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Full Sample Maximum Likelihood Estimation of Dynamic Demand Models

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  • DESCHAMPS , Philippe J.

    (Université de Fribourg and CORE, Université catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium)

Abstract

The maximum likelihood estimation of dynamic demand models has usually been based on the likelihood function conditional on the first observations of the dependent variables. However, this neglects information which may be necessary for identifying the long-run structure. We formulate the unconditional likelihood of a general dynamic demand model involving arbitrary lag orders, express its analytical derivatives in a relatively simple form, and propose a reparameterization which is always welldefined. The methodology is illustrated with a small empirical application, using the levels version of the CBS model proposed by Barten (1989) and annual British data on four commodities.

Suggested Citation

  • DESCHAMPS , Philippe J., 1995. "Full Sample Maximum Likelihood Estimation of Dynamic Demand Models," LIDAM Discussion Papers CORE 1995049, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvco:1995049
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    File URL: https://sites.uclouvain.be/core/publications/coredp/coredp1995.html
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    More about this item

    Keywords

    Unconditional likelihood function; Dynamic demand models; Matrix differential calculus; Error-correction models;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • 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
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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