Forecasting in the presence of structural breaks and model uncertainty:
Saved in:
| Format: | Book |
|---|---|
| Language: | English |
| Published: |
Bingley [u.a.]
Emerald
2008
|
| Edition: | 1. ed. |
| Series: | Frontiers of economics and globalization
3 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | XXVII, 661 S. graph. Darst. 24 cm |
| ISBN: | 9780444529428 |
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| 245 | 1 | 0 | |a Forecasting in the presence of structural breaks and model uncertainty |c ed. by David E. Rapach ... |
| 250 | |a 1. ed. | ||
| 264 | 1 | |a Bingley [u.a.] |b Emerald |c 2008 | |
| 300 | |a XXVII, 661 S. |b graph. Darst. |c 24 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Frontiers of economics and globalization |v 3 | |
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Economic forecasting | |
| 650 | 4 | |a Macroeconomics / Econometric models | |
| 650 | 7 | |a Prognoses |2 gtt | |
| 650 | 4 | |a Ökonometrisches Modell | |
| 650 | 4 | |a Economic forecasting | |
| 650 | 4 | |a Macroeconomics |x Econometric models | |
| 650 | 7 | |a Prognoses. |2 gtt | |
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| 689 | 0 | 2 | |a Prognoseverfahren |0 (DE-588)4358095-6 |D s |
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Record in the Search Index
| _version_ | 1817681155469606912 |
|---|---|
| adam_text |
FRONTIERS OF ECONOMICS AND GLOBALIZATION VOLUME 3 FORECASTING IN THE
PRESENCE OF STRUCTURAL BREAKS AND MODEL UNCERTAINTY EDITED BY DAVID E.
RAPACH SAINT LOUIS UNIVERSITY, SAINT LOUIS, MO, USA MARK E. WOHAR
UNIVERSITY OF NEBRASKA AT OMAHA, OMAHA, NE, USA UNITED KINGDOM - NORTH
AMERICA - JAPAN - INDIA - MALAYSIA - CHINA CONTENTS FRONTIERS OF
ECONOMICS AND GLOBALIZATION V LIST OF CONTRIBUTORS (IN ALPHABETICAL
ORDER) VII EDITORS' INTRODUCTION XXI DAVID E. RAPACH AND MARK E. WOHAR
ACKNOWLEDGMENTS XXVI REFERENCES XXVI PART I: MACROECONOMIC FORECASTING 1
CHAPTER 1 FORECASTING ANNUAL UK INFLATION USING AN ECONOMETRIC MODEL
OVER 1875-1991 3 MICHAEL P. CLEMENTS AND DAVID E HENDRY 1 INTRODUCTION 3
2 ASPECTS OF A GENERAL THEORY OF FORECASTING 7 2.1 STRUCTURAL BREAKS 8
2.2 ROBUSTIFYING FORECASTS AGAINST STRUCTURAL BREAKS 9 2.3 OTHER
IMPLICATIONS 12 3 THE MODEL OF UK INFLATION 14 3.1 THE DATA SERIES 14
3.2 FEEDBACK RELATIONS 15 3.3 THE SELECTED MODEL 16 4 INDIVIDUAL
FORECASTING METHODS - 17 4.1 1982-1991 18 4.2 COMBINED FORECASTING
METHODS 22 5 SUMMARY COMPARISONS 24 5.1 "FORECASTING" 1982-1991 25 5.2
"FORECASTING" 1972-1981 27 6 POLICY IMPLICATIONS 28 7 CONCLUSIONS 29
ACKNOWLEDGMENTS 30 APPENDIX A: DATA DEFINITIONS 30 XII CONTENTS APPENDIX
B: MODEL ESTIMATES 32 LAGGED VARIANTS OF THE SELECTED FULL-SAMPLE MODEL
32 PCGETS FORECASTING MODELS 32 REFERENCES 33 CHAPTER 2 FORECASTING UK
INFLATION: THE ROLES OF STRUCTURAL BREAKS AND TIME DISAGGREGATION 41
JENNIFER L. CASTLE AND DAVID F. HENDRY 1 INTRODUCTION 41 2 QUARTERLY
MODELS OF UK INFLATION 43 2.1 DATA 44 2.2 SINGLE-EQUATION
EQUILIBRIUM-CORRECTION MODELS 47 2.3 VECTOR EQUILIBRIUM-CORRECTION
MODELS 49 3 QUARTERLY FORECASTING MODELS -51 3.1 DIRECT VERSUS ITERATED
