Forecasting Economic Time Series
Cambridge University Press, Oct 8, 1998 - Business & Economics - 368 pages
This book provides a formal analysis of the models, procedures, and measures of economic forecasting with a view to improving forecasting practice. David Hendry and Michael Clements base the analyses on assumptions pertinent to the economies to be forecast, viz. a non-constant, evolving economic system, and econometric models whose form and structure are unknown a priori. The authors find that conclusions which can be established formally for constant-parameter stationary processes and correctly-specified models often do not hold when unrealistic assumptions are relaxed. Despite the difficulty of proceeding formally when models are mis-specified in unknown ways for non-stationary processes that are subject to structural breaks, Hendry and Clements show that significant insights can be gleaned. For example, a formal taxonomy of forecasting errors can be developed, the role of causal information clarified, intercept corrections re-established as a method for achieving robustness against forms of structural change, and measures of forecast accuracy re-interpreted.
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Evaluating forecast accuracy
Forecasting in univariate processes
Monte Carlo techniques
Forecasting in cointegrated systems
Forecasting with largescale macroeconometric models
A taxonomy of forecast errors
beyond mechanistic forecasts
Forecasting using leading indicators
Other editions - View all
1-step forecasts analysis approximation assume asymptotic autocorrelation autoregressive Box-Jenkins calculate chapter CLIs coefficients cointegrating vectors cointegration combination components conditional expectation consider constant terms correctly specified correlation denoted differences differencing discussed distribution Doornik DV model dynamic econometric models economic forecasting equation Ericsson estimation uncertainty eT+h evaluation example first-order forecast accuracy forecast encompassing forecast error forecast horizon forecast origin forecast performance forecast period forecast-error variance GFESM given homoscedastic i-step ahead forecast implies intercept corrections invariant IVDE lack of invariance leading indicators levels linear transformations loss function matrix measures methods MMSFE model mis-specification Monte Carlo MSFE multi-step forecasts non-constant non-linear outcome parameter estimation prediction prediction intervals predictor random variable regressors relative residuals sample scalar second-moment stationary stationary processes statistic stochastic structural breaks time-series unbiased unconditional unit roots values variables vector process yr+i yT+h yT+i zero