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

Yin Liu, Guo-Liang Tian, Chi Zhang () and Hong Qin
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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

Computational Economics, 2024, vol. 64, issue 5, No 8, 2747-2781

Abstract: 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.

Keywords: Economic index; EM algorithm; MM algorithm; Singly-truncated bivariate normal distribution; Stochastic representation; Truncated data (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10614-023-10525-w

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