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Encompassing tests for evaluating multi-step system forecasts invariant to linear transformations

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Abstract

The paper suggests two encompassing tests for evaluating multi-step system forecasts invariant to linear transformations. An invariant measure for forecast accuracy is necessary as the conclusions otherwise can depend on how the forecasts are reported (e.g., as in level or growth rates). Therefore, a measure based on the prediction likelihood of the forecast for all variables at all horizons is used. Both tests are based on a generalization of the encompassing test for univariate forecasts where potential heteroscedasticity and autocorrelation in the forecasts are considered. The tests are used in evaluating quarterly multi-step system forecasts made by Statistics Norway.

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  • Håvard Hungnes, 2018. "Encompassing tests for evaluating multi-step system forecasts invariant to linear transformations," Discussion Papers 871, Statistics Norway, Research Department.
    • Handle: RePEc:ssb:dispap:871
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    File URL: https://www.ssb.no/en/forskning/discussion-papers/_attachment/340009?_ts=1617a4be268
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    Cited by:

    1. Magnus Kvåle Helliesen & Håvard Hungnes & Terje Skjerpen, 2022. "Revisions in the Norwegian National Accounts: accuracy, unbiasedness and efficiency in preliminary figures," Empirical Economics, Springer, vol. 62(3), pages 1079-1121, March.
    2. Håvard Hungnes, 2020. "Predicting the exchange rate path. The importance of using up-to-date observations in the forecasts," Discussion Papers 934, Statistics Norway, Research Department.
    3. Håvard Hungnes, 2020. "Equal predictability test for multi-step-ahead system forecasts invariant to linear transformations," Discussion Papers 931, Statistics Norway, Research Department.
    4. Andrew B. Martinez, 2020. "Extracting Information from Different Expectations," Working Papers 2020-008, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.

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    More about this item

    Keywords

    Macroeconomic forecasts; Econometric models; Forecast performance; Forecast evaluation; Forecast comparison;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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