Holdings: Elements of forecasting

Elements of forecasting:

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Bibliographic Details
Main Author:	Diebold, Francis X. 1959- (Author)
Format:	Book
Language:	English
Published:	Mason, Ohio Thomson/South-Western 2007
Edition:	4. ed.
Subjects:	Forecasting > Statistical methods Forecasting > Problems, exercises, etc Statistik Wirtschaft Prognose Prognoseverfahren Ökonometrie Regressionsanalyse
Online Access:	Table of contents only Inhaltsverzeichnis
Physical Description:	XVIII, 366 S. zahlr. graph. Darst.
ISBN:	032432359X 0324359047 9780324323597 9780324359046

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adam_text	PART I: GETTINO STARTED Chapter· !: Intraductian to Forecasting; Applications, Methods, Banks, Journals, and SoftwarB 1 1. Forecasting in Action 1 2. Forecasting Methods: An Overview of the Book 3 3. Useful Books, Journals, Software, and Online Information 6 4. Looking Ahead 9 Exercises, Problems, and Complements 9 Forecasting in daily life: We are all forecasting, all the time 9 Forecasting in business, finance, economics, and government 9 The basic forecasting framework 10 Degrees of forecastability 10 Data on the web 10 Univariate and multivariate forecasting models 10 Concepts for Review 11 References and Additional Readings 11 Chapter 2: Â Brief Review of Probability, Statistics, and Regression far Forecasting 13 1. Why This Chapter? 13 2. Random Variables, Distributions, and Moments 14 3. Multivariate Random Variables 15 4. Statistics 16 5. Regression Analysis 18 Exercises, Problems, and Complements Interpreting distributions and densities Covariance and correlation Conditional expectations versus linear projections Conditional mean and variance Scatterplots and regression lines Desired values of regression diagnostic statistics Mechanics of fitting a linear regression Regression with and without a constant term Interpreting coefficients and variables Nonlinear least squares Regression semantics Bibliographical and Computational Notes Concepts for Review References and Additional Readings Chapter 3: Six Considerations Basic to Successful Forecasting 1. The Decision Environment and Loss Function 2. The Forecast Object 3. The Forecast Statement 4. The Forecast Horizon 5. The Information Set 6. Methods and Complexity, the Parsimony Principle, and the Shrinkage Principle 7. Concluding Remarks Exercises, Problems, and Complements Data and forecast timing conventions Properties of loss functions Relationships among point, interval, and density forecasts Forecasting at short through long horizons Forecasting as an ongoing process in organizations Assessing forecasting situations Bibliographical and Computational Notes Concepts for Review References and Additional Readings PART II: BUILDING, USING, AND EVALUATING FORECASTING MODELS Chapter 4: Statistical Graphics for Forecasting 1. The Power of Statistical Graphics 2. Simple Graphical Techniques 3. Elements of Graphical Style 4. Application: Graphing Four Components of Real GDP 63 5. Concluding Remarks 66 Exercises, Problems, and Complements 67 Outliers 67 Simple versus partial correlation 67 Graphical regression diagnostic 1: time series plot of yt, yt, and et 67 2 Graphical regression diagnostic 2: time series plot of et or et 68 Graphical regression diagnostic 3: scatterplot of et versus xt 68 Graphical analysis of foreign exchange rate data 68 Common scales 69 Graphing real GDP, continued from Section 4 69 Color 69 Regression, regression diagnostics, and regression graphics in action 69 Bibliographical and Computational Notes 70 Concepts for Review 71 References and Additional Readings 71 Chapter 5: Modeling and Farenasting Trend 72 1. Modeling Trend 72 2. Estimating Trend Models 80 3. Forecasting Trend 81 4. Selecting Forecasting Models Using the Akaiké and Schwarz Criteria 82 5. Application: Forecasting Retail Sales 87 Exercises, Problems, and Complements 94 Calculating forecasts from trend models 94 Identifying and testing trend models 94 Understanding model selection criteria 94 Mechanics of trend estimation and forecasting 95 Properties of polynomial trends 95 Specialized nonlinear trends 95 Moving average smoothing for trend estimation 95 Bias corrections when forecasting from logarithmic models 96 Model selection for long-horizon forecasting 97 The variety of information criteria reported across software packages 97 Bibliographical and Computational Notes 97 Concepts for Review 98 References and Additional Readings 98 Chapter B: Modelling and Forecasting SHasonality 93 1. The Nature and Sources of Seasonality 99 