CONTENTS OF VOLUME 1
Introduction to the Series v
Contents of the Handbook vii
PART 1: 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
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