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Econometric forecasting and high-frequency data analysis [[electronic resource] /] / editors, Roberto S. Mariano, Yiu-Kuen Tse
| Econometric forecasting and high-frequency data analysis [[electronic resource] /] / editors, Roberto S. Mariano, Yiu-Kuen Tse |
| Pubbl/distr/stampa | Hackensack, NJ, : World Scientific, c2008 |
| Descrizione fisica | 1 online resource (200 p.) |
| Disciplina | 330.0112 |
| Altri autori (Persone) |
MarianoRoberto S
TseYiu Kuen <1952-> |
| Collana | Lecture notes series |
| Soggetto topico |
Econometrics
Finance - Econometric models |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-93790-8
9786611937904 981-277-896-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
CONTENTS; Foreword; Preface; Forecast Uncertainty, its Representation and Evaluation Kenneth F. Wallis; 1. Introduction; 1.1 Motivation; 1.2 Overview; A theoretical illustration; Example; Generalisations; Forecast evaluation; 2. Measuring and reporting forecast uncertainty; 2.1 Model-based measures of forecast uncertainty; The linear regression model; Estimation error in multi-step forecasts; Stochastic simulation in non-linear models; Loss functions; Model uncertainty; 2.2 Empirical measures of forecast uncertainty; 2.3 Reporting forecast uncertainty; Forecast intervals; Density forecasts
Graphical presentationsAdditional examples; 2.4 Forecast scenarios; 2.5 Uncertainty and disagreement in survey forecasts; 3. Evaluating interval and density forecasts; 3.1 Likelihood ratio tests of interval forecasts; 3.2 Chi-squared tests of interval forecasts; 3.3 Extensions to density forecasts; 3.4 The probability integral transformation; 3.5 The inverse normal transformation; 3.6 The Bank of England's inflation forecasts; 3.7 Comparing density forecasts; 4. Conclusion; References The University of Pennsylvania Models for High-Frequency Macroeconomic and Modeling Lawrence R. Klein and Suleyman Ozmucur1. Introduction; 2. The Methodology of the Current Quarter Model (CQM); 3. The Methodology of the Survey Corner8; 4. Conclusion; References; Forecasting Seasonal Time Series Philip Hans Franses; 1. Introduction; 2. Seasonal Time Series; How do seasonal time series look like?; What do we want to forecast?; Why is seasonal adjustment often problematic?; 3. Basic Models; The deterministic seasonality model; Seasonal random walk; Airline model; Basic structural model Conclusion4. Advanced Models; Seasonal unit roots; Testing for seasonal unit roots; Seasonal cointegration; Periodic models; Multivariate representation; Conclusion; 5. Recent Advances; Periodic GARCH; 6. Conclusion; References; Car and Affine Processes Christian Gourieroux; 1. Introduction; 2. Compound Autoregressive Processes and A ne Processes; 2.1. The Gaussian Autoregressive Process; 2.2. Definition of a Car Process; 2.3. Marginal Distribution; 2.4. Nonlinear Prediction Formulas; 2.5. Compounding Interpretation; 2.5.1. Integer Autoregressive Process 2.5.2. Nonnegative Continuous Variables2.6. Continuous Time A ne Processes; 3. Autoregressive Gamma Process; 3.1. Gamma Distribution; 3.1.1. Centered Gamma Distribution; 3.1.2. Noncentered Gamma Distribution; 3.1.3. Change of scale; 3.2. The Autoregressive Gamma Process; 3.3. Nonlinear Prediction Formula; 3.4. Link with the Cox, Ingersoll, Ross Process; 3.5. Extensions; 3.5.1. Autoregressive gamma process of order p; 4. Wishart Autoregressive Process; 4.1. The Outer Product of a Gaussian VAR(1) Process; 4.2. Extension to Stochastic Positive Definite Matrices; 4.3. Conditional Moments 4.4. Continuous Time Analogue |
| Record Nr. | UNINA-9910453193503321 |