2. Modeling Seasonality 101 3. Forecasting Seasonal Series 103 4. Application: Forecasting Housing Starts 104 Exercises, Problems, and Complements 108 Log transformations in seasonal models 108 Seasonal adjustment 108 Selecting forecasting models involving calendar effects 108 Testing for seasonality 109 Seasonal regressions with an intercept and s — 1 seasonal dummies 109 Applied trend and seasonal modeling 109 Periodic models 109 Interpreting dummy variables 110 Constructing seasonal models 110 Calendar effects 110 Bibliographical and Computational Notes 111 Concepts for Review 111 References and Additional Readings 111 Chapter 7; Characterizing Cycles 912 1. Covariance Stationary Time Series 113 2. White Noise 117 3. The Lag Operator 123 4. Wold s Theorem, the General Linear Process, and Rational Distributed Lags 124 5. Estimation and Inference for the Mean, Autocorrelation, and Partial Autocorrelation Functions 127 6. Application: Characterizing Canadian Employment Dynamics 130 Exercises, Problems, and Complements 132 Lag operator expressions 1 132 Lag operator expressions 2 133 Autocorrelation functions of covariance stationary series 133 Autocorrelation vs. partial autocorrelation 133 Conditional and unconditional means 133 White noise residuals 133 Selecting an employment forecasting model with the AIC and SIC 134 Simulation of a time series process 134 Sample autocorrelation functions for trending series 134 Sample autocorrelation functions for seasonal series 134 Volatility dynamics: correlograms of squares 135 Bibliographical and Computational Notes 135 Concepts for Review 135 References and Additional Readings 136 Chapter 8; Modeling Cycles: MA, AR, and ARMA Models 137 1. Moving Average (MA) Models 138 2. Autoregressive (AR) Models 145 3. Autoregressive Moving Average (ARMA) Models 152 4. Application: Specifying and Estimating Models for Employment Forecasting 154 Exercises, Problems, and Complements 163 ARMA lag inclusion 163 Shapes of correlograms 163 The autocovariance function of the MA(1) process, revisited 163 ARMA algebra 163 Diagnostic checking of model residuals 163 Mechanics of fitting ARMA models 165 Modeling cyclical dynamics 165 Aggregation and disaggregation: top-down forcasting model vs. bottom-up forecasting model 165 Nonlinear forecasting models: regime switching 165 Difficulties with nonlinear optimization 166 Bibliographical and Computational Notes 167 Concepts for Review 168 References and Additional Readings 169 Chapter Э; Forecasting Cycles 171 1. Optimal Forecasts 171 2. Forecasting Moving Average Processes 172 3. Making the Forecasts Operational 176 4. The Chain Rule of Forecasting 177 5. Application: Forecasting Employment 180 Exercises, Problems, and Complements 184 Forecast accuracy across horizons 184 Mechanics of forecasting with ARMA models: Bankwire continued 184 Forecasting an AR(1) process with known and unknown parameters 185 Forecasting an ARMA(2, 2) process 185 Optimal forecasting under asymmetric loss 186 Truncation of infinite distributed lags, state space representations, and the Kalman filter 187 Point and interval forecasts allowing for serial correlation — Nile.com continued 187 Bootstrapping simulation to acknowledge innovation distribution uncertainty and parameter estimation uncertainty 188 Bibliographical and Computational Notes 189 Concepts for Review 190 References and Additional Readings 190 Chapter ID; Putting It All Together; Â Forecasting Model with Trend, Seasonal and Cyclical Components 131 1. Assembling What We ve Learned 191 2. Application: Forecasting Liquor Sales 193 3. Recursive Estimation Procedures for Diagnosing and Selecting Forecasting Models 207 4. Liquor Sales, Continued 212 Exercises, Problems, and Complements 214 Serially correlated disturbances vs. lagged dependent variables 214 Assessing the adequacy of the liquor sales forecasting model trend specification 214 Improving non trend aspects of the liquor sales forecasting model 214 CUSUM analysis of the housing starts model 215 Model selection based on simulated forecasting performance 215 Seasonal models with time-varying parameters: forecasting AirSpeed passenger-miles 215 Formal models of unobserved components 216 The restrictions associated with unobserved-components structures 216 Additive unobserved-components decomposition and multiplicative unobserved-components decomposition 217 Signal, noise, and overfitting 217 Bibliographical and Computational Notes 217 Concepts for Review 218 References and Additional Readings 218 Chapter 11: Forecasting with Regression Models 21Э 1. Conditional Forecasting Models and