FORECASTS 54 3.2 FORECAST EVALUATION CRITERIA 55 4 QUARTERLY INFLATION
FORECASTS 56 4.1 FORECASTING RESULTS 57 4.2 RANKING OF FORECASTING
MODELS 64 5 FORECASTING ANNUAL INFLATION 65 5.1 DISAGGREGATING FORECASTS
OVER TIME 67 5.2 DDD TAXONOMY 74 6 ANNUAL FORECASTING MODELS 76 6.1
MODELS OF ANNUAL INFLATION 77 6.2 QUARTERLY MODEL OF ANNUAL INFLATION 78
6.3 QUARTERLY INFLATION MODEL USED TO FORECAST 1-YEAR-AHEAD INFLATION 79
7 ANNUAL INFLATION FORECASTS 80 8 CONCLUSIONS 83 ACKNOWLEDGEMENTS 84
APPENDIX A 84 A. 1 AUTOMATIC GETS SELECTION 84 A.2 FORECASTING MODELS 86
REFERENCES 89 CHAPTER 3 FORECASTING WITH SMALL MACROECONOMIC VARS IN THE
PRESENCE OF INSTABILITIES 93 TODD E. CLARK AND MICHAEL W. MCCRACKEN 1
INTRODUCTION 93 2 METHODS USED 95 CONTENTS XIII 3 DATA AND MODEL DETAILS
106 4 RESULTS 109 4.1 FORECAST ACCURACY 110 4.2 LONG-RUN FORECASTS 134 5
CONCLUSION 141 ACKNOWLEDGMENTS 142 REFERENCES 143 CHAPTER 4 FORECASTING
MACROECONOMIC VARIABLES USING DIFFUSION INDEXES IN SHORT SAMPLES WITH
STRUCTURAL CHANGE 149 ANINDYA BANERJEE, MASSIMILIANO MARCELLINO AND IGOR
MASTEN 1 INTRODUCTION 149 2 METHODOLOGY 151 2.1 FORECASTING MODELS 152
2.2 FORECAST COMPARISON 154 3 MONTE CARLO EXPERIMENTS 154 3.1 DESIGN OF
EXPERIMENTS 155 3.2 RESULTS 158 3.3 EXPLAINING THE GOOD PERFORMANCE OF
AR FORECASTS 168 3.4 SUMMARY 171 4 TWO EMPIRICAL EXAMPLES 171 4.1 DATA
171 4.2 FORECASTING RESULTS FOR THE EURO AREA 172 4.3 FORECASTING
RESULTS FOR SLOVENIA 185 4.4 SUMMARY 189 5 CONCLUSIONS 191
ACKNOWLEDGMENTS 192 REFERENCES 192 CHAPTER 5 PREDICTIVE INFERENCE UNDER
MODEL MISSPECIFICATION 195 NII AYI ARMAH AND NORMAN R. SWANSON 1
INTRODUCTION 195 2 BLOCK BOOTSTRAPS FOR RECURSIVE AND ROLLING
M-ESTIMATORS 199 2.1 RECURSIVE ESTIMATION WINDOW 199 2.2 ROLLING
ESTIMATION WINDOW 203 3 THE CS TEST 205 4 MONTE CARLO EXPERIMENTS 209 5
EMPIRICAL ILLUSTRATION: THE MARGINAL PREDICTIVE CONTENT OF MONEY FOR
OUTPUT 220 XIV CONTENTS 6 CONCLUDING REMARKS 227 ACKNOWLEDGMENTS 228
REFERENCES 228 CHAPTER 6 FORECASTING PERSISTENT DATA WITH POSSIBLE
STRUCTURAL BREAKS: OLD SCHOOL AND NEW SCHOOL LESSONS USING OECD
UNEMPLOYMENT RATES 231 WALTER ENDERS AND RUXANDRA PRODAN 1 INTRODUCTION
231 2 FORECASTING WITH STRUCTURAL BREAKS 232 3 TIME-SERIES PROPERTIES OF
THE UNEMPLOYMENT RATES 237 4 SPECIFICS OF THE ESTIMATED MODELS 243 4.1
"OLD SCHOOL" MODELS 243 4.2 "NEW SCHOOL" MODELS 244 4.3 NONLINEAR
MODELS: TAR AND M-TAR 246 5 COMPARATIVE PERFORMANCE OF THE MODELS 247
5.1 FORECAST PERFORMANCE OF THE MODELS WITH AN EXPANDING WINDOW 248 5.2
NEW SCHOOL METHODS USING ONLY POST-BREAK DATA 250 5.3 FORECASTING
PERFORMANCE OF THE TAR AND M-TAR MODELS 251 5.4 FORECASTING PERFORMANCE
WITH INTERCEPT CORRECTIONS 252 5.5 ENCOMPASSING: COMBINING OLD SCHOOL
AND NEW SCHOOL MODELS 254 6 CONCLUSION 256 APPENDIX A 257 REFERENCES 268
CHAPTER 7 WHAT CAN WE LEARN FROM COMPREHENSIVE DATA REVISIONS FOR
FORECASTING INFLATION? SOME US EVIDENCE 27 1 PIERRE L. SIKLOS 1
INTRODUCTION 271 2 METHODOLOGICAL ISSUES 274 3 DATA AND EMPIRICAL
EVIDENCE 279 3.1 DATA 279 3.2 SOME STYLIZED FACTS 280 3.3 COINTEGRATION
