Content Horizons For Forecasts Of Economic Time Series
AbstractWe consider the problem of determining the horizon beyond which forecasts from time series models of stationary processes add nothing to the forecast implicit in the conditional mean. We refer to this as the content horizon for forecasts, and define a forecast content function at horizons s = 1,... S as the proportionate reduction in mean squared forecast error available from a time series forecast relative to the unconditional mean. This function depends upon parameter estimation uncertainty as well as on autocorrelation structure of the process under investigation. We give an approximate expression (to o(1/T)) for the forecast content function at s for a general autoregressive process, and show by simulation that the expression gives a good approximation even at modest sample sizes. Finally we consider parametric and non-parametric (kernel) estimators of the empirical forecast content function, and apply the results to forecast horizons for inflation and the growth rate of GDP, in U.S. and Canadian data.
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Bibliographic InfoPaper provided by McGill University, Department of Economics in its series Departmental Working Papers with number 1999-01.
Length: 21 pages
Date of creation: Apr 1999
Date of revision:
Other versions of this item:
- John Galbraith, 1999. "Content Horizons for Forecasts of Economic Time Series," CIRANO Working Papers 99s-17, CIRANO.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
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