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Joint Tests For Regularity And Autocorrelation In Allocation Systems

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  • DESCHAMPS, P.J.

Abstract

In the context of allocation models with vector autoregressive errors we propose a convenient procedure, based on the Lagrange multiplier principle, for testing any possible combination of absence of serial correlation, homogeneity, and symmetry against any possible alternative which specifies autocorrelation of an arbitrary given order. We also derive generic expressions for the maximum likelihood estimation of the models under six possible combinations of constraints. The methodology is illustrated with the Rotterdam model and the differential AIDS model, both estimated from the same quarterly British data. Copyright 1993 by John Wiley & Sons, Ltd.
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(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Deschamps, P.J., 1990. "Joint Tests For Regularity And Autocorrelation In Allocation Systems," Papers 9042, Tilburg - Center for Economic Research.
    • Handle: RePEc:fth:tilbur:9042
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    Cited by:

    1. Marian Vavra, 2013. "Testing for linear and Markov switching DSGE models," Working and Discussion Papers WP 3/2013, Research Department, National Bank of Slovakia.
    2. Psaradakis, Zacharias & Vávra, Marián, 2014. "On testing for nonlinearity in multivariate time series," Economics Letters, Elsevier, vol. 125(1), pages 1-4.
    3. Deschamps, Philippe J., 1998. "Full maximum likelihood estimation of dynamic demand models," Journal of Econometrics, Elsevier, vol. 82(2), pages 335-359, February.
    4. Deschamps, Philippe J., 2000. "Exact small-sample inference in stationary, fully regular, dynamic demand models," Journal of Econometrics, Elsevier, vol. 97(1), pages 51-91, July.
    5. SCHROYEN, Fred, 2011. "Attitudes towards income risk in the presence of quantity constraints," LIDAM Discussion Papers CORE 2011020, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

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