| Hackensack, NJ, : World Scientific, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Econometric forecasting and high-frequency data analysis [[electronic resource] /] / editors, Roberto S. Mariano, Yiu-Kuen Tse
| Econometric forecasting and high-frequency data analysis [[electronic resource] /] / editors, Roberto S. Mariano, Yiu-Kuen Tse |
| Pubbl/distr/stampa | Hackensack, NJ, : World Scientific, c2008 |
| Descrizione fisica | 1 online resource (200 p.) |
| Disciplina | 330.0112 |
| Altri autori (Persone) |
MarianoRoberto S
TseYiu Kuen <1952-> |
| Collana | Lecture notes series |
| Soggetto topico |
Econometrics
Finance - Econometric models |
| ISBN |
1-281-93790-8
9786611937904 981-277-896-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
CONTENTS; Foreword; Preface; Forecast Uncertainty, its Representation and Evaluation Kenneth F. Wallis; 1. Introduction; 1.1 Motivation; 1.2 Overview; A theoretical illustration; Example; Generalisations; Forecast evaluation; 2. Measuring and reporting forecast uncertainty; 2.1 Model-based measures of forecast uncertainty; The linear regression model; Estimation error in multi-step forecasts; Stochastic simulation in non-linear models; Loss functions; Model uncertainty; 2.2 Empirical measures of forecast uncertainty; 2.3 Reporting forecast uncertainty; Forecast intervals; Density forecasts
Graphical presentationsAdditional examples; 2.4 Forecast scenarios; 2.5 Uncertainty and disagreement in survey forecasts; 3. Evaluating interval and density forecasts; 3.1 Likelihood ratio tests of interval forecasts; 3.2 Chi-squared tests of interval forecasts; 3.3 Extensions to density forecasts; 3.4 The probability integral transformation; 3.5 The inverse normal transformation; 3.6 The Bank of England's inflation forecasts; 3.7 Comparing density forecasts; 4. Conclusion; References The University of Pennsylvania Models for High-Frequency Macroeconomic and Modeling Lawrence R. Klein and Suleyman Ozmucur1. Introduction; 2. The Methodology of the Current Quarter Model (CQM); 3. The Methodology of the Survey Corner8; 4. Conclusion; References; Forecasting Seasonal Time Series Philip Hans Franses; 1. Introduction; 2. Seasonal Time Series; How do seasonal time series look like?; What do we want to forecast?; Why is seasonal adjustment often problematic?; 3. Basic Models; The deterministic seasonality model; Seasonal random walk; Airline model; Basic structural model Conclusion4. Advanced Models; Seasonal unit roots; Testing for seasonal unit roots; Seasonal cointegration; Periodic models; Multivariate representation; Conclusion; 5. Recent Advances; Periodic GARCH; 6. Conclusion; References; Car and Affine Processes Christian Gourieroux; 1. Introduction; 2. Compound Autoregressive Processes and A ne Processes; 2.1. The Gaussian Autoregressive Process; 2.2. Definition of a Car Process; 2.3. Marginal Distribution; 2.4. Nonlinear Prediction Formulas; 2.5. Compounding Interpretation; 2.5.1. Integer Autoregressive Process 2.5.2. Nonnegative Continuous Variables2.6. Continuous Time A ne Processes; 3. Autoregressive Gamma Process; 3.1. Gamma Distribution; 3.1.1. Centered Gamma Distribution; 3.1.2. Noncentered Gamma Distribution; 3.1.3. Change of scale; 3.2. The Autoregressive Gamma Process; 3.3. Nonlinear Prediction Formula; 3.4. Link with the Cox, Ingersoll, Ross Process; 3.5. Extensions; 3.5.1. Autoregressive gamma process of order p; 4. Wishart Autoregressive Process; 4.1. The Outer Product of a Gaussian VAR(1) Process; 4.2. Extension to Stochastic Positive Definite Matrices; 4.3. Conditional Moments 4.4. Continuous Time Analogue |
| Record Nr. | UNINA-9910782272503321 |
