Forecasting with an econometric model: The ‘ragged edge’ problem

Kenneth Frank Wallis

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

Abstract

Abstract In practical econometric forecasting exercises, incomplete data on current and immediate past values of endogenous variables are available. This paper considers various approaches to this ‘ragged edge’ problem, including the common device of treating as ‘temporarily exogenous’ an endogenous variable whose value is known, by deleting it from the set of endogenous variables for whose forecast values the model is solved and suppressing the corresponding structural equation. It is seen that this forecast can be adjusted to coincide with the optimal forecast. The initial discussion concerns the textbook linear simultaneous equation model; extensions to non‐linear dynamic models are described.

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References

1969Estimation of Interdependent Systems in Macroeconometrics 1986Models of the UK economy : a third review by the ESRC macroeconomic modelling bureau 1986On Evaluating the Importance of Nonlinearity in Large Macroeconometric Models

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