- A brief history of forecasting accuracy studies
- A comparison
- Decision Based Forecast Evaluation Framework
- A dynamic single equation
- A framework for interpreting forecasts
- A fullscale dVAR model dRIMc
- A generalized dynamic system
- Motivating Example
- A simple dynamic system
- A static single equation
- A taxonomy of forecast uncertainty
- Accounting for parameter uncertainty
- Acknowledgments
- Adjusting for a lack of fit
- Aggregation over time series and various horizons of error statistics
- An evaluation
- Anthony S Tay and Kenneth F Wallis
- Applications
- Applications in finance
- Applications in macroeconomics
- Assessment of VAR models
- Auxiliary models - 2 3
- Bed occupancy at some hospitals in the UK
- Characteristics of variables
- Checking Model Adequacy for Multi Step Prediction
- Classical regression methods
- Comparison of predictive performance
- Concluding Remarks
- Conclusion - 2
- Conclusions
- Conclusions and Extensions - 2 3 4 5
- Conditional or unconditional forecasts
- Contents
- Correlated Disturbances
- Cyclic Models
- Databased transformations
- Decision Based Forecast Comparisons
- Difference and double difference models dAR and dARr
- Direct Method for ARMA Models
- Direct Method for Autoregressive Models
- Directional forecast evaluation criteria
- Discussion
- Do these problems have potential solutions
- Dynamic generalized linear models
- Dynamic Linear Regression DLR components
- Dynamic optimizing models
- Edited by
- Empirical example
- Empirical Results Weak Form Informational Efficiency
- Empirical studies
- Estimating and representing forecast densities
- Evaluation and Calibration
- Evaluation of probability event and density forecasts
- Ex4 3[1 1 P12
- Example - 2
- Factors Affecting the Accuracy of Judgmental Forecasts
- Failure to match estimation methods to error statistics
- Feasible GLS estimation of VARMA models
- Fixed event predictions
- Forecast Combination
- Forecast comparison
- Forecast Comparisons of the Large Scale Models
- Forecast Encompassing
- Forecast performance measures
- Forecasting - 2
- Forecasting accuracy and prediction intervals
- Forecasting evaluation
- Forecasting Format
- Forecasting linearly transformed and aggregated processes
- Forecasting using estimated processes
- Further practical issues
- General procedure
- General versus specific model specification
- Generalizations and Extensions
- Generalized costoferror functions
- HEGY and other tests
- How can one measure the success or failure of forecasts
- How confident can we be in forecasts
- How do we analyze the properties of forecasting methods
- How does one analyze the properties of forecasting methods
- How is forecasting done by economists - 2
- How is forecasting done generally - 2
- How parameter estimation affects inference A general result
- How parameter estimation affects inference An example
- Initial conditions and nonstationary models1
- Intercepts and trends
- Introduction - 2 3 4 5 6 7 8 9 10 11 12 13 14
- Italian Gross Domestic Product
- Judgmental Adjustments and Combining Forecasts
- Lack of clear objectives
- Large Scale Macroeconomic Models of the Norwegian Economy
- List of Contributors
- Literature Review
- Maximum likelihood estimation
- Measuring forecast accuracy
- Michael P Clements and David F Hendry
- Model 1 Transfer Function TF models
- Model 2 Dynamic Harmonic Regression DHR Young et al 1999
- Model 3 Static relationships
- Model 3 Trigonometric Cycle or Seasonal Harvey 1989 West and Harrison 1989
- Model 4 An equation which reflects the fact that the indicator is supposed to have a lead of 69 months interpreted as eight months
- Model 4 Modulated Cycle or Seasonal Components Young and Pedregal 1997a
- Model 5 A VAR forecasting system constructed using a vector of indicator variables x t
- Model Assessment
- Model checking
- Model selection
- Model Specification and Inflation Forecasting
- Model Based versus Judgmental Forecasts
- Modeling and forecasting volatility skew and kurtosis
- Modeling cycle