Scenario Analysis 220 2. Accounting for Parameter Uncertainty in Confidence Intervals for Conditional Forecasts 220 3. Unconditional Forecasting Models 223 4. Distributed Lags, Polynomial Distributed Lags, and Rational Distributed Lags 224 5. Regressions with Lagged Dependent Variables, Regressions with ARMA Disturbances, and Transfer Function Models 225 6. Vector Autoregressions 228 7. Predictive Causality 230 8. Impulse-Response Functions and Variance Decompositions 231 9. Application: Housing Starts and Completions 235 Exercises, Problems, and Complements 249 Econometrics, time series analysis, and forecasting 249 Forecasting crop yields 249 Regression forecasting models with expectations, or anticipatory, data 249 Business cycle analysis and forecasting: expansions, contractions, turning points, and leading indicators 250 Subjective information, Bayesian VARs, and the Minnesota prior 251 Housing starts and completions, continued 251 Nonlinear regression models 1: functional form and Ramsey s test 251 Nonlinear regression models 2: logarithmic regression models 252 Nonlinear regression models 3: neural networks 252 Spurious regression 253 Comparative forecasting performance of VAR and univariate models 254 Bibliographical and Computational Notes 254 Concepts for Review 255 References and Additional Readings 255 Chapter· 12; Evaluating and Combining Forecasts 257 1. Evaluating a Single Forecast 257 2. Evaluating Two or More Forecasts: Comparing Forecast Accuracy 260 3. Forecast Encompassing and Forecast Combination 263 4. Application: OverSea Shipping Volume on the Atlantic East Trade Lane 268 Exercises, Problems, and Complements 280 Forecast evaluation in action 280 Forecast error analysis 280 Combining forecasts 280 Quantitative forecasting, judgmental forecasting, forecast combination, and shrinkage 281 The algebra of forecast combination 281 The mechanics of practical forecast evaluation and combination 282 What are we forecasting? Preliminary series, revised series, and the limits to forecast accuracy 282 Ex post versus real-time forecast evaluation 283 What do we know about the accuracy of macroeconomic forecasts? 283 Forecast evaluation when realizations are unobserved 283 Forecast error variances in models with estimated parameters 283 The empirical success of forecast combination 284 Forecast combination and the Box-Jenkins paradigm 284 Consensus forecasts 285 The Delphi method for combining experts forecasts 285 Bibliographical and Computational Notes 285 Concepts for Review 286 References and Additional Readings 286 PART III: MORE ADVANCED TOPICS Chapter· 13; Unit Roots, Stochastic Trends, ARIMA Forecasting Modais, and Smoothing ■ 2BS 1. Stochastic Trends and Forecasting 288 2. Unit Roots: Estimation and Testing 295 3. Application: Modeling and Forecasting the Yen/Dollar Exchange Rate 302 4. Smoothing 312 5. Exchange Rates, Continued 318 Exercises, Problems, and Complements 320 Modeling and forecasting the deutschemark/ dollar (DEM/USD) exchange rate 320 Housing starts and completions, continued 320 АШМА models, smoothers, and shrinkage 320 Using stochastic trend unobserved-components models to implement smoothing techniques in a probabilistic framework 320 Automatic ARIMA modeling 321 The multiplicative seasonal ARIMA^, d, q) x (P, D, Q) model 321 The Dickey-Fuller regression in the AR(2) case 321 Holt-Winters smoothing with multiplicative seasonality 322 Cointegration 323 Error correction 323 Forecast encompassing tests for /(1) series 324 Evaluating forecasts of integrated series 324 Theiľs tZ-statistic 324 Bibliographical and Computational Notes 325 Concepts for Review 326 References and Additional Readings 326 Chapter Ą Volatility Measurement, Modeling, and Forecasting ■ 329 1. The Basic ARCH Process 330 2. The GARCH Process 333 3. Extensions of ARCH and GARCH Models 337 4. Estimating, Forecasting, and Diagnosing GARCH Models 340 5. Application: Stock Market Volatility 341 Exercises, Problems, and Complements 349 Removing conditional mean dynamics before modeling volatility dynamics 349 Variations on the basic ARCH and GARCH models 349 Empirical performance of pure ARCH models as approximations to volatility dynamics 349 Direct modeling of volatility proxies 350 GARCH volatility forecasting 350 Assessing volatility dynamics in observed returns and in standardized returns 350 Allowing for leptokurtic conditional densities 351 Optimal prediction under asymmetric loss 351 Multivariate GARCH models 351 Bibliographical and Computational Notes 352 Concepts for Review 352 References and Additional Readings 353 Bibliography 355 Name Index 361 Subject Index 363
