Course Detail
经济预测：方法、模型与应用
本课程基于《经济预测：方法、模型与应用》生成，围绕I Foundations、II Forecast Methods、Introduction、Loss Functions、The Parametric Forecasting Problem、Classical Estimation of Forecasting Models、Bayesian Forecasting Methods、Univariate Linear Prediction Models等内容展开，按章节组织知识点、讲解与练习，帮助学习者循序渐进地理解核心概念、掌握典型方法，并通过配套题目巩固学习效果。
Chapters
课程章节
按章节循序渐进阅读正文，并进入对应小节练习。
- 00I Foundations
- 01II Forecast Methods
- 02Introduction
- 03Loss Functions
- Loss Functions
- 2.1 CONSTRUCTION AND SPECIFICATION OF THE LOSS FUNCTION
- 2.1.2 Common Properties of Loss Functions
- 2.1.3 Existence of Expected Loss
- 2.1.4 Loss Functions Not Based on Expected Loss
- 2.2.1.2 Absolute Error Loss
- 2.2.1.5 Piecewise Asymmetric Loss
- 2.2.1.6 Binary Loss
- 2.2.3 Loss Functions That Depend on Other State Variables
- 2.3 MULTIVARIATE LOSS FUNCTIONS
- 2.4 SCORING RULES FOR DISTRIBUTION FORECASTS
- 2.5 EXAMPLES OF APPLICATIONS OF FORECASTS IN MACROECONOMICS AND FINANCE
- 2.5.2 Portfolio Choice under Mean–Variance Utility
- 2.5.3 Directional Trading System
- 2.6 CONCLUSION
- 04The Parametric Forecasting Problem
- The Parametric Forecasting Problem
- 3.1 OPTIMAL POINT FORECASTS
- 3.1.2 Interpretation of Forecast Optimality
- 3.1.3 Optimal Forecasts Conditional on Future Variables
- 3.2.1 Loss-Based versus Two-Step Approaches
- 3.2.2 Risk and the Information Set
- 3.2.3 Minimax and Average Risk
- 3.3 BAYESIAN APPROACH
- 3.4 RELATING THE BAYESIAN AND CLASSICAL METHODS
- 3.4.1 Density Forecasts
- 3.5 EMPIRICAL EXAMPLE: ASSET ALLOCATION WITH PARAMETER UNCERTAINTY
- 3.6 CONCLUSION
- 05Classical Estimation of Forecasting Models
- 06Bayesian Forecasting Methods
- Bayesian Forecasting Methods
- 5.1 BAYES RISK
- 5.1.1 Empirical Bayes Methods
- 5.2 RIDGE AND SHRINKAGE ESTIMATORS
- 5.3 COMPUTATIONAL METHODS
- 5.4 ECONOMIC APPLICATIONS OF BAYESIAN FORECASTING METHODS
- 5.4.1 Bayesian Investor’s Asset Allocation
- 5.5 CONCLUSION
- 6.1 TRADE-OFFS IN MODEL SELECTION
- 6.2 SEQUENTIAL HYPOTHESIS TESTING
- 6.3 INFORMATION CRITERIA
- 6.4 CROSS VALIDATION
- 6.5 LASSO MODEL SELECTION
- 6.6 HARD VERSUS SOFT THRESHOLDS: BAGGING
- 6.7.1 Statistical Measures of Forecast Performance
- 6.7.2 Economic Measures of Forecast Performance
- 6.8 PROPERTIES OF MODEL SELECTION PROCEDURES
- 6.9 RISK FOR MODEL SELECTION METHODS: MONTE CARLO SIMULATIONS
- 6.10 CONCLUSION
- 6.11.1 Schwarz Information Criterion
- 6.11.2 Akaike Information Criterion
- 07Univariate Linear Prediction Models
- Univariate Linear Prediction Models
- 7.1.1 Covariance Stationarity
- 7.1.2 ARMA Models
- 7.2 ESTIMATION AND LAG SELECTION FOR ARMA MODELS
- 7.2.2 Choice of Lag Orders
- 7.3 FORECASTING WITH ARMA MODELS
- 7.3.2 Finite-Sample Properties of Forecasts from AR(1) Models
- 7.3.3 Direct versus Iterated Multiperiod Forecasts
- 7.3.4 Forecasting Variables with Unit Roots
- 7.4.1 Forecasting Models with Seasonal Components
- 7.4.2 Deterministic Time Trends
- 7.5.1 Exponentially Weighted Moving Average
- 7.5.3 Unobserved Components
- 7.5.4 Equivalence with ARMA Models
- 08Univariate Nonlinear Prediction Models
- Univariate Nonlinear Prediction Models
- 8.1 THRESHOLD AUTOREGRESSIVE MODELS
- 8.2 SMOOTH TRANSITION AUTOREGRESSIVE MODELS
- 8.2.1 Empirical Evidence
