Details

Autor(en) / Beteiligte

• Elliott, Graham,
• Granger, C. W. J
• Timmermann, Allan

Titel

Handbook of economic forecasting [electronic resource]

Ist Teil von

• Handbook of economic forecasting ; : 1

Auflage

1st ed

Link zum Volltext

• Elsevier Books AutoHoldings

Beschreibungen/Notizen

• Description based upon print version of record.
• Includes bibliographical references and indexes.
• 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
• 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 eco