adam_txt	PART I: GETTINO STARTED Chapter· !: Intraductian to Forecasting; Applications, Methods, Banks, Journals, and SoftwarB 1 1. Forecasting in Action 1 2. Forecasting Methods: An Overview of the Book 3 3. Useful Books, Journals, Software, and Online Information 6 4. Looking Ahead 9 Exercises, Problems, and Complements 9 Forecasting in daily life: We are all forecasting, all the time 9 Forecasting in business, finance, economics, and government 9 The basic forecasting framework 10 Degrees of forecastability 10 Data on the web 10 Univariate and multivariate forecasting models 10 Concepts for Review 11 References and Additional Readings 11 Chapter 2: Â Brief Review of Probability, Statistics, and Regression far Forecasting 13 1. Why This Chapter? 13 2. Random Variables, Distributions, and Moments 14 3. Multivariate Random Variables 15 4. Statistics 16 5. Regression Analysis 18 Exercises, Problems, and Complements Interpreting distributions and densities Covariance and correlation Conditional expectations versus linear projections Conditional mean and variance Scatterplots and regression lines Desired values of regression diagnostic statistics Mechanics of fitting a linear regression Regression with and without a constant term Interpreting coefficients and variables Nonlinear least squares Regression semantics Bibliographical and Computational Notes Concepts for Review References and Additional Readings Chapter 3: Six Considerations Basic to Successful Forecasting 1. The Decision Environment and Loss Function 2. The Forecast Object 3. The Forecast Statement 4. The Forecast Horizon 5. The Information Set 6. Methods and Complexity, the Parsimony Principle, and the Shrinkage Principle 7. Concluding Remarks Exercises, Problems, and Complements Data and forecast timing conventions Properties of loss functions Relationships among point, interval, and density forecasts Forecasting at short through long horizons Forecasting as an ongoing process in organizations Assessing forecasting situations Bibliographical and Computational Notes Concepts for Review References and Additional Readings PART II: BUILDING, USING, AND EVALUATING FORECASTING MODELS Chapter 4: Statistical Graphics for Forecasting 1. The Power of Statistical Graphics 2. Simple Graphical Techniques 3. Elements of Graphical Style 4. Application: Graphing Four Components of Real GDP 63 5. Concluding Remarks 66 Exercises, Problems, and Complements 67 Outliers 67 Simple versus partial correlation 67 Graphical regression diagnostic 1: time series plot of yt, yt, and et 67 2 Graphical regression diagnostic 2: time series plot of et or \et\ 68 Graphical regression diagnostic 3: scatterplot of et versus xt 68 Graphical analysis of foreign exchange rate data 68 Common scales 69 Graphing real GDP, continued from Section 4 69 Color 69 Regression, regression diagnostics, and regression graphics in action 69 Bibliographical and Computational Notes 70 Concepts for Review 71 References and Additional Readings 71 Chapter 5: Modeling and Farenasting Trend 72 1. Modeling Trend 72 2. Estimating Trend Models 80 3. Forecasting Trend 81 4. Selecting Forecasting Models Using the Akaiké and Schwarz Criteria 82 5. Application: Forecasting Retail Sales 87 Exercises, Problems, and Complements 94 Calculating forecasts from trend models 94 Identifying and testing trend models 94 Understanding model selection criteria 94 Mechanics of trend estimation and forecasting 95 Properties of polynomial trends 95 Specialized nonlinear trends 95 Moving average smoothing for trend estimation 95 Bias corrections when forecasting from logarithmic models 96 Model selection for long-horizon forecasting 97 The variety of "information criteria" reported across software packages 97 Bibliographical and Computational Notes 97 Concepts for Review 98 References and Additional Readings 98 Chapter B: Modelling and Forecasting SHasonality 93 1. The Nature and Sources of Seasonality 99 2. Modeling Seasonality 101 3. Forecasting Seasonal Series 103 4. Application: Forecasting Housing Starts 104 Exercises, Problems, and Complements 108 Log transformations in seasonal models 108 Seasonal adjustment 108 Selecting forecasting models involving calendar effects 108 Testing for seasonality 109 Seasonal regressions with an intercept and s — 1 seasonal dummies 109 Applied trend and seasonal modeling 109 Periodic models 109 Interpreting dummy variables 110 Constructing seasonal models 110 Calendar effects 110 Bibliographical and Computational Notes 111 Concepts for Review 111 References and Additional Readings 111 Chapter 