| Hackensack, NJ, : World Scientific, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Econometric forecasting and high-frequency data analysis / / editors, Roberto S. Mariano, Yiu-Kuen Tse
| Econometric forecasting and high-frequency data analysis / / editors, Roberto S. Mariano, Yiu-Kuen Tse |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Hackensack, NJ, : World Scientific, c2008 |
| Descrizione fisica | 1 online resource (200 p.) |
| Disciplina | 330.0112 |
| Altri autori (Persone) |
MarianoRoberto S
TseYiu Kuen <1952-> |
| Collana | Lecture notes series |
| Soggetto topico |
Econometrics
Finance - Econometric models |
| ISBN |
1-281-93790-8
9786611937904 981-277-896-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
CONTENTS; Foreword; Preface; Forecast Uncertainty, its Representation and Evaluation Kenneth F. Wallis; 1. Introduction; 1.1 Motivation; 1.2 Overview; A theoretical illustration; Example; Generalisations; Forecast evaluation; 2. Measuring and reporting forecast uncertainty; 2.1 Model-based measures of forecast uncertainty; The linear regression model; Estimation error in multi-step forecasts; Stochastic simulation in non-linear models; Loss functions; Model uncertainty; 2.2 Empirical measures of forecast uncertainty; 2.3 Reporting forecast uncertainty; Forecast intervals; Density forecasts
Graphical presentationsAdditional examples; 2.4 Forecast scenarios; 2.5 Uncertainty and disagreement in survey forecasts; 3. Evaluating interval and density forecasts; 3.1 Likelihood ratio tests of interval forecasts; 3.2 Chi-squared tests of interval forecasts; 3.3 Extensions to density forecasts; 3.4 The probability integral transformation; 3.5 The inverse normal transformation; 3.6 The Bank of England's inflation forecasts; 3.7 Comparing density forecasts; 4. Conclusion; References The University of Pennsylvania Models for High-Frequency Macroeconomic and Modeling Lawrence R. Klein and Suleyman Ozmucur1. Introduction; 2. The Methodology of the Current Quarter Model (CQM); 3. The Methodology of the Survey Corner8; 4. Conclusion; References; Forecasting Seasonal Time Series Philip Hans Franses; 1. Introduction; 2. Seasonal Time Series; How do seasonal time series look like?; What do we want to forecast?; Why is seasonal adjustment often problematic?; 3. Basic Models; The deterministic seasonality model; Seasonal random walk; Airline model; Basic structural model Conclusion4. Advanced Models; Seasonal unit roots; Testing for seasonal unit roots; Seasonal cointegration; Periodic models; Multivariate representation; Conclusion; 5. Recent Advances; Periodic GARCH; 6. Conclusion; References; Car and Affine Processes Christian Gourieroux; 1. Introduction; 2. Compound Autoregressive Processes and A ne Processes; 2.1. The Gaussian Autoregressive Process; 2.2. Definition of a Car Process; 2.3. Marginal Distribution; 2.4. Nonlinear Prediction Formulas; 2.5. Compounding Interpretation; 2.5.1. Integer Autoregressive Process 2.5.2. Nonnegative Continuous Variables2.6. Continuous Time A ne Processes; 3. Autoregressive Gamma Process; 3.1. Gamma Distribution; 3.1.1. Centered Gamma Distribution; 3.1.2. Noncentered Gamma Distribution; 3.1.3. Change of scale; 3.2. The Autoregressive Gamma Process; 3.3. Nonlinear Prediction Formula; 3.4. Link with the Cox, Ingersoll, Ross Process; 3.5. Extensions; 3.5.1. Autoregressive gamma process of order p; 4. Wishart Autoregressive Process; 4.1. The Outer Product of a Gaussian VAR(1) Process; 4.2. Extension to Stochastic Positive Definite Matrices; 4.3. Conditional Moments 4.4. Continuous Time Analogue |
| Record Nr. | UNINA-9910823017903321 |
| Hackensack, NJ, : World Scientific, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||