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Modelling & Controlling Monetary and Economic Identities with Constrained State Space Models

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  • Gurupdesh S. Pandher

Abstract

The paper presents a method for modelling and controlling time series with identity structures. The approach is presented in the context of monetary targeting where the monetary identity (e.g. reserve money equals net foreign assets plus domestic credit) is modelled using a constrained state space model and next‐period changes in domestic credit (policy variable) are estimated to reach the target level of reserve money. The constrained modelling ensures that aggregation and identity relations among items are dynamically satisfied during estimation, leading to more accurate forecasting and targeting. Applications to Germany, UK and USA show that the constrained state space model provides significant improvements in targeting and forecasting performance over the AR(1) benchmark and the unconstrained model. Reduction in the mean square error of targeting over AR(1) is in the range of 76–95% for the three countries while the gain in targeting efficiency over unconstrained modelling is between 21% and 55%. Beyond monetary targeting, the method has wide application to the dynamic modelling and control of economic and financial time series with identity and aggregation constraints (e.g. balance of payment, national income, purchasing power parity, company balance sheet). L'article présente une méthode de modélisation et de contrôle des séries temporelles avec des structures d'identité. L'approche est présentée dans le contexte de ciblage monétaire où l'identité monétaire (c. à d. monnaie de réserve égale avoirs étrangers plus crédit intérieur) est modélisée en utilisant un modèle spatial sous contrainte et où les variations du crédit intérieur à la période suivante (variable de politique) sont estimés pour atteindre le niveau visé de monnaie de réserve. La modélisation sous contrainte assure que les relations d'agrégation et d'identité entre items sont satisfaites en dynamique dans l'estimation, ce qui conduit à des prévisions et ciblages plus précis. L'application à l'Allemagne, le Royaume‐Uni et les USA montrent que le modèle contraint apporte des améliorations importantes dans la performance de ciblage et de prévision par rapport à l'étalonnage auto‐régressif (1) et au modèle sans contrainte. La réduction d'erreur du moindre carré par rapport à l'AR est comprise entre 76 et 95% pour les trois pays tandis que le gain en efficacité de ciblage sur le modèle sans contrainte se situe entre 21 et 55%. Par delà le ciblage monétaire, la méthode a une large application à la modélisation dynamique et au contrôle des séries temporelles économiques et financières avec des contraintes d'identité et d'agrégation (par ex. la balance des paiements, le revenu national, la parité de pouvoir d'achat, le bilan d'une compagnie).

Suggested Citation

  • Gurupdesh S. Pandher, 2007. "Modelling & Controlling Monetary and Economic Identities with Constrained State Space Models," International Statistical Review, International Statistical Institute, vol. 75(2), pages 150-169, August.
  • Handle: RePEc:bla:istatr:v:75:y:2007:i:2:p:150-169
    DOI: 10.1111/j.1751-5823.2007.00012.x
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    1. Mario I. Blejer & Alfredo M. Leone & Pau Rabanal & Gerd Schwartz, 2002. "Inflation Targeting in the Context of IMF-Supported Adjustment Programs," IMF Staff Papers, Palgrave Macmillan, vol. 49(3), pages 1-2.
    2. Stanley Fischer, 1995. "Modern Approaches to Central Banking," NBER Working Papers 5064, National Bureau of Economic Research, Inc.
    3. International Monetary Fund, 1989. "Financial and Fiscal Programming Under Debt Rescheduling," IMF Working Papers 1989/061, International Monetary Fund.
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    1. Adrian Pizzinga, 2010. "Constrained Kalman Filtering: Additional Results," International Statistical Review, International Statistical Institute, vol. 78(2), pages 189-208, August.

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