AND COINTEGRATION BREAKDOWN TESTS 286 4 BENCHMARK REVISIONS AND
INFLATION FORECASTS 288 5 CONCLUSIONS 297 ACKNOWLEDGEMENTS 297
REFERENCES 297 CONTENTS XV PART II: FINANCIAL FORECASTING 301 CHAPTER 8
ESTIMATING AND FORECASTING GARCH MODELS IN THE PRESENCE OF STRUCTURAL
BREAKS AND REGIME SWITCHES 303 ERIC HILLEBRAND AND MARCELO C. MEDEIROS 1
INTRODUCTION 303 2 THE STATISTICAL CONSEQUENCES OF NEGLECTING PARAMETER
CHANGES 305 3 THE FLEXIBLE COEFFICIENT GARCH(1, 1) MODEL 309 3.1
OVERVIEW 309 3.2 PARAMETER ESTIMATION 312 3.3 DETERMINING THE NUMBER OF
REGIMES . 313 4 FORECAST COMPARISON OF LOCALLY STATIONARY GARCH(1, 1)
VS. FCGARCH(M) MODELS . 316 4.1 DATA 316 4.2 LOCALGARCH(L, 1) MODELS 317
4.3 FCGARCH( 1,1) MODELS 317 4.4 FORECAST COMPARISON 321 5 CONCLUSION
324 REFERENCES 324 CHAPTER 9 A SOURCE OF LONG MEMORY IN VOLATILITY 329
NAMWON HYUNG, SER-HUANG POON AND CLIVE W.J. GRANGER 1 INTRODUCTION 329 2
HOW MUCH VOLATILITY MEMORY IS THERE? 332 3 LONG-MEMORY VOLATILITY MODELS
336 3.1 FI(E)GARCH MODELS 336 3.2 BREAKS AND STRUCTURAL CHANGE 338 3.3
VOLATILITY REGIME-SWITCHING MODEL 339 4 EMPIRICAL STUDY 340 4.1
SHORT-MEMORY MODELS 341 4.2 ESTIMATION 342 4.3 OUT-OF-SAMPLE FORECASTS
344 4.4 FORECAST EVALUATION 350 4.5 RESULTS 351 5 THE CASE FOR
VOLATILITY BREAKS 363 5.1 ECONOMIC SIGNIFICANCE 363 5.2 INTERNATIONAL
EVIDENCE 366 5.3 CAUSES OF VOLATILITY BREAKS 369 5.4 DISTINGUISHING
BREAKS AND JUMPS 372 6 CONCLUSION 373 XVI CONTENTS ACKNOWLEDGMENTS 374
APPENDIX A: QUADRATIC AND BIPOWER VARIATION 374 REFERENCES 376 CHAPTER
10 FORECASTING STOCK RETURN VOLATILITY IN THE PRESENCE OF STRUCTURAL
BREAKS 381 DAVID E. RAPACH, JACK K. STRAUSS AND MARK E. WOHAR 1
INTRODUCTION 381 2 ECONOMETRIC METHODOLOGY 385 2.1 MODIFIED ITERATIVE
CUMULATIVE SUM OF SQUARES ALGORITHM 385 2.2 GJR-GARCH MODEL . 386 2.3
IN-SAMPLE TESTS 387 2.4 FORECASTING MODELS 388 2.5 COMBINATION FORECASTS
390 2.6 FORECAST EVALUATION 392 3 EMPIRICAL RESULTS 393 3.1 DATA 393 3.2
IN-SAMPLE RESULTS 395 3.3 OUT-OF-SAMPLE RESULTS 402 4 CONCLUSION 412
ACKNOWLEDGMENTS 413 REFERENCES 414 CHAPTER 11 FINANCIAL TIME SERIES AND
VOLATILITY PREDICTION USING NOVAS TRANSFORMATIONS 417 DIMITRIS N.
POLITIS AND DIMITRIOS D. THOMAKOS 1 INTRODUCTION 417 2 NOVAS
TRANSFORMATION AND IMPLIED DISTRIBUTIONS 419 3 NOVAS DISTRIBUTIONAL
MATCHING 425 3.1 PARAMETRIZATION 425 3.2 OBJECTIVE FUNCTIONS FOR
OPTIMIZATION 427 4 NOVAS FORECASTING 430 5 EMPIRICAL EXAMPLES 432 5.1
DATA, DATA-GENERATING PROCESSES, AND SUMMARY STATISTICS 432 5.2 NOVAS
OPTIMIZATION AND FORECASTING SPECIFICATIONS 434 5.3 DISCUSSION OF
RESULTS 438 6 CONCLUDING REMARKS 444 ACKNOWLEDGMENTS 445 REFERENCES 446
CONTENTS XVII CHAPTER 12 MODELING FOREIGN EXCHANGE RATES WITH JUMPS 449
JOHN M. MAHEU AND THOMAS H. MCCURDY 1 INTRODUCTION 449 2 BASIC JUMP
MODEL 452 2.1 HETEROGENEOUS JUMP PARAMETERIZATION 452 3 BENCHMARK
SPECIFICATIONS 453 3.1 SV MODEL 453 3.2 GARCH MODEL 454 4 POSTERIOR
INFERENCE 454 5 MODEL COMPARISON 455 5.1 CALCULATIONS * 457 6 VOLATILITY
FORECASTS 459 6.1 CALCULATIONS 459 7 DATA S 460 8 RESULTS 461 9
