Regression Analysis of Multivariate Fractional Data
讨论了处理多元分数响应变量的回归模型和估计方法，包括条件均值模型和完全参数模型，并提出了新的参数化方法和模型设定检验，通过蒙特卡洛研究评估了估计量和检验的小样本性质。
The present article discusses alternative regression models and estimation methods for dealing with multivariate fractional response variables. Both conditional mean models, estimable by quasi-maximum likelihood, and fully parametric models (Dirichlet and Dirichlet-multinomial), estimable by maximum likelihood, are considered. A new parameterization is proposed for the parametric models, which accommodates the most common specifications for the conditional mean (e.g., multinomial logit, nested logit, random parameters logit, dogit). The text also discusses at some length the specification analysis of fractional regression models, proposing several tests that can be performed through artificial regressions. Finally, an extensive Monte Carlo study evaluates the finite sample properties of most of the estimators and tests considered.