7; Characterizing Cycles 912 1. Covariance Stationary Time Series 113 2. White Noise 117 3. The Lag Operator 123 4. Wold's Theorem, the General Linear Process, and Rational Distributed Lags 124 5. Estimation and Inference for the Mean, Autocorrelation, and Partial Autocorrelation Functions 127 6. Application: Characterizing Canadian Employment Dynamics 130 Exercises, Problems, and Complements 132 Lag operator expressions 1 132 Lag operator expressions 2 133 Autocorrelation functions of covariance stationary series 133 Autocorrelation vs. partial autocorrelation 133 Conditional and unconditional means 133 White noise residuals 133 Selecting an employment forecasting model with the AIC and SIC 134 Simulation of a time series process 134 Sample autocorrelation functions for trending series 134 Sample autocorrelation functions for seasonal series 134 Volatility dynamics: correlograms of squares 135 Bibliographical and Computational Notes 135 Concepts for Review 135 References and Additional Readings 136 Chapter 8; Modeling Cycles: MA, AR, and ARMA Models 137 1. Moving Average (MA) Models 138 2. Autoregressive (AR) Models 145 3. Autoregressive Moving Average (ARMA) Models 152 4. Application: Specifying and Estimating Models for Employment Forecasting 154 Exercises, Problems, and Complements 163 ARMA lag inclusion 163 Shapes of correlograms 163 The autocovariance function of the MA(1) process, revisited 163 ARMA algebra 163 Diagnostic checking of model residuals 163 Mechanics of fitting ARMA models 165 Modeling cyclical dynamics 165 Aggregation and disaggregation: top-down forcasting model vs. bottom-up forecasting model 165 Nonlinear forecasting models: regime switching 165 Difficulties with nonlinear optimization 166 Bibliographical and Computational Notes 167 Concepts for Review 168 References and Additional Readings 169 Chapter Э; Forecasting Cycles 171 1. Optimal Forecasts 171 2. Forecasting Moving Average Processes 172 3. Making the Forecasts Operational 176 4. The Chain Rule of Forecasting 177 5. Application: Forecasting Employment 180 Exercises, Problems, and Complements 184 Forecast accuracy across horizons 184 Mechanics of forecasting with ARMA models: Bankwire continued 184 Forecasting an AR(1) process with known and unknown parameters 185 Forecasting an ARMA(2, 2) process 185 Optimal forecasting under asymmetric loss 186 Truncation of infinite distributed lags, state space representations, and the Kalman filter 187 Point and interval forecasts allowing for serial correlation — Nile.com continued 187 Bootstrapping simulation to acknowledge innovation distribution uncertainty and parameter estimation uncertainty 188 Bibliographical and Computational Notes 189 Concepts for Review 190 References and Additional Readings 190 Chapter ID; Putting It All Together; Â Forecasting Model with Trend, Seasonal and Cyclical Components 131 1. Assembling What We've Learned 191 2. Application: Forecasting Liquor Sales 193 3. Recursive Estimation Procedures for Diagnosing and Selecting Forecasting Models 207 4. Liquor Sales, Continued 212 Exercises, Problems, and Complements 214 Serially correlated disturbances vs. lagged dependent variables 214 Assessing the adequacy of the liquor sales forecasting model trend specification 214 Improving non trend aspects of the liquor sales forecasting model 214 CUSUM analysis of the housing starts model 215 Model selection based on simulated forecasting performance 215 Seasonal models with time-varying parameters: forecasting AirSpeed passenger-miles 215 Formal models of unobserved components 216 The restrictions associated with unobserved-components structures 216 Additive unobserved-components decomposition and multiplicative unobserved-components decomposition 217 Signal, noise, and overfitting 217 Bibliographical and Computational Notes 217 Concepts for Review 218 References and Additional Readings 218 Chapter 11: Forecasting with Regression Models 21Э 1. Conditional Forecasting Models and Scenario Analysis 220 2. Accounting for Parameter Uncertainty in Confidence Intervals for Conditional Forecasts 220 3. Unconditional Forecasting Models 223 4. Distributed Lags, Polynomial Distributed Lags, and Rational Distributed Lags 224 5. Regressions with Lagged Dependent Variables, Regressions with ARMA Disturbances, and Transfer Function Models 225 6. Vector Autoregressions 228 7. Predictive Causality 230 8. Impulse-Response Functions and Variance Decompositions 231 9. Application: Housing Starts and Completions 235 Exercises, Problems, and Complements 249 Econometrics, time series analysis, and forecasting 249 Forecasting crop