CONCLUSIONS 468 ACKNOWLEDGEMENTS 468 APPENDIX A 469 A.I JUMP MODEL 469
A.2 SV MODEL 471 A.3 GARCH MODEL 472 REFERENCES 472 CHAPTER 13 BAGGING
BINARY AND QUANTILE PREDICTORS FOR TIME SERIES: FURTHER ISSUES 477
TAE-HWY LEE AND YANG YANG 1 INTRODUCTION 478 2 WHAT IS BAGGING? 480 3
BAGGING WITH DIFFERENT AVERAGING SCHEMES 482 4 BAGGING MULTI-STEP
QUANTILE FORECASTS 497 5 BAGGING QUANTILE FORECASTS WITH DIFFERENT TICK
LOSSES 516 6 BAGGING QUANTILE FORECASTS WITH DIFFERENT ESTIMATION
ALGORITHMS 519 7 BAGGING QUANTILE FORECASTS WITH DIFFERENT QUANTILE
REGRESSION MODELS 521 8 BAGGING BINARY AND QUANTILE FORECASTS IN
DIFFERENT FREQUENCIES , 526 9 PRETESTING AND BAGGING 526 10 SUMMARY AND
CONCLUSION 530 ACKNOWLEDGEMENTS 532 REFERENCES 532 XVIII CONTENTS
CHAPTER 14 FORECASTING INTEREST RATES: AN APPLICATION OF THE STOCHASTIC
UNIT ROOT AND STOCHASTIC COINTEGRATION FRAMEWORKS 535 ROBERT SOLLIS 1
INTRODUCTION 535 2 STOCHASTIC UNIT ROOTS, HETEROSCEDASTIC INTEGRATION
AND STOCHASTIC COINTEGRATION 538 2.1 STOCHASTIC UNIT ROOTS 538 2.2
TESTING FOR A STUR 541 2.3 HETEROSCEDASTIC INTEGRATION, STOCHASTIC
COINTEGRATION AND HETEROSCEDASTIC COINTEGRATION 542 3 DATA, UNIT ROOT
TESTS AND STUR RESULTS 546 3.1 DATA . 546 3.2 UNIT ROOT TEST RESULTS 546
3.3 ESTIMATED STUR MODELS AND FORECASTS 548 4 HI, SC AND HC TEST RESULTS
552 5 SC FORECASTING RESULTS 554 6 CONCLUSIONS 555 REFERENCES 557
CHAPTER 15 BAYESIAN MODEL AVERAGING IN THE PRESENCE OF STRUCTURAL BREAKS
56 1 FRANCESCO RAVAZZOLO, RICHARD PAAP, DICK VAN DIJK AND PHILIP HANS
FRANSES 1 INTRODUCTION 2 METHODOLOGY 2.1 THE MODEL 2.2 PRIOR
SPECIFICATION AND POSTERIOR SIMULATION 2.3 USING THE POSTERIOR RESULTS 3
MODEL UNCERTAINTY AND STRUCTURAL BREAKS IN RETURN FORECASTING MODELS FOR
THE S&P 500 3.1 DATA 3.2 PRIOR SPECIFICATION 3.3 FULL-SAMPLE ESTIMATION
RESULTS 4 ACTIVE INVESTMENT STRATEGIES ALLOWING FOR MODEL UNCERTAINTY
AND STRUCTURAL BREAKS 4.1 A UTILITY-BASED PERFORMANCE MEASURE 4.2
EMPIRICAL RESULTS 5 CONCLUSION ACKNOWLEDGEMENTS REFERENCES 561 563 563
565 567 569 569 570 570 579 579 582 590 592 592 CONTENTS XI X CHAPTER 16
THE ECONOMIC AND STATISTICAL VALUE OF FORECAST COMBINATIONS UNDER REGIME
SWITCHING: AN APPLICATION TO PREDICTABLE US RETURNS 595 MASSIMO GUIDOLIN
AND CARRIE FANGZHOU NA 1 INTRODUCTION 595 2 MODELS 599 3 DATA 602 4
ECONOMETRIC ESTIMATES 604 4.1 MODEL SELECTION 604 4.2 A FOUR-STATE MODEL
608 5 FORECASTING PERFORMANCE 618 5.1 MEAN SQUARE FORECAST ERROR RESULTS
620 5.2 TESTING DIFFERENTIAL PREDICTIVE ACCURACY 623 6 ARE FORECAST
COMBINATIONS USEFUL? STATISTICAL EVIDENCE 628 6.1 THE FORECAST
COMBINATION PROBLEM 629 6.2 RECURSIVE ESTIMATES OF COMBINATION WEIGHTS
631 6.3 STATISTICAL TESTS 633 7 THE ECONOMIC VALUE OF FORECAST
COMBINATIONS: PORTFOLIO IMPLICATIONS 638 7.1 RECURSIVE PORTFOLIO WEIGHTS
640 7.2 OUT-OF-SAMPLE PORTFOLIO PERFORMANCE 641 7.3 SUB-SAMPLE
PERFORMANCE 646 7.4 TRANSACTION COSTS 646 8 CONCLUSION 649
ACKNOWLEDGMENTS 651 REFERENCES 651 SUBJECT INDEX 657 |
| adam_txt |
FRONTIERS OF ECONOMICS AND GLOBALIZATION VOLUME 3 FORECASTING IN THE
PRESENCE OF STRUCTURAL BREAKS AND MODEL UNCERTAINTY EDITED BY DAVID E.
