Forecasting Economic Time SeriesCambridge University Press, 8. 10. 1998 - Počet stran: 368 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. |
Obsah
2 | 18 |
5 | 25 |
First principles | 33 |
Evaluating forecast accuracy | 52 |
Fixedevent forecasts | 59 |
4 | 66 |
6 | 73 |
Forecasting in univariate processes | 79 |
beyond mechanistic forecasts | 180 |
1 | 200 |
Forecasting using leading indicators | 207 |
Combining forecasts | 227 |
Multistep estimation | 243 |
4 | 244 |
Parsimony | 280 |
Testing forecast accuracy | 312 |
Monte Carlo techniques | 107 |
Forecasting in cointegrated systems | 119 |
2 | 142 |
3 | 153 |
Forecasting with largescale macroeconometric models | 157 |
8 | 319 |
Postscript | 329 |
359 | |
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Forecasting Economic Time Series Michael P. Clements,David Hendry Náhled není k dispozici. - 1998 |
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1-step forecasts analysis approximation asymptotic autoregressive Box-Jenkins chapter coefficients cointegration combination components conditional expectation consider constant terms correctly specified correlation denoted differences differencing discussed distribution Doornik DV model dynamic econometric models economic Engle equation Ericsson estimation uncertainty eT+h evaluation example F-test forecast accuracy forecast encompassing forecast errors forecast horizon forecast performance forecast period forecast-error variances GFESM given Granger h-step ahead forecast Hendry intercept corrections invariant IVDE lack of invariance leading indicators levels linear transformations loss function M₁ matrix model mis-specification Monte Carlo MSFE multi-step forecasts non-linear outcome parameter estimates prediction prediction intervals predictor regressors relative residuals sample scalar stationary stationary processes statistic stochastic structural breaks time-series unbiased unconditional unit roots values variables XT+1 XT+h YT+1 YT+h zero