Spectral analysis, seasonality, nonlinearity, methodology and forecasting : collected papers of Clive W.J. Granger / / edited by Eric Ghysels, Norman R. Swanson, Mark W. Watson

• Autore: Granger C. W. J (Clive William John), <1934-2009.>
• Pubblicazione: Cambridge ; ; New York, : Cambridge University Press, 2001
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (xix, 523 pages) : digital, PDF file(s)
• Disciplina: 330/.01/5195
• Soggetto topico: Econometrics; Economics, Mathematical
• Altri autori: GhyselsEric <1956-> SwansonNorman R <1964-> (Norman Rasmus) WatsonMark W
• Note generali: Title from publisher's bibliographic system (viewed on 05 Oct 2015).
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Cover -- Half-title -- Series-title -- Title -- Copyright -- Dedication -- Contents -- Acknowledgments -- ACADEMIC PRESS -- AMERICAN STATISTICAL ASSOCIATION -- BLACKWELL PUBLISHERS -- BUREAU OF THE CENSUS -- CAMBRIDGE UNIVERSITY PRESS -- CHARTERED INSTITUTION OF WATER AND ENVIRONMENTAL MANAGEMENT -- THE ECONOMETRICS SOCIETY -- ELSEVIER -- FEDERAL RESERVE BANK OF MINNEAPOLIS -- HELBING AND LICHTENHAHN VERLAG -- JOHN WILEY & -- SONS, LTD. -- MACMILLAN PUBLISHERS, LTD. -- MIT PRESS -- TAYLOR & -- FRANCIS, LTD. -- Contributors -- Introduction -- Volume I -- SPECTRAL METHODS -- SEASONALITY -- NONLINEARITY -- METHODOLOGY -- FORECASTING -- Volume II -- CAUSALITY -- INTEGRATION AND COINTEGRATION -- LONG MEMORY -- REFERENCES -- CHAPTER 1 The ET Interview: Professor Clive Granger -- BOOKS -- PAPERS -- Forthcoming -- Submitted -- PRICE RESEARCH -- SPECULATIVE MARKETS AND THEORY OF FINANCE -- STATISTICAL THEORY AND APPLIED STATISTICS -- PART ONE SPECTRAL ANALYSIS -- CHAPTER 2 Spectral Analysis of New York Stock Market Prices -- Summary -- 1. THE RANDOM WALK HYPOTHESIS -- 2. SPECTRAL METHODS -- 3. RESULTS OF THE ANALYSIS -- APPENDIX A -- Some Technical Considerations -- APPENDIX B -- Description of Series Analyzed -- Power Spectra -- Cross Spectra -- CHAPTER 3 The Typical Spectral Shape of an Economic Variable -- 1. INTRODUCTION -- 2. EXAMPLES OF ESTIMATED SPECTRA -- 3. THE PROBLEM OF TREND -- 4. INTERPRETATION: BUSINESS CYCLES -- 5. DESCRIPTION: MODEL FITTING -- 6. IMPLICATIONS FOR MODEL BUILDING -- 7. IMPLICATIONS FOR CONTROL -- REFERENCES -- PART TWO SEASONALITY -- CHAPTER 4 Seasonality: Causation, Interpretation, and Implications -- 1. CAUSES OF SEASONALITY -- 1.1 Calendar -- 1.2 Timing Decisions -- 1.3 Weather -- 1.4 Expectation -- 2. DEFINITION -- 2.1 Definition 1 -- 2.2 Definition 2 -- 2.3 Definition 3 -- 3. SEASONAL MODELS -- 3.1 Model 1. ; 3.2 Model 2 -- 3.3 Model 3 -- 3.4 Model 4 -- 3.5 Model 5 -- 3.6 Model 6 -- 4. DECOMPOSITION -- 5. WHY ADJUST? -- 6. OVERVIEW OF ADJUSTMENT METHODS -- 7. CRITERIA FOR EVALUATION -- 7.1 Property 1 -- 