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MULTISTEP ESTIMATION AND FORECASTING IN DYNAMIC-MODELS
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DOI：10.1016/0304-4076(91)90035-C.png)
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We consider the situation in which a researcher uses a misspecified model for forecasting. If the interest is in multi-step forecasting and the accuracy of the forecasts is measured through the sum of squared multi-step forecast errors, then in large samples it is preferable to minimize corresponding sum of squared in-sample multi-step forecast errors. We derive the asymptotic properties of the resulting estimator. To assess the behavior of the estimator in small samples, we perform a Monte Carlo experiment. In most of the cases considered, OLS outperforms the estimator. We conjecture that this occurs because the estimator is still defined on the basis of squared errors.
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1ST-ORDER AUTOREGRESSIVE MODEL
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