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Consistent Quasi-Maximum Likelihood Estimation With Limited Information

In: Maximum Likelihood Estimation of Misspecified Models: Twenty Years Later

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  • Douglas Miller
  • Sang-Hak Lee

Abstract

In this chapter, we use the minimum cross-entropy method to derive an approximate joint probability model for a multivariate economic process based on limited information about the marginal quasi-density functions and the joint moment conditions. The modeling approach is related to joint probability models derived from copula functions, but we note that the entropy approach has some practical advantages over copula-based models. Under suitable regularity conditions, the quasi-maximum likelihood estimator (QMLE) of the model parameters is consistent and asymptotically normal. We demonstrate the procedure with an application to the joint probability model of trading volume and price variability for the Chicago Board of Trade soybean futures contract.

Suggested Citation

  • Douglas Miller & Sang-Hak Lee, 2003. "Consistent Quasi-Maximum Likelihood Estimation With Limited Information," Advances in Econometrics, in: Maximum Likelihood Estimation of Misspecified Models: Twenty Years Later, pages 149-164, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-9053(03)17007-7
    DOI: 10.1016/S0731-9053(03)17007-7
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