RAPACH SAINT LOUIS UNIVERSITY, SAINT LOUIS, MO, USA MARK E. WOHAR
UNIVERSITY OF NEBRASKA AT OMAHA, OMAHA, NE, USA UNITED KINGDOM - NORTH
AMERICA - JAPAN - INDIA - MALAYSIA - CHINA CONTENTS FRONTIERS OF
ECONOMICS AND GLOBALIZATION V LIST OF CONTRIBUTORS (IN ALPHABETICAL
ORDER) VII EDITORS' INTRODUCTION XXI DAVID E. RAPACH AND MARK E. WOHAR
ACKNOWLEDGMENTS XXVI REFERENCES XXVI PART I: MACROECONOMIC FORECASTING 1
CHAPTER 1 FORECASTING ANNUAL UK INFLATION USING AN ECONOMETRIC MODEL
OVER 1875-1991 3 MICHAEL P. CLEMENTS AND DAVID E HENDRY 1 INTRODUCTION 3
2 ASPECTS OF A GENERAL THEORY OF FORECASTING 7 2.1 STRUCTURAL BREAKS 8
2.2 ROBUSTIFYING FORECASTS AGAINST STRUCTURAL BREAKS 9 2.3 OTHER
IMPLICATIONS 12 3 THE MODEL OF UK INFLATION 14 3.1 THE DATA SERIES 14
3.2 FEEDBACK RELATIONS 15 3.3 THE SELECTED MODEL 16 4 INDIVIDUAL
FORECASTING METHODS - 17 4.1 1982-1991 18 4.2 COMBINED FORECASTING
METHODS 22 5 SUMMARY COMPARISONS 24 5.1 "FORECASTING" 1982-1991 25 5.2
"FORECASTING" 1972-1981 27 6 POLICY IMPLICATIONS 28 7 CONCLUSIONS 29
ACKNOWLEDGMENTS 30 APPENDIX A: DATA DEFINITIONS 30 XII CONTENTS APPENDIX
B: MODEL ESTIMATES 32 LAGGED VARIANTS OF THE SELECTED FULL-SAMPLE MODEL
32 PCGETS FORECASTING MODELS 32 REFERENCES 33 CHAPTER 2 FORECASTING UK
INFLATION: THE ROLES OF STRUCTURAL BREAKS AND TIME DISAGGREGATION 41
JENNIFER L. CASTLE AND DAVID F. HENDRY 1 INTRODUCTION 41 2 QUARTERLY
MODELS OF UK INFLATION 43 2.1 DATA 44 2.2 SINGLE-EQUATION
EQUILIBRIUM-CORRECTION MODELS 47 2.3 VECTOR EQUILIBRIUM-CORRECTION
MODELS 49 3 QUARTERLY FORECASTING MODELS -51 3.1 DIRECT VERSUS ITERATED
FORECASTS 54 3.2 FORECAST EVALUATION CRITERIA 55 4 QUARTERLY INFLATION
FORECASTS 56 4.1 FORECASTING RESULTS 57 4.2 RANKING OF FORECASTING
MODELS 64 5 FORECASTING ANNUAL INFLATION 65 5.1 DISAGGREGATING FORECASTS
OVER TIME 67 5.2 DDD TAXONOMY 74 6 ANNUAL FORECASTING MODELS 76 6.1
MODELS OF ANNUAL INFLATION 77 6.2 QUARTERLY MODEL OF ANNUAL INFLATION 78
6.3 QUARTERLY INFLATION MODEL USED TO FORECAST 1-YEAR-AHEAD INFLATION 79
7 ANNUAL INFLATION FORECASTS 80 8 CONCLUSIONS 83 ACKNOWLEDGEMENTS 84
APPENDIX A 84 A. 1 AUTOMATIC GETS SELECTION 84 A.2 FORECASTING MODELS 86
REFERENCES 89 CHAPTER 3 FORECASTING WITH SMALL MACROECONOMIC VARS IN THE
PRESENCE OF INSTABILITIES 93 TODD E. CLARK AND MICHAEL W. MCCRACKEN 1
INTRODUCTION 93 2 METHODS USED 95 CONTENTS XIII 3 DATA AND MODEL DETAILS
106 4 RESULTS 109 4.1 FORECAST ACCURACY 110 4.2 LONG-RUN FORECASTS 134 5
CONCLUSION 141 ACKNOWLEDGMENTS 142 REFERENCES 143 CHAPTER 4 FORECASTING
MACROECONOMIC VARIABLES USING DIFFUSION INDEXES IN SHORT SAMPLES WITH
STRUCTURAL CHANGE 149 ANINDYA BANERJEE, MASSIMILIANO MARCELLINO AND IGOR
MASTEN 1 INTRODUCTION 149 2 METHODOLOGY 151 2.1 FORECASTING MODELS 152
2.2 FORECAST COMPARISON 154 3 MONTE CARLO EXPERIMENTS 154 3.1 DESIGN OF
EXPERIMENTS 155 3.2 RESULTS 158 3.3 EXPLAINING THE GOOD PERFORMANCE OF
AR FORECASTS 168 3.4 SUMMARY 171 4 TWO EMPIRICAL EXAMPLES 171 4.1 DATA
171 4.2 FORECASTING RESULTS FOR THE EURO AREA 172 4.3 FORECASTING
RESULTS FOR SLOVENIA 185 4.4 SUMMARY 189 5 CONCLUSIONS 191
ACKNOWLEDGMENTS 192 REFERENCES 192 CHAPTER 5 PREDICTIVE INFERENCE UNDER
MODEL MISSPECIFICATION 195 NII AYI ARMAH AND NORMAN R. SWANSON 1
INTRODUCTION 195 2 BLOCK BOOTSTRAPS FOR RECURSIVE AND ROLLING
M-ESTIMATORS 199 2.1 RECURSIVE ESTIMATION WINDOW 199 2.2 ROLLING
ESTIMATION WINDOW 203 3 THE CS TEST 205 4 MONTE CARLO EXPERIMENTS 209 5
