A model is proposed to analyze longitudinal data where two response variables are available, one of which is a binary indicator of selection and the other is continuous and observed only if the first is equal to 1. The model also accounts for individual covariates and may be considered as a bivariate finite mixture growth model as it is based on three submodels: (i) a probit model for the selection variable; (ii) a linear model for the continuous variable; and (iii) a multinomial logit model for the class membership. To suitably address endogeneity, the first two components rely on correlated errors as in a standard selection model. The proposed approach is applied to the analysis of the dynamics of household portfolio choices based on an unbalanced panel dataset of Italian households over the 1998–2014 period. For this dataset, we identify three latent classes of households with specific investment behaviors and we assess the effect of individual characteristics on households’ portfolio choices. Our empirical findings also confirm the need to jointly model risky asset market participation and the conditional portfolio share to properly analyze investment behaviors over the life-cycle.

提出了一个用于分析纵向数据的模型，该模型中有两个响应变量可用，其中一个是选择的二元指标，另一个是连续的，只有在第一个等于1时才能观察到。该模型还考虑了各个协变量，并且可能是由于它基于三个子模型，因此被视为双变量有限混合增长模型：（i）选择变量的概率模型；（ii）连续变量的线性模型；（iii）类成员资格的多项式logit模型。为了适当地解决内生性，前两个组件依赖于标准选择模型中的相关误差。所建议的方法基于1998-2014年间意大利家庭的不平衡面板数据集，用于分析家庭投资组合选择的动态。对于此数据集，我们确定了三种具有特定投资行为的潜在住户类别，并评估了个人特征对住户投资组合选择的影响。我们的经验发现还证实，有必要对风险资产市场的参与度和有条件的投资组合进行建模，以正确分析整个生命周期内的投资行为。