MULTI‐STEP ESTIMATION FOR FORECASTING

Michael P. Clements · David Forbes Hendry

98 citations0/0 files storedOxford Bulletin of Economics and Statistics

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Forecasting Techniques and Applications Monetary Policy and Economic Impact Statistical and numerical algorithms

Abstract

We delineate conditions which favour multi‐step, or dynamic, estimation for multi‐step forecasting. An analytical example shows how dynamic estimation (DE) may accommodate incorrectly‐specified models as the forecast lead alters, improving forecast performance for some misspecifications. However, in correctly‐specified models, reducing finite‐sample biases does not justify DE. In a Monte Carlo forecasting study for integrated processes, estimating a unit root in the presence of a neglected negative moving‐average error may favour DE, though other solutions exist to that scenario. A second Monte Carlo study obtains the estimator biases and explains these using asymptotic approximations.

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References

1979Distribution of the Estimators for Autoregressive Time Series with a Unit Root 1981Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root 1990Forecasting, Structural Time Series Models and the Kalman Filter 1987Forecasting and testing in co-integrated systems 1993Co-integration, Error Correction, and the Econometric Analysis of Non-Stationary Data 1996Co-Integration, Error Correction, and the Econometric Analysis of Non- Stationary Data.1988Tests For Unit Roots: A Monte Carlo Investigation 1989Tests for Unit Roots: A Monte Carlo Investigation

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