- Models with deterministic seasonality
- Models with nonlinear conditional mean
- Modern timeseries methods
- Monte Carlo evidence
- Motivation and Chapter Outline
- Multi Step Parameter Estimation Methods
- Multivariate Information Sets
- Multivariate Models
- Negative exponential utility A finance application
- Neil R Ericsson
- Nonlinear and Non Gaussian Models
- Nonlinear models
- Nonparametric tests - 2
- Nonperiodic models
- Nt et t t L t
- OECD indicators and VARs using OECD indicator variables
- Optimization of hyperparameters
- Other methods
- Outlier models
- P y Q[P y
- P2t c0 C1 X Koi KiX
- Parametric bootstrap
- Parametric tests
- Periodic models
- Philip Hans Franses and Richard Paap
- Postsample predictive testing and model evaluation
- Practical Examples
- Preface
- Presentation of Density Forecasts
- Producers and Users of Forecasts
- Quadratic cost functions and the MSFE criteria
- Rapidlysampled data Electricity demand forecasting
- Rationality Weak and Strong Efficiency
- Recent Developments in Forecast Evaluation An Overview
- Reconciling conflicting rationality findings
- Related Topics and Unresolved Issues
- Relationships between models
- Representation and parameter estimation
- Rolling event forecasts
- Ruey S Tsay
- Seasonal and cyclic components
- Seasonal Models
- Seasonal unitroot models
- Significance Tests of Forecast Accuracy
- Simulated example
- Simulation Results
- Single equation regression models
- Small forwardlooking models
- Smallsample modifications to the Diebold Mariano test
- Smoothing
- Some extensions
- Special issues
- Specification of the cointegrating rank
- Specification of the Kronecker Indices
- Stability of relationships
- STAR Model 2121 Definition
- State estimation
- State Space Models
- Statistical Treatment
- Stefan Lundbergh and Timo Terasvirta
- Stochastic variance models
- Strong form informational efficiency
- Structural models with Markov switching
- Summary - 2
- Testing the Equivalence of Forecast Values
- Tests of predictive performance
- The Bank of Canada The process
- The benefits of combining forecasting methods
- The Characteristics of Forecasting Competitions
- The Competing Models
- The core and related models - 2
- The core model - 2 3 4
- The Diebold Mariano DM test
- The effects of errorcorrection terms
- The incumbent EqCM model eRIM
- The Kalman filter and the prediction error decomposition
- The Meese Rogoff MR test
- The methodological adequacy of forecasting competition research
- The Morgan GrangerNewbold MGN test
- The naive model
- The Objectives of Forecasting Competitions
- The process
- The Reserve Bank of New Zealand The process
- The role of structural breaks
- The Unobserved Components UC model
- The US Federal Reserve The process
- The use of a single time origin to construct the forecasts
- The use of automatic methods of forecasting
- The use of forecasting competitions
- Trend components
- Trend Models
- Two Inflation Models
- Twostate twoaction decision problems7
- Types of Forecasts
- Typical UC Models
- UK Empirical Results
- Unification of the methods
- Unit roots and periodic integration
- US GNP forecasts
- US inflation studies
- Valueat Risk and extreme quantiles
- VAR and EC models
- VAR models
- VAR processes
- Variations of the MGN test
- VARMA models
- VARMA processes
- VARs with a greater role for financial variables
- Vector autoregressions
- Volatility models
- Wests asymptotic inference on predictive ability
- What are the main problems
- What are the main problems Do these have potential solutions
- What can be forecast
- What can be forecast And how confident can we be in our forecasts
- What is a forecast
- What is the forecast horizon and interval
- What is the future of economic forecasting
- What needs to be forecast
- What special data features matter most - 2
- What type of model should be constructed
- Whites Reality Check pvalue
- Why Are Some Forecasts Not Rational
- X2 a0 X ai Pixli n Xjj205
- Yt asy1 v TsTt yM asiyl v TTt et 1931
- Yt Zat Xtp Gtet at1 Ttat Wtp Htet520