EMPIRICAL ILLUSTRATION: THE MARGINAL PREDICTIVE CONTENT OF MONEY FOR
OUTPUT 220 XIV CONTENTS 6 CONCLUDING REMARKS 227 ACKNOWLEDGMENTS 228
REFERENCES 228 CHAPTER 6 FORECASTING PERSISTENT DATA WITH POSSIBLE
STRUCTURAL BREAKS: OLD SCHOOL AND NEW SCHOOL LESSONS USING OECD
UNEMPLOYMENT RATES 231 WALTER ENDERS AND RUXANDRA PRODAN 1 INTRODUCTION
231 2 FORECASTING WITH STRUCTURAL BREAKS 232 3 TIME-SERIES PROPERTIES OF
THE UNEMPLOYMENT RATES 237 4 SPECIFICS OF THE ESTIMATED MODELS 243 4.1
"OLD SCHOOL" MODELS 243 4.2 "NEW SCHOOL" MODELS 244 4.3 NONLINEAR
MODELS: TAR AND M-TAR 246 5 COMPARATIVE PERFORMANCE OF THE MODELS 247
5.1 FORECAST PERFORMANCE OF THE MODELS WITH AN EXPANDING WINDOW 248 5.2
NEW SCHOOL METHODS USING ONLY POST-BREAK DATA 250 5.3 FORECASTING
PERFORMANCE OF THE TAR AND M-TAR MODELS 251 5.4 FORECASTING PERFORMANCE
WITH INTERCEPT CORRECTIONS 252 5.5 ENCOMPASSING: COMBINING OLD SCHOOL
AND NEW SCHOOL MODELS 254 6 CONCLUSION 256 APPENDIX A 257 REFERENCES 268
CHAPTER 7 WHAT CAN WE LEARN FROM COMPREHENSIVE DATA REVISIONS FOR
FORECASTING INFLATION? SOME US EVIDENCE 27 1 PIERRE L. SIKLOS 1
INTRODUCTION 271 2 METHODOLOGICAL ISSUES 274 3 DATA AND EMPIRICAL
EVIDENCE 279 3.1 DATA 279 3.2 SOME STYLIZED FACTS 280 3.3 COINTEGRATION
AND COINTEGRATION BREAKDOWN TESTS 286 4 BENCHMARK REVISIONS AND
INFLATION FORECASTS 288 5 CONCLUSIONS 297 ACKNOWLEDGEMENTS 297
REFERENCES 297 CONTENTS XV PART II: FINANCIAL FORECASTING 301 CHAPTER 8
ESTIMATING AND FORECASTING GARCH MODELS IN THE PRESENCE OF STRUCTURAL
BREAKS AND REGIME SWITCHES 303 ERIC HILLEBRAND AND MARCELO C. MEDEIROS 1
INTRODUCTION 303 2 THE STATISTICAL CONSEQUENCES OF NEGLECTING PARAMETER
CHANGES 305 3 THE FLEXIBLE COEFFICIENT GARCH(1, 1) MODEL 309 3.1
OVERVIEW 309 3.2 PARAMETER ESTIMATION 312 3.3 DETERMINING THE NUMBER OF
REGIMES . 313 4 FORECAST COMPARISON OF LOCALLY STATIONARY GARCH(1, 1)
VS. FCGARCH(M) MODELS . 316 4.1 DATA 316 4.2 LOCALGARCH(L, 1) MODELS 317
4.3 FCGARCH( 1,1) MODELS 317 4.4 FORECAST COMPARISON 321 5 CONCLUSION
324 REFERENCES 324 CHAPTER 9 A SOURCE OF LONG MEMORY IN VOLATILITY 329
NAMWON HYUNG, SER-HUANG POON AND CLIVE W.J. GRANGER 1 INTRODUCTION 329 2
HOW MUCH VOLATILITY MEMORY IS THERE? 332 3 LONG-MEMORY VOLATILITY MODELS
336 3.1 FI(E)GARCH MODELS 336 3.2 BREAKS AND STRUCTURAL CHANGE 338 3.3
VOLATILITY REGIME-SWITCHING MODEL 339 4 EMPIRICAL STUDY 340 4.1
SHORT-MEMORY MODELS 341 4.2 ESTIMATION 342 4.3 OUT-OF-SAMPLE FORECASTS
344 4.4 FORECAST EVALUATION 350 4.5 RESULTS 351 5 THE CASE FOR
VOLATILITY BREAKS 363 5.1 ECONOMIC SIGNIFICANCE 363 5.2 INTERNATIONAL
EVIDENCE 366 5.3 CAUSES OF VOLATILITY BREAKS 369 5.4 DISTINGUISHING
BREAKS AND JUMPS 372 6 CONCLUSION 373 XVI CONTENTS ACKNOWLEDGMENTS 374
APPENDIX A: QUADRATIC AND BIPOWER VARIATION 374 REFERENCES 376 CHAPTER
10 FORECASTING STOCK RETURN VOLATILITY IN THE PRESENCE OF STRUCTURAL
BREAKS 381 DAVID E. RAPACH, JACK K. STRAUSS AND MARK E. WOHAR 1
INTRODUCTION 381 2 ECONOMETRIC METHODOLOGY 385 2.1 MODIFIED ITERATIVE
CUMULATIVE SUM OF SQUARES ALGORITHM 385 2.2 GJR-GARCH MODEL . 386 2.3
IN-SAMPLE TESTS 387 2.4 FORECASTING MODELS 388 2.5 COMBINATION FORECASTS
390 2.6 FORECAST EVALUATION 392 3 EMPIRICAL RESULTS 393 3.1 DATA 393 3.2
IN-SAMPLE RESULTS 395 3.3 OUT-OF-SAMPLE RESULTS 402 4 CONCLUSION 412
ACKNOWLEDGMENTS 413 REFERENCES 414 CHAPTER 11 FINANCIAL TIME SERIES AND
VOLATILITY PREDICTION USING NOVAS TRANSFORMATIONS 417 DIMITRIS N.
