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Forecasting Multivariate Time Series with Linear Restrictions Using Constrained Structural State-Space Models

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  • Pandher, Gurupdesh S

Abstract

This paper presents a methodology for modelling and forecasting multivariate time series with linear restrictions using the constrained structural state-space framework. The model has natural applications to forecasting time series of macroeconomic/financial identities and accounts. The explicit modelling of the constraints ensures that model parameters dynamically satisfy the restrictions among items of the series, leading to more accurate and internally consistent forecasts. It is shown that the constrained model offers superior forecasting efficiency. A testable identification condition for state space models is also obtained and applied to establish the identifiability of the constrained model. The proposed methods are illustrated on Germany's quarterly monetary accounts data. Results show significant improvement in the predictive efficiency of forecast estimators for the monetary account with an overall efficiency gain of 25% over unconstrained modelling. Copyright © 2002 by John Wiley & Sons, Ltd.

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  • Pandher, Gurupdesh S, 2002. "Forecasting Multivariate Time Series with Linear Restrictions Using Constrained Structural State-Space Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(4), pages 281-300, July.
    • Handle: RePEc:jof:jforec:v:21:y:2002:i:4:p:281-300
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    Cited by:

    1. Gomez, Nicolas & Guerrero, Victor M., 2006. "Restricted forecasting with VAR models: An analysis of a test for joint compatibility between restrictions and forecasts," International Journal of Forecasting, Elsevier, vol. 22(4), pages 751-770.
    2. Adrian Pizzinga, 2010. "Constrained Kalman Filtering: Additional Results," International Statistical Review, International Statistical Institute, vol. 78(2), pages 189-208, August.
    3. Shalini Sharma & Víctor Elvira & Emilie Chouzenoux & Angshul Majumdar, 2021. "Recurrent Dictionary Learning for State-Space Models with an Application in Stock Forecasting," Post-Print hal-03184841, HAL.
    4. Pizzinga, Adrian, 2009. "Further investigation into restricted Kalman filtering," Statistics & Probability Letters, Elsevier, vol. 79(2), pages 264-269, January.

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