......Page 8
Dedication......Page 9
Contents......Page 11
BLACKWELL PUBLISHERS......Page 15
THE ECONOMETRICS SOCIETY......Page 16
ELSEVIER......Page 17
TAYLOR & FRANCIS, LTD.......Page 18
Contributors......Page 19
SPECTRAL METHODS......Page 23
SEASONALITY......Page 25
NONLINEARITY......Page 26
METHODOLOGY......Page 28
FORECASTING......Page 30
CAUSALITY......Page 32
INTEGRATION AND COINTEGRATION......Page 34
LONG MEMORY......Page 40
REFERENCES......Page 41
CHAPTER 1 The ET Interview: Professor Clive Granger......Page 50
BOOKS......Page 87
PAPERS......Page 88
Submitted......Page 99
PRICE RESEARCH......Page 100
SPECULATIVE MARKETS AND THEORY OF FINANCE......Page 101
STATISTICAL THEORY AND APPLIED STATISTICS......Page 102
PART ONE SPECTRAL ANALYSIS......Page 105
1. THE RANDOM WALK HYPOTHESIS......Page 107
2. SPECTRAL METHODS......Page 110
3. RESULTS OF THE ANALYSIS......Page 114
Some Technical Considerations......Page 121
Power Spectra......Page 123
Cross Spectra......Page 124
1. INTRODUCTION......Page 128
2. EXAMPLES OF ESTIMATED SPECTRA......Page 129
3. THE PROBLEM OF TREND......Page 131
4. INTERPRETATION: BUSINESS CYCLES......Page 133
5. DESCRIPTION: MODEL FITTING......Page 135
6. IMPLICATIONS FOR MODEL BUILDING......Page 136
7. IMPLICATIONS FOR CONTROL......Page 138
REFERENCES......Page 139
PART TWO SEASONALITY......Page 141
1.1 Calendar......Page 143
1.4 Expectation......Page 144
2. DEFINITION......Page 146
2.2 Definition 2......Page 147
3.1 Model 1......Page 148
3.3 Model 3......Page 149
3.6 Model 6......Page 150
4. DECOMPOSITION......Page 152
5. WHY ADJUST?......Page 154
6. OVERVIEW OF ADJUSTMENT METHODS......Page 156
7.1 Property 1......Page 159
7.3 Property 3......Page 160
7.4 Property 4......Page 161
7.6 Property 6......Page 162
8. EFFECTS OF ADJUSTMENT IN PRACTICE......Page 163
9. RELATING PAIRS OF ADJUSTED SERIES......Page 164
10. CONCLUSIONS......Page 166
REFERENCES......Page 167
CHAPTER 5 Is Seasonal Adjustment a Linear or Nonlinear Data-Filtering Process?......Page 169
1.1 The Decompositions......Page 171
1.2 Multiplicative Versus Additive......Page 172
1.3 Outlier Detections......Page 173
1.4 Moving Average Filter Selection......Page 174
2. A SIMULATION STUDY......Page 175
2.1 The Data-Generating Processes......Page 176
2.2 Properties of Linear Approximations......Page 178
2.4 Technical Details......Page 181
3. SIMULATION AND EMPIRICAL RESULTS......Page 185
3.1 Seasonal Filtering and Linear Regression......Page 186
3.2 Simulation Evidence on Properties of Linear Approximation......Page 187
3.3 An Empirical Investigation......Page 189
4. CONCLUSION......Page 191
ACKNOWLEDGMENTS......Page 192
REFERENCES......Page 193
PART THREE NONLINEARITY......Page 197
1. NON-LINEAR MODELS......Page 199
2. INSTANTANEOUS DATA TRANSFORMATIONS......Page 201
3. INTRODUCTION TO BILINEAR MODELS......Page 205
4. PARTICULAR CASE 1: A DIAGONAL MODEL......Page 207
5. PARTICULAR CASE 2: WHITE NOISE MODELS......Page 208
REFERENCES......Page 209
1. INTRODUCTION......Page 210
2. CORRELATION EXPONENT TABLES......Page 212
3. THE BDS TEST......Page 221
4. CONCLUSIONS......Page 226
REFERENCES......Page 227
1. INTRODUCTION......Page 230
2. THE NEURAL NETWORK TEST......Page 232
3.1 The Keenan, Tsay, and Ramsey RESET Tests......Page 234
