Econometric forecasting and high-frequency data analysis
editors, Roberto S. Mariano, Yiu-Kuen Tse.
Bok Engelsk 2008 · Electronic books.
| Utgitt | Hackensack, NJ : : World Scientific, , c2008.
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| Omfang | 1 online resource (200 p.)
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| Opplysninger | Description based upon print version of record.. - 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. - 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. - 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. - 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. - This important book consists of surveys of high-frequency financial data analysis and econometric forecasting, written by pioneers in these areas including Nobel laureate Lawrence Klein. Some of the chapters were presented as tutorials to an audience in the Econometric Forecasting and High-Frequency Data Analysis Workshop at the Institute for Mathematical Science, National University of Singapore in May 2006. They will be of interest to researchers working in macroeconometrics as well as financial econometrics. Moreover, readers will find these chapters useful as a guide to the literature as
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| ISBN | 9789812778956
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