Forecasting Economic Time Series

Preface to the Second Edition

Preface to the First Edition

Chapter One Introduction to the Theory of Time Series


1.1 Introducing Time Series


1.2 Covariances and Stationarity


1.3 Some Mathematical Tools


1.4 The Linear Cyclic Model


1.5 The Autoregressive Model


1.6 The Moving Average Model


1.7 The Mixed Autoregressive-Moving Average Model


1.8 Interpreting the Mixed Model


1.9 Filters


1.10 Deterministic Components


1.11 Wold's Decomposition


1.12 Nonstationary Processes


1.13 Integrated Processes


1.14 Models for Seasonal Time Series

Chapter Two Spectral Analysis


2.1 Introduction


2.2 Filters


2.3 The Spectrum of Some Common Models


2.4 Aliasing


2.5 The Cross Spectrum


2.6 Estimation of Spectral Functions


2.7 The Typical Spectral Shape and Its Interpretation


2.8 Seasonal Adjustment: An Application of the Cross Spectrum


2.9 Advanced Spectral Techniques

Chapter Three Building Linear Time Series Models


3.1 Model Building Philosophy


3.2 Identification


3.3 Initial Estimates for Coefficients


3.4 The Autocorrelation Function as a Characteristic of Process Behavior


3.5 Estimation


3.6 Diagnostic Checking


3.7 Model Building for Seasonal Time Series


3.8 Time Series Model Building—An Overview

Chapter Four The Theory of Forecasting


4.1 Some Basic Concepts


4.2 Generalized Cost Functions


4.3 Properties of Optimal, Single-Series Forecasts


4.4 Optimal Forecasts for Particular Models


4.5 A Frequency-Domain Approach


4.6 Expectations and Forecasts


4.7 Unbiased Forecasts


4.8 Invertibility


4.9 Types of Forecasts

Chapter Five Practical Methods for Univariate Time Series Forecasting


5.1 Introduction


5.2 Box-Jenkins Forecasting Methods


5.3 Exponential Smoothing Methods


5.4 Stepwise Autoregression


5.5 A Fully Automatic Forecasting Procedure Based on the Combination of Forecasts


5.6 Comparison of Univariate Forecasting Procedures

Chapter Six Forecasting from Regression Models


6.1 Introduction


6.2 Single Equation Models


6.3 Simultaneous Equation Models


6.4 Danger of Spurious Regressions in Econometric Models

Chapter Seven Multiple Series Modeling and Forecasting


7.1 Introduction


7.2 Theoretical Models for Multiple Time Series


7.3 Causality and Feedback


7.4 Co-Integrated Series and Error-Correction Models


7.5 Properties of Optimal Multiseries Forecasts


7.6 Forecasting Aggregates


7.7 Rational Expectations

Chapter Eight Building Multiple Time Series Forecasting Models


8.1 Introduction


8.2 Building Bivariate Models: Unidirectional Causality


8.3 Building Vector ARMA Models


8.4 Building Forecasting Models for Several Related Time Series


8.5 Testing for Causality


8.6 Testing for Co-Integration

Chapter Nine The Combination and Evaluation of Forecasts


9.1 Typical Suboptimality of Economic Forecasts


9.2 The Combination of Forecasts


9.3 The Evaluation of Forecasts


9.4 A Survey of the Performance of Macroeconomic Forecasts


9.5 Econometric Forecasting and Time Series Analysis


9.6 Leading Indicators

Chapter Ten Further Topics


10.1 State-Space Representation, the Kalman Filter


10.2 Time-Varying Parameter Models


10.3 Nonlinear Models


10.4 Bilinear Models


10.5 Instantaneous Data Transformations


10.6 Forecasting White Noise


10.7 Predicting Variances: ARCH Models


10.8 Forecasting Unobserved Components

References

Author Index

Subject Index