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Handbook of Economic Forecasting part 2. Research on forecasting methods has made important progress over recent years and these developments are brought together in the Handbook of Economic Forecasting. The handbook covers developments in how forecasts are constructed based on multivariate time-series models, dynamic factor models, nonlinear models and combination methods. The handbook also includes chapters on forecast evaluation, including evaluation of point forecasts and probability forecasts and contains chapters on survey forecasts and volatility forecasts. Areas of applications of forecasts covered in the handbook include economics, finance and marketing | This page intentionally left blank CONTENTS OF VOLUME 1 Introduction to the Series v Contents of the Handbook vii PART1 FORECASTING METHODOLOGY Chapter 1 Bayesian Forecasting JOHN GEWEKE AND CHARLES WHITEMAN 3 Abstract 4 Keywords 4 1. Introduction 6 2. Bayesian inference and forecasting A primer 7 2.1. Models for observables 7 2.2. Model completion with prior distributions 10 2.3. Model combination and evaluation 14 2.4. Forecasting 19 3. Posterior simulation methods 25 3.1. Simulation methods before 1990 25 3.2. Markov chain Monte Carlo 30 3.3. The full Monte 36 4. Twas not always so easy A historical perspective 41 4.1. In the beginning there was diffuseness conjugacy and analytic work 41 4.2. The dynamic linear model 43 4.3. The Minnesota revolution 44 4.4. After Minnesota Subsequent developments 49 5. Some Bayesian forecasting models 53 5.1. Autoregressive leading indicator models 54 5.2. Stationary linear models 56 5.3. Fractional integration 59 5.4. Cointegration and error correction 61 5.5. Stochastic volatility 64 6. Practical experience with Bayesian forecasts 68 6.1. National BVAR forecasts The Federal Reserve Bank of Minneapolis 69 6.2. Regional BVAR forecasts Economic conditions in Iowa 70 References 73 xi xii Contents of Volume 1 Chapter 2 Forecasting and Decision Theory CLIVE W.J. GRANGER AND MARK J. MACHINA 81 Abstract 82 Keywords 82 Preface 83 1. History of the field 83 1.1. Introduction 83 1.2. The Cambridge papers 84 1.3. Forecasting versus statistical hypothesis testing and estimation 87 2. Forecasting with decision-based loss functions 87 2.1. Background 87 2.2. Framework and basic analysis 88 2.3. Recovery of decision problems from loss functions 93 2.4. Location-dependent loss functions 96 2.5. Distribution-forecast and distribution-realization loss functions 97 References 98 Chapter 3 Forecast Evaluation KENNETH D. WEST 99 Abstract 100 Keywords 100 1. Introduction 101 2. A brief history 102 3. A small number of nonnested models Part I 104 4.
