tailieunhanh - Handbook of Economic Forecasting part 4

Handbook of Economic Forecasting part 4. 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 | 4 J. Geweke and C. Whiteman . The Metropolis-Hastings algorithm 33 . Metropolis within Gibbs 34 . The full Monte 36 . Predictive distributions and point forecasts 37 . Model combination and the revision of assumptions 39 4. Twas not always so easy A historical perspective 41 . In the beginning there was diffuseness conjugacy and analytic work 41 . The dynamic linear model 43 . The Minnesota revolution 44 . After Minnesota Subsequent developments 49 5. Some Bayesian forecasting models 53 . Autoregressive leading indicator models 54 . Stationary linear models 56 . The stationary AR p model 56 . The stationary ARMA p q model 57 . Fractional integration 59 . Cointegration and error correction 61 . Stochastic volatility 64 6. Practical experience with Bayesian forecasts 68 . National BVAR forecasts The Federal Reserve Bank of Minneapolis 69 . Regional BVAR forecasts economic conditions in Iowa 70 References 73 Abstract Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach in general requires explicit formulation of a model and conditioning on known quantities in order to draw inferences about unknown ones. In Bayesian forecasting one simply takes a subset of the unknown quantities to be future values of some variables of interest. This chapter presents the principles of Bayesian forecasting and describes recent advances in computational capabilities for applying them that have dramatically expanded the scope of applicability of the Bayesian approach. It describes historical developments and the analytic compromises that were necessary prior to recent developments the application of the new procedures in a variety of examples and reports on two long-term Bayesian forecasting exercises. Keywords Markov chain Monte Carlo predictive distribution probability forecasting simulation vector autoregression Ch. 1 Bayesian Forecasting 5 JEL classification C530 C110 C150