7.2 Property 2 -- 7.3 Property 3 -- 7.4 Property 4 -- 7.5 Property 5 -- 7.6 Property 6 -- 8. EFFECTS OF ADJUSTMENT IN PRACTICE -- 9. RELATING PAIRS OF ADJUSTED SERIES -- 10. CONCLUSIONS -- REFERENCES -- CHAPTER 5 Is Seasonal Adjustment a Linear or Nonlinear Data-Filtering Process? -- 1. ON POTENTIAL SOURCES OF NONLINEARITY IN THE X-11 PROGRAM -- 1.1 The Decompositions -- 1.2 Multiplicative Versus Additive -- 1.3 Outlier Detections -- 1.4 Moving Average Filter Selection -- 1.5 Aggregation -- 2. A SIMULATION STUDY -- 2.1 The Data-Generating Processes -- 2.2 Properties of Linear Approximations -- 2.3 Linear Regression and Filtering -- 2.4 Technical Details -- 3. SIMULATION AND EMPIRICAL RESULTS -- 3.1 Seasonal Filtering and Linear Regression -- 3.2 Simulation Evidence on Properties of Linear Approximation -- 3.3 An Empirical Investigation -- 4. CONCLUSION -- ACKNOWLEDGMENTS -- REFERENCES -- PART THREE NONLINEARITY -- CHAPTER 6 Non-Linear Time Series Modeling -- 1. NON-LINEAR MODELS -- 2. INSTANTANEOUS DATA TRANSFORMATIONS -- 3. INTRODUCTION TO BILINEAR MODELS -- 4. PARTICULAR CASE 1: A DIAGONAL MODEL -- 5. PARTICULAR CASE 2: WHITE NOISE MODELS -- REFERENCES -- CHAPTER 7 Using the Correlation Exponent to Decide Whether an Economic Series is Chaotic -- Summary -- 1. INTRODUCTION -- 2. CORRELATION EXPONENT TABLES -- 3. THE BDS TEST -- 4. CONCLUSIONS -- ACKNOWLEDGEMENT -- REFERENCES -- CHAPTER 8 Testing for Neglected Nonlinearity in Time Series Models -- 1. INTRODUCTION -- 2. THE NEURAL NETWORK TEST -- 3. ALTERNATIVE TESTS -- 3.1 The Keenan, Tsay, and Ramsey RESET Tests -- 3.2 The White Dynamic Information Matrix Test -- 3.3 The McLeod and Li Test. ; 3.4 The BDS Test -- 3.5 The Bispectrum Test -- 4. THE SIMULATION DESIGN -- 4.1 Block1 -- 4.2 Block2 -- 4.3 Bivariate Models -- 5. RESULTS OF THE SIMULATION -- 6. TESTS ON ACTUAL ECONOMIC TIME SERIES -- 7. CONCLUSIONS -- REFERENCES -- CHAPTER 9 Modeling Nonlinear Relationships Between Extended-Memory Variables -- 1. INTRODUCTION -- 2. BALANCE OF AN EQUATION WITH SIMPLE NONLINEARITY -- 3. ALTERNATIVE DEFINITIONS OF EXTENDED MEMORY -- 4. TESTING FOR LINEARITY -- 5. NONLINEAR MODELING -- 6. CONCLUSIONS -- REFERENCES -- CHAPTER 10 Semiparametric Estimates of the Relation Between Weather and Electricity Sales -- 1. INTRODUCTION -- 2. THE NONPARAMETRIC REGRESSION MODEL -- 3. THE DATA AND SOME MODIFICATIONS -- 4. RESULTS -- 5. CONCLUSIONS -- REFERENCES -- PART FOUR METHODOLOGY -- CHAPTER 11 Time Series Modeling and Interpretation -- Summary -- 1. INTRODUCTION -- 2. THE SUM OF TWO INDEPENDENT SERIES -- 3. SERIES AGGREGATION AND OBSERVATIONAL ERROR MODELS -- 4. TIME AGGREGATION, NON-INTEGER LAGS AND FEEDBACK MODELS -- 5. REALIZABILITY OF SIMPLE MODELS -- 6. SIMULATION OF OBSERVATION ERROR MODELS -- REFERENCES -- CHAPTER 12 On the Invertibility of Time Series Models -- 1. A DEFINITION OF INVERTIBILITY -- 2. LINEAR MODELS -- 3. A CLASS OF NON-INVERTIBLE MODELS -- 4. BILINEAR MODELS -- 5. CONCLUSIONS -- REFERENCES -- CHAPTER 13 Near Normality and Some Econometric Models -- REFERENCES -- CHAPTER 14 The Time Series Approach to Econometric Model Building -- 1. TWO PHILOSOPHIES -- 2. NONSENSE REGRESSIONS -- 3. PREWHITENING -- 4. BUILDING BIVARIATE FEEDBACK MODELS -- 5. CONCLUSIONS -- CHAPTER 15 Comments on the Evaluation of Policy Models -- Abstract -- 1. INTRODUCTION -- 2. THE CONTROL MECHANISM -- 2.1 Test 1 -- 2.2 Test 2 -- 2.3 Test 3 -- 2.4 Test 4 -- 2.5 Test 5 -- 2.6 Test 6 -- 2.7 Discussion -- 3. AN APPLICATION TO A MODEL OF THE UNEMPLOYMENT RATE. ; 4. AN APPLICATION TO TWO MODELS OF THE DEMAND FOR BORROWED RESERVES -- 5. AN APPLICATION TO A MODEL FOR THE DEMAND FOR NARROW MONEY IN THE UNITED KINGDOM -- 6. CONCLUSION -- APPENDIX. DESCRIPTION OF THE DATA -- REFERENCES -- CHAPTER 16 Implications of Aggregation with Common Factors -- 1. INTRODUCTION -- 2. COMMON FACTORS, INDIVIDUAL FACTORS, AND MODEL SIMPLIFICATION -- 3. AN EXAMPLE: THE ARBITRAGE PRICING THEORY MODEL -- 4. EXPECTATIONS -- 5. SOME PRACTICAL CONSIDERATIONS -- 6. SUMMING UP -- APPENDIX: NONLINEAR MODELS -- REFERENCES -- PART FIVE FORECASTING -- CHAPTER 17 Estimating the Probability of Flooding on a Tidal River -- Abstract -- MATHEMATICAL THEORY -- PROBABILITY OF FLOODING AT GAINSBOROUGH -- Introduction -- Correlation Between Tides and Flow Over the Year -- The Distribution of Tide Heights -- Distribution of Flows -- Flood-Producing Flow/Tide Combinations -- The Probabilities of High Flows Occurring -- Final Results and Conclusion -- ACKNOWLEDGEMENTS -- APPENDIX -- Statistical Concepts Used in the Paper -- CHAPTER 18 Prediction with a Generalized Cost of Error Function -- INTRODUCTION -- QUADRATIC ERROR COST AND THE GAUSSIAN PROCESS -- LINEAR COST FUNCTION -- GENERAL SYMMETRIC COST FUNCTIONS -- NON-SYMMETRIC COST FUNCTIONS -- SOME PRACTICAL CONSEQUENCES OF THE RESULTS -- SUMMARY AND CONCLUSIONS -- APPENDIX -- Proofs of two Theorems -- REFERENCES -- CHAPTER 19 Some Comments on the Evaluation of Economic Forecasts -- 1. INTRODUCTION -- 2. UNIVARIATE TIME SERIES PREDICTION -- 3. COST FUNCTIONS -- 4. RANKING FORECASTS ON A LEAST SQUARES BASIS -- 5. FORECAST EFFICIENCY -- 6. HOW GOOD IS A PARTICULAR SET OF FORECASTS? -- 7. DIAGNOSTIC CHECKS ON FORECAST PERFORMANCE -- 8. CONCLUSIONS -- REFERENCES -- CHAPTER 20 The Combination of Forecasts -- INTRODUCTION -- CHOICE OF METHOD FOR DETERMINING WEIGHTS -- DESIRABLE PROPERTIES OF METHODS. ; PERFORMANCE OF DIFFERENT METHODS -- MINOR MODIFICATIONS TO METHODS -- COMBINING FORECASTS FROM THE OUTSET -- COMMENTS -- CONCLUSIONS -- APPENDIX -- A1 