- 8.3.1 Forecasting with Regime Switching Models
- 8.3.2 Empirical Example: Two-State Regime Switching Models
- 8.3.3 Refinements to the Markov Switching Model
- 8.4 TESTING FOR NONLINEARITY
- 8.5 FORECASTING WITH NONLINEAR UNIVARIATE MODELS
- 8.5.1 Empirical Comparisons
- 8.6 CONCLUSION
- 09Vector Autoregressions
- 9.1 SPECIFICATION OF VECTOR AUTOREGRESSIONS
- 9.2 CLASSICAL ESTIMATION OF VARS
- 9.2.2 Choice of Lag Length
- 9.2.4 Multiperiod Forecasts with VARs
- 9.2.5 Factor-Augmented VARs
- 9.3.1 Bayesian Estimation
- 9.3.2 Minnesota Prior
- 9.3.3 Alternative Priors
- 9.3.4 Time-Varying Parameter VARs
- 9.3.5 Large-Dimensional Bayesian VARs
- 9.3.6 Empirical Performance of Bayesian VARs
- 9.3.7 Empirical Example: Asset Allocation with Return Predictability
- 9.4 DSGE MODELS
- 9.4.1 Estimation and Computation of Forecasts for DSGE Models
- 9.4.2 Empirical Evidence on DSGE Models
- 9.5 CONDITIONAL FORECASTS
- 9.6 EMPIRICAL EXAMPLE
- 9.7 CONCLUSION
- 10Forecasting in a Data-Rich Environment
- 10.1 FORECASTING WITH FACTOR MODELS
- 10.1.1 Dynamic Factor Models
- 10.2.1 Maximum Likelihood Estimation (Small N)
- 10.2.2 Principal Components Estimation (Large N)
- 10.2.3 Consistency and Efficiency
- 10.2.4 Extracting Factors from the Frequency Domain
- 10.2.5 Bayesian Methods
- 10.3 DETERMINING THE NUMBER OF COMMON FACTORS
- 10.4 PRACTICAL ISSUES ARISING WITH FACTOR MODELS
- 10.4.3 Missing Observations
- 10.5 EMPIRICAL EVIDENCE
- 10.5.1 Factor Models versus Bayesian VARs
- 10.6 FORECASTING WITH PANEL DATA
- 10.7 CONCLUSION
- 11Nonparametric Forecasting Methods
- Nonparametric Forecasting Methods
- 11.1 KERNEL ESTIMATION OF FORECASTING MODELS
- 11.2 ESTIMATION OF SIEVE MODELS
- 11.2.2 Splines
- 11.2.3 Artificial Neural Networks
- 11.2.3.1 White’s QuickNet
- 11.2.4 Projection Pursuit Regression
- 11.2.5 Empirical Illustration
- 11.3 BOOSTED REGRESSION TREES
- 11.4 CONCLUSION
- 12.1 POINT AND DENSITY FORECASTS FOR BINARY OUTCOMES
- 12.2 DENSITY FORECASTS FOR BINARY OUTCOMES
- 12.2.2 Bayesian Approaches
- 12.3 CONSTRUCTING POINT FORECASTS FOR BINARY OUTCOMES
- 12.3.3 Maximum Utility Estimation
- 12.5 CONCLUSION
- 12Volatility and Density Forecasting
- 13.1 ROLE OF THE LOSS FUNCTION
- 13.2.1 Location–Scale Models of Density Forecasts
- 13.2.2 GARCH Models
- 13.2.3 Refinements to GARCH Models
- 13.3 FORECASTS USING REALIZED VOLATILITY MEASURES
- 13.4 APPROACHES TO DENSITY FORECASTING
- 13.4.1 Parametric Density Models
- 13.4.1.2 Mixtures of Normals
- 13.4.2 Nonparametric and Semiparametric Density Estimation
- 13.4.3 How to Report Density Forecasts
- 13.5 INTERVAL AND QUANTILE FORECASTS
- 13.5.1 Estimation
- 13.6 MULTIVARIATE VOLATILITY MODELS
- 13.7 COPULAS
- 13.8 CONCLUSION
- 14.1 OPTIMAL FORECAST COMBINATIONS: THEORY
- 14.1.1 Optimal Combinations under MSE Loss
- 14.1.2 Optimal Combinations under Linex Loss
- 14.2.1 Estimation of Forecast Combination Weights under MSE Loss
- 14.2.2 Estimation Methods for Time-Varying Combination Weights
- 14.2.3 Forecast Combination Puzzle
- 14.2.4 Application to Survey Forecasts
- 14.3 RISK FOR FORECAST COMBINATIONS
- 14.4 MODEL COMBINATION
- 14.4.1 Complete Subset Regressions
- 14.4.2 Empirical Illustration
