Economic Forecasting (eBook)

Graham Elliott, Allan Timmermann (Autoren)

eBook Download: PDF

2016
568 Seiten
Princeton University Press (Verlag)
978-1-4008-8089-8 (ISBN)

Infos

A comprehensive and integrated approach to economic forecasting problemsEconomic forecasting involves choosing simple yet robust models to best approximate highly complex and evolving data-generating processes. This poses unique challenges for researchers in a host of practical forecasting situations, from forecasting budget deficits and assessing financial risk to predicting inflation and stock market returns. Economic Forecasting presents a comprehensive, unified approach to assessing the costs and benefits of different methods currently available to forecasters.This text approaches forecasting problems from the perspective of decision theory and estimation, and demonstrates the profound implications of this approach for how we understand variable selection, estimation, and combination methods for forecasting models, and how we evaluate the resulting forecasts. Both Bayesian and non-Bayesian methods are covered in depth, as are a range of cutting-edge techniques for producing point, interval, and density forecasts. The book features detailed presentations and empirical examples of a range of forecasting methods and shows how to generate forecasts in the presence of large-dimensional sets of predictor variables. The authors pay special attention to how estimation error, model uncertainty, and model instability affect forecasting performance.Presents a comprehensive and integrated approach to assessing the strengths and weaknesses of different forecasting methodsApproaches forecasting from a decision theoretic and estimation perspectiveCovers Bayesian modeling, including methods for generating density forecastsDiscusses model selection methods as well as forecast combinationsCovers a large range of nonlinear prediction models, including regime switching models, threshold autoregressions, and models with time-varying volatilityFeatures numerous empirical examplesExamines the latest advances in forecast evaluationEssential for practitioners and students alike

Autor

Graham Elliott is professor of economics at the University of California, San Diego. Allan Timmermann holds the Atkinson/Epstein Chair at the Rady School of Management at the University of California, San Diego, where he is also professor of finance and economics.

Produkt-Details

• Erscheint lt. Verlag: 5.4.2016
• Zusatzinfo: 80 line illus. 25 tables.
• Verlagsort: Princeton
• Sprache: englisch
• Themenwelt: Wirtschaft ► Allgemeines / Lexika
• Themenwelt: Wirtschaft ► Volkswirtschaftslehre ► Ökonometrie
• Schlagworte: Accuracy and precision • Asymptotic Distribution • autocorrelation • Autoregressive conditional heteroskedasticity • Autoregressive model • Autoregressive–moving-average model • Bayesian • Bayesian inference • Bayesian information criterion • Bayesian linear regression • Bayesian Statistics • Bias of an estimator • central limit theorem • Conditional probability distribution • Corner solution • Count Data • Cross-validation (statistics) • Curse of Dimensionality • Dummy variable (statistics) • Empirical Bayes method • ensemble learning • Equivalence test • Error Correction Model • Errors and residuals • Error Term • estimation • Estimation theory • Estimator • expectation–maximization algorithm • exponential smoothing • False positive rate • Family-wise error rate • Forecast Error • Forecasting • Free parameter • General Linear Model • geometric distribution • Gibbs Sampling • goodness of fit • Granger Causality • Hyperparameter • inference • Informal Methods (Validation and Verification) • Inverse-Gamma Distribution • Joint probability distribution • Kalman Filter • Kullback–Leibler divergence • Latent Variable • Least absolute deviations • Least Squares • Likelihood-ratio test • linear regression • Logistic Regression • Loss Function • Mathematical Optimization • Maximum a posteriori estimation • maximum likelihood estimation • Mean absolute error • Mean absolute percentage error • Metropolis–Hastings algorithm • Model Selection • Moving-average model • Non-linear least squares • Normal distribution • Nuisance parameter • Ordinary Least Squares • Outlier • overfitting • Parameter • Point Estimation • Polynomial Regression • Principal Component Analysis • principal component regression • Probability Distribution • probit • Projection pursuit regression • p-value • Rate of Convergence • Root-mean-square deviation • Scenario Analysis • Seemingly unrelated regressions • semimartingale • Shrinkage Estimator • Sieve Estimator • Simple Linear Regression • star model • stochastic discount factor • Stochastic volatility • Student's t-test • Sufficient statistic • summary statistics • test statistic • Tikhonov regularization • unit root • Unit root test • Variable (mathematics) • variance reduction • vector autoregression • Volatility Clustering • Weighted arithmetic mean
• ISBN-10: 1-4008-8089-0 / 1400880890
• ISBN-13: 978-1-4008-8089-8 / 9781400880898