3.2 The White Dynamic Information Matrix Test......Page 235
3.5 The Bispectrum Test......Page 236
4.1 Block1......Page 237
4.2 Block2......Page 238
5. RESULTS OF THE SIMULATION......Page 239
6. TESTS ON ACTUAL ECONOMIC TIME SERIES......Page 246
REFERENCES......Page 249
1. INTRODUCTION......Page 252
2. BALANCE OF AN EQUATION WITH SIMPLE NONLINEARITY......Page 254
3. ALTERNATIVE DEFINITIONS OF EXTENDED MEMORY......Page 260
4. TESTING FOR LINEARITY......Page 262
5. NONLINEAR MODELING......Page 265
6. CONCLUSIONS......Page 266
REFERENCES......Page 267
1. INTRODUCTION......Page 269
2. THE NONPARAMETRIC REGRESSION MODEL......Page 271
3. THE DATA AND SOME MODIFICATIONS......Page 275
4. RESULTS......Page 278
REFERENCES......Page 289
PART FOUR METHODOLOGY......Page 293
1. INTRODUCTION......Page 295
2. THE SUM OF TWO INDEPENDENT SERIES......Page 298
3. SERIES AGGREGATION AND OBSERVATIONAL ERROR MODELS......Page 301
4. TIME AGGREGATION, NON-INTEGER LAGS AND FEEDBACK MODELS......Page 302
5. REALIZABILITY OF SIMPLE MODELS......Page 303
6. SIMULATION OF OBSERVATION ERROR MODELS......Page 305
REFERENCES......Page 309
1. A DEFINITION OF INVERTIBILITY......Page 311
2. LINEAR MODELS......Page 312
3. A CLASS OF NON-INVERTIBLE MODELS......Page 314
4. BILINEAR MODELS......Page 315
5. CONCLUSIONS......Page 316
REFERENCES......Page 317
CHAPTER 13 Near Normality and Some Econometric Models......Page 318
REFERENCES......Page 322
1. TWO PHILOSOPHIES......Page 324
2. NONSENSE REGRESSIONS......Page 327
3. PREWHITENING......Page 330
4. BUILDING BIVARIATE FEEDBACK MODELS......Page 333
5. CONCLUSIONS......Page 338
1. INTRODUCTION......Page 339
2. THE CONTROL MECHANISM......Page 340
2.1 Test 1......Page 342
2.2 Test 2......Page 343
2.6 Test 6......Page 344
3. AN APPLICATION TO A MODEL OF THE UNEMPLOYMENT RATE......Page 345
4. AN APPLICATION TO TWO MODELS OF THE DEMAND FOR BORROWED RESERVES......Page 348
5. AN APPLICATION TO A MODEL FOR THE DEMAND FOR NARROW MONEY IN THE UNITED KINGDOM......Page 353
APPENDIX. DESCRIPTION OF THE DATA......Page 355
REFERENCES......Page 356
1. INTRODUCTION......Page 358
2. COMMON FACTORS, INDIVIDUAL FACTORS, AND MODEL SIMPLIFICATION......Page 361
3. AN EXAMPLE: THE ARBITRAGE PRICING THEORY MODEL......Page 364
4. EXPECTATIONS......Page 365
5. SOME PRACTICAL CONSIDERATIONS......Page 367
APPENDIX: NONLINEAR MODELS......Page 369
REFERENCES......Page 372
PART FIVE FORECASTING......Page 375
MATHEMATICAL THEORY......Page 377
Correlation Between Tides and Flow Over the Year......Page 379
The Distribution of Tide Heights......Page 380
Distribution of Flows......Page 381
Flood-Producing Flow/Tide Combinations......Page 383
The Probabilities of High Flows Occurring......Page 384
Final Results and Conclusion......Page 385
Statistical Concepts Used in the Paper......Page 386
INTRODUCTION......Page 388
QUADRATIC ERROR COST AND THE GAUSSIAN PROCESS......Page 389
LINEAR COST FUNCTION......Page 390
GENERAL SYMMETRIC COST FUNCTIONS......Page 391
NON-SYMMETRIC COST FUNCTIONS......Page 392
SOME PRACTICAL CONSEQUENCES OF THE RESULTS......Page 393
SUMMARY AND CONCLUSIONS......Page 394
Proofs of two Theorems......Page 395
REFERENCES......Page 396
1. INTRODUCTION......Page 397