POLITIS AND DIMITRIOS D. THOMAKOS 1 INTRODUCTION 417 2 NOVAS
TRANSFORMATION AND IMPLIED DISTRIBUTIONS 419 3 NOVAS DISTRIBUTIONAL
MATCHING 425 3.1 PARAMETRIZATION 425 3.2 OBJECTIVE FUNCTIONS FOR
OPTIMIZATION 427 4 NOVAS FORECASTING 430 5 EMPIRICAL EXAMPLES 432 5.1
DATA, DATA-GENERATING PROCESSES, AND SUMMARY STATISTICS 432 5.2 NOVAS
OPTIMIZATION AND FORECASTING SPECIFICATIONS 434 5.3 DISCUSSION OF
RESULTS 438 6 CONCLUDING REMARKS 444 ACKNOWLEDGMENTS 445 REFERENCES 446
CONTENTS XVII CHAPTER 12 MODELING FOREIGN EXCHANGE RATES WITH JUMPS 449
JOHN M. MAHEU AND THOMAS H. MCCURDY 1 INTRODUCTION 449 2 BASIC JUMP
MODEL 452 2.1 HETEROGENEOUS JUMP PARAMETERIZATION 452 3 BENCHMARK
SPECIFICATIONS 453 3.1 SV MODEL 453 3.2 GARCH MODEL 454 4 POSTERIOR
INFERENCE 454 5 MODEL COMPARISON 455 5.1 CALCULATIONS * 457 6 VOLATILITY
FORECASTS 459 6.1 CALCULATIONS 459 7 DATA S 460 8 RESULTS 461 9
CONCLUSIONS 468 ACKNOWLEDGEMENTS 468 APPENDIX A 469 A.I JUMP MODEL 469
A.2 SV MODEL 471 A.3 GARCH MODEL 472 REFERENCES 472 CHAPTER 13 BAGGING
BINARY AND QUANTILE PREDICTORS FOR TIME SERIES: FURTHER ISSUES 477
TAE-HWY LEE AND YANG YANG 1 INTRODUCTION 478 2 WHAT IS BAGGING? 480 3
BAGGING WITH DIFFERENT AVERAGING SCHEMES 482 4 BAGGING MULTI-STEP
QUANTILE FORECASTS 497 5 BAGGING QUANTILE FORECASTS WITH DIFFERENT TICK
LOSSES 516 6 BAGGING QUANTILE FORECASTS WITH DIFFERENT ESTIMATION
ALGORITHMS 519 7 BAGGING QUANTILE FORECASTS WITH DIFFERENT QUANTILE
REGRESSION MODELS 521 8 BAGGING BINARY AND QUANTILE FORECASTS IN
DIFFERENT FREQUENCIES , 526 9 PRETESTING AND BAGGING 526 10 SUMMARY AND
CONCLUSION 530 ACKNOWLEDGEMENTS 532 REFERENCES 532 XVIII CONTENTS
CHAPTER 14 FORECASTING INTEREST RATES: AN APPLICATION OF THE STOCHASTIC
UNIT ROOT AND STOCHASTIC COINTEGRATION FRAMEWORKS 535 ROBERT SOLLIS 1
INTRODUCTION 535 2 STOCHASTIC UNIT ROOTS, HETEROSCEDASTIC INTEGRATION
AND STOCHASTIC COINTEGRATION 538 2.1 STOCHASTIC UNIT ROOTS 538 2.2
TESTING FOR A STUR 541 2.3 HETEROSCEDASTIC INTEGRATION, STOCHASTIC
COINTEGRATION AND HETEROSCEDASTIC COINTEGRATION 542 3 DATA, UNIT ROOT
TESTS AND STUR RESULTS 546 3.1 DATA . 546 3.2 UNIT ROOT TEST RESULTS 546
3.3 ESTIMATED STUR MODELS AND FORECASTS 548 4 HI, SC AND HC TEST RESULTS
552 5 SC FORECASTING RESULTS 554 6 CONCLUSIONS 555 REFERENCES 557
CHAPTER 15 BAYESIAN MODEL AVERAGING IN THE PRESENCE OF STRUCTURAL BREAKS
56 1 FRANCESCO RAVAZZOLO, RICHARD PAAP, DICK VAN DIJK AND PHILIP HANS
FRANSES 1 INTRODUCTION 2 METHODOLOGY 2.1 THE MODEL 2.2 PRIOR
SPECIFICATION AND POSTERIOR SIMULATION 2.3 USING THE POSTERIOR RESULTS 3
MODEL UNCERTAINTY AND STRUCTURAL BREAKS IN RETURN FORECASTING MODELS FOR
THE S&P 500 3.1 DATA 3.2 PRIOR SPECIFICATION 3.3 FULL-SAMPLE ESTIMATION
RESULTS 4 ACTIVE INVESTMENT STRATEGIES ALLOWING FOR MODEL UNCERTAINTY
AND STRUCTURAL BREAKS 4.1 A UTILITY-BASED PERFORMANCE MEASURE 4.2
EMPIRICAL RESULTS 5 CONCLUSION ACKNOWLEDGEMENTS REFERENCES 561 563 563
565 567 569 569 570 570 579 579 582 590 592 592 CONTENTS XI X CHAPTER 16
THE ECONOMIC AND STATISTICAL VALUE OF FORECAST COMBINATIONS UNDER REGIME
SWITCHING: AN APPLICATION TO PREDICTABLE US RETURNS 595 MASSIMO GUIDOLIN
AND CARRIE FANGZHOU NA 1 INTRODUCTION 595 2 MODELS 599 3 DATA 602 4
ECONOMETRIC ESTIMATES 604 4.1 MODEL SELECTION 604 4.2 A FOUR-STATE MODEL
608 5 FORECASTING PERFORMANCE 618 5.1 MEAN SQUARE FORECAST ERROR RESULTS
620 5.2 TESTING DIFFERENTIAL PREDICTIVE ACCURACY 623 6 ARE FORECAST
COMBINATIONS USEFUL? STATISTICAL EVIDENCE 628 6.1 THE FORECAST
COMBINATION PROBLEM 629 6.2 RECURSIVE ESTIMATES OF COMBINATION WEIGHTS
631 6.3 STATISTICAL TESTS 633 7 THE ECONOMIC VALUE OF FORECAST
COMBINATIONS: PORTFOLIO IMPLICATIONS 638 7.1 RECURSIVE PORTFOLIO WEIGHTS
640 7.2 OUT-OF-SAMPLE PORTFOLIO PERFORMANCE 641 7.3 SUB-SAMPLE