- 14.4.3 Risk of Model Combinations
- 14.5 DENSITY COMBINATION
- 14.5.1 Classical Approach to Density Combination
- 14.6 BAYESIAN MODEL AVERAGING
- 14.7 EMPIRICAL EVIDENCE
- 14.8 CONCLUSION
- 13Desirable Properties of Forecasts
- 15.1 INFORMAL EVALUATION METHODS
- 15.2 LOSS DECOMPOSITION METHODS
- 15.3 EFFICIENCY PROPERTIES WITH KNOWN LOSS
- 15.3.1 Efficiency Properties under Squared Error Loss
- 15.3.2 Optimality Tests with Known Loss Shape, but Unknown Parameters
- 15.4 OPTIMALITY TESTS UNDER UNKNOWN LOSS
- 15.5 OPTIMALITY TESTS THAT DO NOT RELY ON MEASURING THE OUTCOME
- 15.6 INTERPRETING EFFICIENCY TESTS
- 15.7 CONCLUSION
- 14Evaluation of Individual Forecasts
- 16.1 THE SAMPLING DISTRIBUTION OF AVERAGE LOSSES
- 16.2 SIMULATING OUT-OF-SAMPLE FORECASTS
- 16.2.1 Expanding Estimation Window
- 16.2.3 Fixed-Proportion Estimation Window
- 16.3 CONDUCTING INFERENCE ON THE OUT-OF-SAMPLE AVERAGE LOSS
- 16.3.1 West’s Result under Squared Error Loss
- 16.4 OUT-OF-SAMPLE ASYMPTOTICS FOR RATIONALITY TESTS
- 16.5 EVALUATION OF AGGREGATE VERSUS DISAGGREGATE FORECASTS
- 16.6 CONCLUSION
- 15Evaluation and Comparison of Multiple Forecasts
- 17.1 FORECAST ENCOMPASSING TESTS
- 17.1.1 Encompassing Tests under MSE Loss
- 17.2 TESTS OF EQUIVALENT EXPECTED LOSS: THE DIEBOLD–MARIANO TEST
- 17.2.2 The Diebold–Mariano Test
- 17.3 COMPARING FORECASTING METHODS: THE GIACOMINI–WHITE APPROACH
- 17.3.1 Conditional Test of Forecasting Performance
- 17.4.1 Complications Arising from Nested Models
- 17.4.2 Recentered Test Statistic
- 17.4.3 Finite Sample Behavior of Tests
- 17.5 COMPARING MANY FORECASTS
- 17.6 ADDRESSING DATA MINING
- 17.7 IDENTIFYING SUPERIOR MODELS
- 17.8 CHOICE OF SAMPLE SPLIT
- 17.9 RELATING THE METHODS
- 17.10 IN-SAMPLE VERSUS OUT-OF-SAMPLE FORECAST COMPARISON
- 17.11 CONCLUSION
- 16Evaluating Density Forecasts
- 18.1 EVALUATION BASED ON LOSS FUNCTIONS
- 18.1.1 Evaluation of Individual Density Forecasts
- 18.1.2 Comparing Density Forecasts
- 18.1.3 Empirical Application to Volatility Forecasting
- 18.2.1 Calibration
- 18.2.2 Resolution
- 18.2.4 Receiver Operator Characteristic (ROC) curve
- 18.3 TESTS BASED ON THE PROBABILITY INTEGRAL TRANSFORM
- 18.4 EVALUATION OF MULTICATEGORY FORECASTS
- 18.5 EVALUATING INTERVAL FORECASTS
- 18.6 CONCLUSION
- 17Forecasting under Model Instability
- 19.1 BREAKS AND FORECASTING PERFORMANCE
- 19.2 LIMITATIONS OF IN-SAMPLE TESTS FOR MODEL INSTABILITY
- 19.3 MODELS WITH A SINGLE BREAK
- 19.4 MODELS WITH MULTIPLE BREAKS
- 19.5.2 Change Point Models
- 19.5.3 Time-Varying Parameter Models
- 19.6 AD HOC METHODS FOR DEALING WITH BREAKS
- 19.6.2 Intercept Correction
- 19.6.3 Forecast Combination and Model Instability
- 19.7 MODEL INSTABILITY AND FORECAST EVALUATION
- 19.8 CONCLUSION
- 18Trending Variables and Forecasting
- 19Forecasting Nonstandard Data
- 21.1 FORECASTING COUNT DATA
- 21.2 FORECASTING DURATIONS
- 21.3 REAL-TIME DATA
- 21.4 IRREGULARLY OBSERVED AND UNOBSERVED DATA
- 21.4.2 Mixed Data Sampling Methods
- 21.4.3 State-Space Approaches with Irregular Data
- A.1.1 Basic Setting
- A.2 KALMAN FILTER EQUATIONS
- A.2.1 Derivation of the Kalman Filter
- A.2.2.2 Hodrick–Prescott Filter
- A.2.4 Kalman Smoothing
- Examples
- Ullah, A.