2. UNIVARIATE TIME SERIES PREDICTION......Page 399
3. COST FUNCTIONS......Page 400
4. RANKING FORECASTS ON A LEAST SQUARES BASIS......Page 402
5. FORECAST EFFICIENCY......Page 403
6. HOW GOOD IS A PARTICULAR SET OF FORECASTS?......Page 406
7. DIAGNOSTIC CHECKS ON FORECAST PERFORMANCE......Page 409
REFERENCES......Page 411
INTRODUCTION......Page 413
CHOICE OF METHOD FOR DETERMINING WEIGHTS......Page 415
DESIRABLE PROPERTIES OF METHODS......Page 416
PERFORMANCE OF DIFFERENT METHODS......Page 418
MINOR MODIFICATIONS TO METHODS......Page 422
COMMENTS......Page 423
CONCLUSIONS......Page 424
A1 Combining an Arithmetic and a Logarithmic Forecast......Page 425
A2 The Relationship Between the Combined Forecast Variance and the Variances of the Original Forecast Errors......Page 426
Harrison......Page 428
REFERENCES......Page 431
THE BEGINNINGS......Page 433
SIMPLE EXTENSIONS......Page 435
FURTHER EXTENSIONS......Page 437
REFERENCES......Page 440
1. INTRODUCTION......Page 442
3.1 Switching Regression Models in Which the Regime is Indicated by the Lagged Forecast Error......Page 444
3.2 Switching Regression Models in Which the Regime is Indicated by an Economically Relevant Variable......Page 448
4. SMOOTH TRANSITION REGRESSION MODELS......Page 449
5. A FURTHER EMPIRICAL EXAMPLE......Page 450
6. A COMPARISON OF THE IN-SAMPLE AND OUT-OF-SAMPLE PERFORMANCE OF THE ALTERNATIVE COMBINING METHODS......Page 454
7. CONCLUSION......Page 455
REFERENCES......Page 456
1. INTRODUCTION......Page 458
2. AUTOCORRELATION PROPERTIES OF TRANSFORMED SERIES: THE STATIONARY CASE......Page 459
3. AUTOCORRELATION PROPERTIES OF TRANSFORMED SERIES: INTEGRATED PROCESSES......Page 463
4. FORECASTING TRANSFORMED VARIABLES......Page 466
5. CONCLUSIONS......Page 474
Properties of Hermite Polynomials......Page 475
REFERENCES......Page 478
1. INTRODUCTION......Page 479
2. CAUSAL VARIABLES......Page 480
3. INSTANTANEOUS TRANSFORMATIONS......Page 481
4. BILINEAR MODELS......Page 483
5. NORMED MARKOV CHAINS......Page 484
6. TRULY CHAOTIC MODELS......Page 486
7. NONLINEAR MOVING AVERAGE MODELS......Page 487
8. TIME-VARYING PARAMETER MODELS......Page 488
9. NUMERICAL ILLUSTRATIONS......Page 490
REFERENCES......Page 492
1. CRITICISMS OF FORECASTS......Page 494
2. REPRESENTING FORECAST UNCERTAINTY......Page 500
3. ALL SWING TOGETHER......Page 502
4. UNDERESTIMATING CHANGE......Page 504
5. HOW FAR AHEAD CAN WE FORECAST......Page 505
6. TOO MUCH EMPHASIS ON OLD DATA?......Page 506
7. THE USE OF FORECAST ERRORS......Page 508
8. CHOOSING WHAT WE FORECAST – USING LEADING INDICATORS......Page 509
9. CONDITIONAL OR UNCONDITIONAL FORECASTS?......Page 513
10. FORECASTING DURING A STRUCTURAL BREAK......Page 514
Suggestions to Improve Forecasts......Page 515
ACKNOWLEDGEMENTS......Page 516
REFERENCES......Page 517
1. INTRODUCTION......Page 519
2. METHODOLOGIES......Page 520
3. DESCRIPTION OF THE PRESENT PROJECT......Page 521
4.1 The Basic Strategy......Page 522
4.2 An Example for HOUR1......Page 524
4.3 Models Across the Hours......Page 525
4.4 Statistical Analysis......Page 526
5. ADAPTIVE MODELS......Page 529
6.1 The 1990–91 Forecast Results......Page 530
6.2 The 1991–92 Forecast Results......Page 532
7. CONCLUSIONS......Page 536
REFERENCES......Page 537
Index......Page 539