| Description |
1 online resource (xviii, 261 pages) : illustrations |
| Series |
Econometric society monographs ; 52 |
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Econometric Society monographs ; no. 52.
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| Contents |
880-01 Preface; Acronyms and Abbreviations; 1 Introduction; 1.1 Unobserved Components and Filters; 1.2 Independence, White Noise and Martingale Differences; 1.2.1 The Law of Iterated Expectations and Optimal Predictions; 1.2.2 Definitions and Properties; 1.3 Volatility; 1.3.1 Stochastic Volatility; 1.3.2 Generalized Autoregressive Conditional Heteroscedasticity; 1.3.3 Exponential GARCH; 1.3.4 Variance, Scale and Outliers; 1.3.5 Location/Scale Models; 1.4 Dynamic Conditional Score Models; 1.5 Distributions and Quantiles; 1.6 Plan of Book; 2 Statistical Distributions and Asymptotic Theory |
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880-01/(S Machine generated contents note: 1.1. Unobserved Components and Filters -- 1.2. Independence, White Noise and Martingale Differences -- 1.2.1. Law of Iterated Expectations and Optimal Predictions -- 1.2.2. Definitions and Properties -- 1.3. Volatility -- 1.3.1. Stochastic Volatility -- 1.3.2. Generalized Autoregressive Conditional Heteroscedasticity -- 1.3.3. Exponential GARCH -- 1.3.4. Variance, Scale and Outliers -- 1.3.5. Location/Scale Models -- 1.4. Dynamic Conditional Score Models -- 1.5. Distributions and Quantiles -- 1.6. Plan of Book -- 2.1. Distributions -- 2.1.1. Student's t Distribution -- 2.1.2. General Error Distribution -- 2.1.3. Beta Distribution -- 2.1.4. Gamma Distribution -- 2.2. Maximum Likelihood -- 2.2.1. Student's t Distribution -- 2.2.2. General Error Distribution -- 2.2.3. Gamma Distribution -- 2.2.4. Consistency and Asymptotic Normality* -- 2.3. Maximum Likelihood Estimation of Dynamic Conditional Score Models -- 2.3.1. Information Matrix Lemma -- 2.3.2. Information Matrix for the First-Order Model -- 2.3.3. Information Matrix with the δ Parameterization* -- 2.3.4. Asymptotic Distribution -- 2.3.5. Consistency and Asymptotic Normality* -- 2.3.6. Nonstationarity -- 2.3.7. Several Parameters -- 2.4. Higher Order Models* -- 2.5. Tests -- 2.5.1. Serial Correlation -- 2.5.2. Goodness of Fit of Distributions -- 2.5.3. Residuals -- 2.5.4. Model Fit -- 2.6. Explanatory Variables -- 3.1. Dynamic Student's t Location Model -- 3.2. Basic Properties -- 3.2.1. Generalization and Reduced Form -- 3.2.2. Moments of the Observations -- 3.2.3. Autocorrelation Function -- 3.3. Maximum Likelihood Estimation -- 3.3.1. Asymptotic Distribution of the Maximum Likelihood Estimator -- 3.3.2. Monte Carlo Experiments -- 3.3.3. Application to U.S. GDP -- 3.4. Parameter Restrictions* -- 3.5. Higher Order Models and the State Space Form* -- 3.5.1. Linear Gaussian Models and the Kalman Filter -- 3.5.2. DCS Model -- 3.5.3. QARMA Models -- 3.6. Trend and Seasonality -- 3.6.1. Local Level Model -- 3.6.2. Application to Weekly Hours of Employees in U.S. Manufacturing -- 3.6.3. Local Linear Trend -- 3.6.4. Stochastic Seasonal -- 3.6.5. Application to Rail Travel -- 3.6.6. QARIMA and Seasonal QARIMA Models* -- 3.7. Smoothing -- 3.7.1. Weights -- 3.7.2. Smoothing Recursions for Linear State Space Models -- 3.7.3. Smoothing Recursions for DCS Models -- 3.7.4. Conditional Mode Estimation and the Score -- 3.8. Forecasting -- 3.8.1. QARMA Models -- 3.8.2. State Space Form* -- 3.9. Components and Long Memory -- 3.10. General Error Distribution -- 3.11. Skew Distributions -- 3.11.1. How to Skew a Distribution -- 3.11.2. Dynamic Skew-t Location Model -- 4.1. Beta-t-EGARCH -- 4.2. Properties of Stationary Beta-t-EGARCH Models -- 4.2.1. Exponential GARCH -- 4.2.2. Moments -- 4.2.3. Autocorrelation Functions of Squares and Powers of Absolute Values -- 4.2.4. Autocorrelations and Kurtosis -- 4.3. Leverage Effects -- 4.4. Gamma-GED-EGARCH -- 4.5. Forecasting -- 