yields 249 Regression forecasting models with expectations, or anticipatory, data 249 Business cycle analysis and forecasting: expansions, contractions, turning points, and leading indicators 250 Subjective information, Bayesian VARs, and the Minnesota prior 251 Housing starts and completions, continued 251 Nonlinear regression models 1: functional form and Ramsey's test 251 Nonlinear regression models 2: logarithmic regression models 252 Nonlinear regression models 3: neural networks 252 Spurious regression 253 Comparative forecasting performance of VAR and univariate models 254 Bibliographical and Computational Notes 254 Concepts for Review 255 References and Additional Readings 255 Chapter· 12; Evaluating and Combining Forecasts 257 1. Evaluating a Single Forecast 257 2. Evaluating Two or More Forecasts: Comparing Forecast Accuracy 260 3. Forecast Encompassing and Forecast Combination 263 4. Application: OverSea Shipping Volume on the Atlantic East Trade Lane 268 Exercises, Problems, and Complements 280 Forecast evaluation in action 280 Forecast error analysis 280 Combining forecasts 280 Quantitative forecasting, judgmental forecasting, forecast combination, and shrinkage 281 The algebra of forecast combination 281 The mechanics of practical forecast evaluation and combination 282 What are we forecasting? Preliminary series, revised series, and the limits to forecast accuracy 282 Ex post versus real-time forecast evaluation 283 What do we know about the accuracy of macroeconomic forecasts? 283 Forecast evaluation when realizations are unobserved 283 Forecast error variances in models with estimated parameters 283 The empirical success of forecast combination 284 Forecast combination and the Box-Jenkins paradigm 284 Consensus forecasts 285 The Delphi method for combining experts' forecasts 285 Bibliographical and Computational Notes 285 Concepts for Review 286 References and Additional Readings 286 PART III: MORE ADVANCED TOPICS Chapter· 13; Unit Roots, Stochastic Trends, ARIMA Forecasting Modais, and Smoothing ■ 2BS 1. Stochastic Trends and Forecasting 288 2. Unit Roots: Estimation and Testing 295 3. Application: Modeling and Forecasting the Yen/Dollar Exchange Rate 302 4. Smoothing 312 5. Exchange Rates, Continued 318 Exercises, Problems, and Complements 320 Modeling and forecasting the deutschemark/ dollar (DEM/USD) exchange rate 320 Housing starts and completions, continued 320 АШМА models, smoothers, and shrinkage 320 Using stochastic trend unobserved-components models to implement smoothing techniques in a probabilistic framework 320 Automatic ARIMA modeling 321 The multiplicative seasonal ARIMA^, d, q) x (P, D, Q) model 321 The Dickey-Fuller regression in the AR(2) case 321 Holt-Winters smoothing with multiplicative seasonality 322 Cointegration 323 Error correction 323 Forecast encompassing tests for /(1) series 324 Evaluating forecasts of integrated series 324 Theiľs tZ-statistic 324 Bibliographical and Computational Notes 325 Concepts for Review 326 References and Additional Readings 326 Chapter \Ą\ Volatility Measurement, Modeling, and Forecasting ■ 329 1. The Basic ARCH Process 330 2. The GARCH Process 333 3. Extensions of ARCH and GARCH Models 337 4. Estimating, Forecasting, and Diagnosing GARCH Models 340 5. Application: Stock Market Volatility 341 Exercises, Problems, and Complements 349 Removing conditional mean dynamics before modeling volatility dynamics 349 Variations on the basic ARCH and GARCH models 349 Empirical performance of pure ARCH models as approximations to volatility dynamics 349 Direct modeling of volatility proxies 350 GARCH volatility forecasting 350 Assessing volatility dynamics in observed returns and in standardized returns 350 Allowing for leptokurtic conditional densities 351 Optimal prediction under asymmetric loss 351 Multivariate GARCH models 351 Bibliographical and Computational Notes 352 Concepts for Review 352 References and Additional Readings 353 Bibliography 355 Name Index 361 Subject Index 363
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id	DE-604.BV022537221
illustrated	Illustrated
index_date	2024-07-02T18:08:49Z
indexdate	2024-07-09T20:59:45Z
institution	BVB
isbn	032432359X 0324359047 9780324323597 9780324359046
language	English
lccn	2006909336
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-015743718
oclc_num	77258238
open_access_boolean
owner	DE-945 DE-739 DE-1050 DE-355 DE-BY-UBR DE-19 DE-BY-UBM DE-573 DE-11 DE-188 DE-523 DE-N2
owner_facet	DE-945 DE-739 DE-1050 DE-355 DE-BY-UBR DE-19 DE-BY-UBM DE-573 DE-11 DE-188 DE-523 DE-N2
physical	XVIII, 366 S. zahlr. graph. Darst.