Combining an Arithmetic and a Logarithmic Forecast -- A2 The Relationship Between the Combined Forecast Variance and the Variances of the Original Forecast Errors -- A3 The Distribution of... -- A4 Results of Combining Forecasts of the Airline Passenger Data -- Brown -- Box-Jenkins -- Harrison -- Constant -- Changing -- REFERENCES -- CHAPTER 21 Invited Review Combining Forecasts - Twenty Years Later -- Abstract -- THE BEGINNINGS -- SIMPLE EXTENSIONS -- FURTHER EXTENSIONS -- CONCLUSION -- REFERENCES -- CHAPTER 22 The Combination of Forecasts Using Changing Weights -- Abstract -- 1. INTRODUCTION -- 2. SWITCHING REGRESSION MODELS AND THEIR APPLICATION TO THE COMBINATION OF FORECASTS -- 3. COMBINING FORECASTS USING SWITCHING REGRESSION MODELS -- 3.1 Switching Regression Models in Which the Regime is Indicated by the Lagged Forecast Error -- 3.2 Switching Regression Models in Which the Regime is Indicated by an Economically Relevant Variable -- 4. SMOOTH TRANSITION REGRESSION MODELS -- 5. A FURTHER EMPIRICAL EXAMPLE -- 6. A COMPARISON OF THE IN-SAMPLE AND OUT-OF-SAMPLE PERFORMANCE OF THE ALTERNATIVE COMBINING METHODS -- 7. CONCLUSION -- APPENDIX -- REFERENCES -- CHAPTER 23 Forecasting Transformed Series -- Summary -- 1. INTRODUCTION -- 2. AUTOCORRELATION PROPERTIES OF TRANSFORMED SERIES: THE STATIONARY CASE -- 3. AUTOCORRELATION PROPERTIES OF TRANSFORMED SERIES: INTEGRATED PROCESSES -- 4. FORECASTING TRANSFORMED VARIABLES -- 5. CONCLUSIONS -- APPENDIX -- Properties of Hermite Polynomials -- ACKNOWLEDGEMENT -- REFERENCES -- CHAPTER 24 Forecasting White Noise -- 1. INTRODUCTION -- 2. CAUSAL VARIABLES -- 3. INSTANTANEOUS TRANSFORMATIONS -- 4. BILINEAR MODELS -- 5. NORMED MARKOV CHAINS. ; 6. TRULY CHAOTIC MODELS.
• Sommario/riassunto: This book, and its companion volume in the Econometric Society Monographs series (ESM number 33), present a collection of papers by Clive W. J. Granger. His contributions to economics and econometrics, many of them seminal, span more than four decades and touch on all aspects of time series analysis. The papers assembled in this volume explore topics in spectral analysis, seasonality, nonlinearity, methodology, and forecasting. Those in the companion volume investigate themes in causality, integration and cointegration, and long memory. The two volumes contain the original articles as well as an introduction written by the editors.
• Titolo autorizzato: Spectral analysis, seasonality, nonlinearity, methodology and forecasting
• ISBN: 1-139-88279-1; 0-511-06676-7; 1-280-16020-9; 0-511-11845-7; 1-139-14636-X; 0-511-06045-9; 0-511-29762-9; 0-511-75396-9; 0-511-06889-1
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910819062103321
• Lo trovi qui: Univ. Federico II
• Serie: Econometric Society monographs ; ; no. 32.