4.5.1. Beta-t-EGARCH -- 4.5.2. Gamma-GED-EGARCH -- 4.5.3. Integrated Exponential Models -- 4.5.4. Predictive Distribution -- 4.6. Maximum Likelihood Estimation and Inference -- 4.6.1. Asymptotic Theory for Beta-t-EGARCH -- 4.6.2. Monte Carlo Experiments -- 4.6.3. Gamma-GED-EGARCH -- 4.6.4. Leverage -- 4.7. Beta-t-GARCH -- 4.7.1. Properties of First-Order Model -- 4.7.2. Leverage Effects -- 4.7.3. Link with Beta-t-EGARCH -- 4.7.4. Estimation and Inference -- 4.7.5. Gamma-GED-GARCH -- 4.8. Smoothing -- 4.9. Application to Hang Seng and Dow Jones -- 4.10. Two Component Models -- 4.11. Trends, Seasonals and Explanatory Variables in Volatility Equations -- 4.12. Changing Location -- 4.12.1. Explanatory Variables -- 4.12.2. Stochastic Location and Stochastic Scale -- 4.13. Testing for Changing Volatility and Leverage -- 4.13.1. Portmanteau Test for Changing Volatility -- 4.13.2. Martingale Difference Test -- 4.13.3. Leverage -- 4.13.4. Diagnostics -- 4.14. Skew Distributions -- 4.15. Time-Varying Skewness and Kurtosis* -- 5.1. General Properties -- 5.1.1. Heavy Tails -- 5.1.2. Moments and Autocorrelations -- 5.1.3. Forecasts -- 5.1.4. Asymptotic Distribution of Maximum Likelihood Estimators -- 5.2. Generalized Gamma Distribution -- 5.2.1. Moments -- 5.2.2. Forecasts -- 5.2.3. Maximum Likelihood Estimation -- 5.3. Generalized Beta Distribution -- 5.3.1. Log-Logistic Distribution -- 5.3.2. Moments, Autocorrelations and Forecasts -- 5.3.3. Maximum Likelihood Estimation -- 5.3.4. Burr Distribution -- 5.3.5. Generalized Pareto Distribution -- 5.3.6. F Distribution -- 5.4. Log-Normal Distribution -- 5.5. Monte Carlo Experiments -- 5.6. Leverage, Long Memory and Diurnal Variation -- 5.7. Tests and Model Selection -- 5.8. Estimating Volatility from the Range -- 5.8.1. Application to Paris CAC and Dow Jones -- 5.8.2. Range-EGARCH Model -- 5.9. Duration -- 5.10. Realized Volatility -- 5.11. Count Data and Qualitative Observations -- 6.1. Kernel Density Estimation for Time Series -- 6.1.1. Filtering and Smoothing -- 6.1.2. Estimation -- 6.1.3. Correcting for Changing Mean and Variance -- 6.1.4. Specification and Diagnostic Checking -- 6.2. Time-Varying Quantiles -- 6.2.1. Kernel-Based Estimation -- 6.2.2. Direct Estimation of Individual Quantiles -- 6.3. Forecasts -- 6.4. Application to NASDAQ Returns -- 6.4.1. Direct Modelling of Returns -- 6.4.2. ARMA-GARCH Residuals -- 6.4.3. Bandwidth and Tails -- 7.1. Multivariate Distributions -- 7.1.1. Estimation -- 7.1.2. Regression -- 7.1.3. Dynamic Models -- 7.2. Multivariate Location Models -- 7.2.1. Structural Time Series Models -- 7.2.2. DCS Model for the Multivariate t -- 7.2.3. Asymptotic Theory* -- 7.2.4. Regression and Errors in Variables -- 7.3. Dynamic Correlation -- 7.3.1. Bivariate Gaussian Model -- 7.3.2. Time-Varying Parameters in Regression -- 7.3.3. Multivariate t Distribution -- 7.3.4. Tests of Changing Correlation -- 7.4. Dynamic Multivariate Scale -- 7.5. Dynamic Scale and Association -- 7.6. Copulas -- 7.6.1. Copulas and Quantiles -- 7.6.2. Measures of Association -- 7.6.3. Maximum Likelihood Estimation -- 7.6.4. Dynamic Copulas -- 7.6.5. Tests Against Changing Association -- A.1. Unconditional Mean Parameterization -- A.2. Paramerization with δ -- A.3. Leverage -- B.1. Beta-t-EGARCH -- B.2. Gamma-GED-EGARCH -- B.3. Beta-t-GARCH |
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2.1 Distributions2.1.1 Student's t Distribution; 2.1.2 General Error Distribution; 2.1.3 Beta Distribution; 2.1.4 Gamma Distribution; 2.2 Maximum Likelihood; 2.2.1 Student's t Distribution; 2.2.2 General Error Distribution; 2.2.3 Gamma Distribution; 2.2.4 Consistency and Asymptotic Normality*; 2.3 Maximum Likelihood Estimation; 2.3.1 An Information Matrix Lemma; 2.3.2 Information Matrix for the First-Order Model; 2.3.3 Information Matrix with the 0=x""010E Parameterization*; 2.3.4 Asymptotic Distribution; 2.3.5 Consistency and Asymptotic Normality*; 2.3.6 Nonstationarity |