PERFORMANCE 646 7.4 TRANSACTION COSTS 646 8 CONCLUSION 649
ACKNOWLEDGMENTS 651 REFERENCES 651 SUBJECT INDEX 657 |
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| id | DE-604.BV023477944 |
| illustrated | Illustrated |
| index_date | 2024-07-02T21:36:53Z |
| indexdate | 2024-12-06T09:03:53Z |
| institution | BVB |
| isbn | 9780444529428 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016660150 |
| oclc_num | 845390168 |
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| owner_facet | DE-12 DE-945 |
| physical | XXVII, 661 S. graph. Darst. 24 cm |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Emerald |
| record_format | marc |
| series | Frontiers of economics and globalization |
| series2 | Frontiers of economics and globalization |
| spelling | Forecasting in the presence of structural breaks and model uncertainty ed. by David E. Rapach ... 1. ed. Bingley [u.a.] Emerald 2008 XXVII, 661 S. graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Frontiers of economics and globalization 3 Includes bibliographical references and index Economic forecasting Macroeconomics / Econometric models Prognoses gtt Ökonometrisches Modell Macroeconomics Econometric models Prognoses. gtt Makroökonomisches Modell (DE-588)4074486-3 gnd rswk-swf Prognoseverfahren (DE-588)4358095-6 gnd rswk-swf Statistisches Modell (DE-588)4121722-6 gnd rswk-swf Makroökonomisches Modell (DE-588)4074486-3 s Statistisches Modell (DE-588)4121722-6 s Prognoseverfahren (DE-588)4358095-6 s b DE-604 Rapach, David E. Sonstige (DE-588)171710746 oth Frontiers of economics and globalization 3 (DE-604)BV022878481 3 GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016660150&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Forecasting in the presence of structural breaks and model uncertainty Frontiers of economics and globalization Economic forecasting Macroeconomics / Econometric models Prognoses gtt Ökonometrisches Modell Macroeconomics Econometric models Prognoses. gtt Makroökonomisches Modell (DE-588)4074486-3 gnd Prognoseverfahren (DE-588)4358095-6 gnd Statistisches Modell (DE-588)4121722-6 gnd |
| subject_GND | (DE-588)4074486-3 (DE-588)4358095-6 (DE-588)4121722-6 |
| title | Forecasting in the presence of structural breaks and model uncertainty |
| title_auth | Forecasting in the presence of structural breaks and model uncertainty |
| title_exact_search | Forecasting in the presence of structural breaks and model uncertainty |
| title_exact_search_txtP | Forecasting in the presence of structural breaks and model uncertainty |
| title_full | Forecasting in the presence of structural breaks and model uncertainty ed. by David E. Rapach ... |
| title_fullStr | Forecasting in the presence of structural breaks and model uncertainty ed. by David E. Rapach ... |
| title_full_unstemmed | Forecasting in the presence of structural breaks and model uncertainty ed. by David E. Rapach ... |
| title_short | Forecasting in the presence of structural breaks and model uncertainty |
| title_sort | forecasting in the presence of structural breaks and model uncertainty |
| topic | Economic forecasting Macroeconomics / Econometric models Prognoses gtt Ökonometrisches Modell Macroeconomics Econometric models Prognoses. gtt Makroökonomisches Modell (DE-588)4074486-3 gnd Prognoseverfahren (DE-588)4358095-6 gnd Statistisches Modell (DE-588)4121722-6 gnd |
| topic_facet | Economic forecasting Macroeconomics / Econometric models Prognoses Ökonometrisches Modell Macroeconomics Econometric models Prognoses. Makroökonomisches Modell Prognoseverfahren Statistisches Modell |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016660150&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV022878481 |
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