publishDate	2007
publishDateSearch	2007
publishDateSort	2007
publisher	Thomson/South-Western
record_format	marc
spelling	Diebold, Francis X. 1959- Verfasser (DE-588)123909104 aut Elements of forecasting Francis X. Diebold 4. ed. Mason, Ohio Thomson/South-Western 2007 XVIII, 366 S. zahlr. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Forecasting Statistical methods Forecasting Problems, exercises, etc Statistik (DE-588)4056995-0 gnd rswk-swf Wirtschaft (DE-588)4066399-1 gnd rswk-swf Prognose (DE-588)4047390-9 gnd rswk-swf Prognoseverfahren (DE-588)4358095-6 gnd rswk-swf Ökonometrie (DE-588)4132280-0 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 gnd rswk-swf Prognose (DE-588)4047390-9 s Ökonometrie (DE-588)4132280-0 s DE-604 Prognoseverfahren (DE-588)4358095-6 s Statistik (DE-588)4056995-0 s DE-188 Wirtschaft (DE-588)4066399-1 s 1\p DE-604 Regressionsanalyse (DE-588)4129903-6 s 2\p DE-604 http://www.loc.gov/catdir/toc/fy0707/2006909336.html Table of contents only Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015743718&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk
spellingShingle	Diebold, Francis X. 1959- Elements of forecasting Forecasting Statistical methods Forecasting Problems, exercises, etc Statistik (DE-588)4056995-0 gnd Wirtschaft (DE-588)4066399-1 gnd Prognose (DE-588)4047390-9 gnd Prognoseverfahren (DE-588)4358095-6 gnd Ökonometrie (DE-588)4132280-0 gnd Regressionsanalyse (DE-588)4129903-6 gnd
subject_GND	(DE-588)4056995-0 (DE-588)4066399-1 (DE-588)4047390-9 (DE-588)4358095-6 (DE-588)4132280-0 (DE-588)4129903-6
title	Elements of forecasting
title_auth	Elements of forecasting
title_exact_search	Elements of forecasting
title_exact_search_txtP	Elements of forecasting
title_full	Elements of forecasting Francis X. Diebold
title_fullStr	Elements of forecasting Francis X. Diebold
title_full_unstemmed	Elements of forecasting Francis X. Diebold
title_short	Elements of forecasting
title_sort	elements of forecasting
topic	Forecasting Statistical methods Forecasting Problems, exercises, etc Statistik (DE-588)4056995-0 gnd Wirtschaft (DE-588)4066399-1 gnd Prognose (DE-588)4047390-9 gnd Prognoseverfahren (DE-588)4358095-6 gnd Ökonometrie (DE-588)4132280-0 gnd Regressionsanalyse (DE-588)4129903-6 gnd
topic_facet	Forecasting Statistical methods Forecasting Problems, exercises, etc Statistik Wirtschaft Prognose Prognoseverfahren Ökonometrie Regressionsanalyse
url	http://www.loc.gov/catdir/toc/fy0707/2006909336.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015743718&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT dieboldfrancisx elementsofforecasting

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