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2.3.7 Several Parameters2.4 Higher Order Models; 2.5 Tests; 2.5.1 Serial Correlation; 2.5.2 Goodness of Fit of Distributions; 2.5.3 Residuals; 2.5.4 Model Fit; 2.6 Explanatory Variables; 3 Location; 3.1 Dynamic Student's t Location Model; 3.2 Basic Properties; 3.2.1 Generalization and Reduced Form; 3.2.2 Moments of the Observations; 3.2.3 Autocorrelation Function; 3.3 Maximum Likelihood Estimation; 3.3.1 Asymptotic Distribution of the Maximum Likelihood Estimator; 3.3.2 Monte Carlo Experiments; 3.3.3 Application to U.S. GDP; 3.4 Parameter Restrictions* |
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3.5 Higher Order Models and the State Space Form*3.5.1 Linear Gaussian Models and the Kalman Filter; 3.5.2 The DCS Model; 3.5.3 QARMA Models; 3.6 Trend and Seasonality; 3.6.1 Local Level Model; 3.6.2 Application to Weekly Hours of Employees in U.S. Manufacturing; 3.6.3 Local Linear Trend; 3.6.4 Stochastic Seasonal; 3.6.5 Application to Rail Travel; 3.6.6 QARIMA and Seasonal QARIMA Models*; 3.7 Smoothing; 3.7.1 Weights; 3.7.2 Smoothing Recursions for Linear State Space Models; 3.7.3 Smoothing Recursions for DCS Models; 3.7.4 Conditional Mode Estimation and the Score; 3.8 Forecasting |
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3.8.1 QARMA Models3.8.2 State Space Form*; 3.9 Components and Long Memory; 3.10 General Error Distribution; 3.11 Skew Distributions; 3.11.1 How to Skew a Distribution; 3.11.2 Dynamic Skew-t Location Model; 4 Scale; 4.1 Beta-tttt-EGARCH; 4.2 Properties of Stationary Beta-tttt-EGARCH Models; 4.2.1 Exponential GARCH; 4.2.2 Moments; 4.2.3 Autocorrelation Functions of Squares and Powersof Absolute Values; 4.2.4 Autocorrelations and Kurtosis; 4.3 Leverage Effects; 4.4 Gamma-GED-EGARCH; 4.5 Forecasting; 4.5.1 Beta-t-EGARCH; 4.5.2 Gamma-GED-EGARCH; 4.5.3 Integrated Exponential Models |
| Summary |
Presents a statistical theory for a class of nonlinear time-series models. The overall approach will be of interest to econometricians and statisticians |
| Bibliography |
Includes bibliographical references (pages 247-254) and indexes |
| Notes |
Print version record |
| Subject |
Econometrics.
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Finance -- Mathematical models.
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Time-series analysis.
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BUSINESS & ECONOMICS -- Economics -- General.
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BUSINESS & ECONOMICS -- Reference.
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Finanzas -- Modelos matemáticos
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Análisis de series temporales
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Econometrics
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Finance -- Mathematical models
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Time-series analysis
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Nichtlineare Zeitreihenanalyse
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Wahrscheinlichkeitsverteilung
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Dynamisches Modell
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| Form |
Electronic book
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| LC no. |
2012036508 |
| ISBN |
9781107336889 |
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1107336880 |
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1139540939 |
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9781139540933 |
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9781107335226 |
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1107335221 |
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9781107333567 |
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1107333563 |
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