Handbook of economic forecasting ; 1

Online-Ressource

Handbooks in economics. - Amsterdam [u.a.] : Elsevier, ZDB-ID 2685869-1. - Vol. 24

Includes bibliographical references and indexes. - Print version record

Front cover; Title page; Copyright page; Introduction to the Series; Contents of the Handbook; Table of contents; Part 1: FORECASTING METHODOLOGY; 1 Bayesian Forecasting; Abstract; Keywords; Introduction; Bayesian inference and forecasting: A primer; Models for observables; An example: Vector autoregressions; An example: Stochastic volatility; The forecasting vector of interest; Model completion with prior distributions; The role of the prior; Prior predictive distributions; Hierarchical priors and shrinkage; Latent variables; Model combination and evaluation; Models and probability

A model is as good as its predictionsPosterior predictive distributions; Forecasting; Loss functions and the subjective decision maker; Probability forecasting and remote clients; Forecasts from a combination of models; Conditional forecasting; Posterior simulation methods; Simulation methods before 1990; Direct sampling; Acceptance sampling; Importance sampling; Markov chain Monte Carlo; The Gibbs sampler; The Metropolis-Hastings algorithm; Metropolis within Gibbs; The full Monte; Predictive distributions and point forecasts; Model combination and the revision of assumptions

'Twas not always so easy: A historical perspectiveIn the beginning, there was diffuseness, conjugacy, and analytic work; The dynamic linear model; The Minnesota revolution; After Minnesota: Subsequent developments; Some Bayesian forecasting models; Autoregressive leading indicator models; Stationary linear models; The stationary AR(p) model; The stationary ARMA(p,q) model; Fractional integration; Cointegration and error correction; Stochastic volatility; Practical experience with Bayesian forecasts; National BVAR forecasts: The Federal Reserve Bank of Minneapolis

Regional BVAR forecasts: economic conditions in IowaReferences; 2 Forecasting and Decision Theory; Abstract; Keywords; Preface; History of the field; Introduction; The Cambridge papers; Forecasting versus statistical hypothesis testing and estimation; Forecasting with decision-based loss functions; Background; Framework and basic analysis; Decision problems, forecasts and decision-based loss functions; Derivatives of decision-based loss functions; Inessential transformations of a decision problem; Recovery of decision problems from loss functions; Recovery from point-forecast loss functions

Implications of squared-error lossAre squared-error loss functions appropriate as ""local approximations""?; Implications of error-based loss; Location-dependent loss functions; Distribution-forecast and distribution-realization loss functions; References; 3 Forecast Evaluation; Abstract; Keywords; Introduction; A brief history; A small number of nonnested models, Part I; A small number of nonnested models, Part II; A small number of nonnested models, Part III; A small number of models, nested: MSPE; A small number of models, nested, Part II; Summary on small number of models

Large number of models

Bayesian ForecastingForecasting and Decision Theory -- Forecast Evaluation -- Forecast Combinations -- Predictive Density Evaluation -- Forecasting with VARMA Models -- Forecasting with Unobserved Components Time Series Models -- Forecasting economic variables with nonlinear models -- Approximate Nonlinear Forecasting Methods -- Forecasting with Many Predictors -- Forecasting with Trending Data -- Forecasting with Breaks -- Forecasting Seasonal Time Series -- Survey Expectations -- Volatility and Correlation Forecasting -- Leading Indicators -- Forecasting with Real-Time Macroeconomic Data -- Forecasting in Marketing.

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