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A General Inferential Framework for Singly-Truncated Bivariate Normal Models with Applications in Economics

Author

Listed:
  • Yin Liu

    (Zhongnan University of Economics and Law)

  • Guo-Liang Tian

    (Southern University of Science and Technology)

  • Chi Zhang

    (Shenzhen University)

  • Hong Qin

    (Zhongnan University of Economics and Law)

Abstract

To analyze the singly-truncated bivariate economic data, we establish a class of singly-truncated bivariate normal distributions via stochastically representing the original bivariate normal random vector as a mixture of the singly-truncated part and its complementary components. Aided with the stochastic representaion, we creatively construct two novel unified and simple algorithms—the expectation–maximization algorithm as well as the minorization–maximization algorithm—to calculate the maximum likelihood estimates of the means and covariance matrix for the model of interest. In addition, we also develop a DA algorithm for posterior sampling in Bayesian analysis. Both simulation results and two real data applications in economics, collaborated by comparisons with existing methods, demonstrate the effectiveness and stability of proposed methodologies.

Suggested Citation

  • Yin Liu & Guo-Liang Tian & Chi Zhang & Hong Qin, 2024. "A General Inferential Framework for Singly-Truncated Bivariate Normal Models with Applications in Economics," Computational Economics, Springer;Society for Computational Economics, vol. 64(5), pages 2747-2781, November.
    • Handle: RePEc:kap:compec:v:64:y:2024:i:5:d:10.1007_s10614-023-10525-w
    DOI: 10.1007/s10614-023-10525-w
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