A One-Factor Multivariate Time Series Model of Metropolitan Wage Rates
本文提出并估计了一个单因子多元时间序列模型，该模型是多元指标（或因子分析）模型的动态推广，利用卡尔曼滤波算法通过最大似然方法估计，并基于洛杉矶都市区的行业工资数据估计了未观测的都市工资率，通过假设检验、模型诊断和样本外预测评估模型。
Abstract The paper formulates and estimates a single-factor multivariate time series model. The model is a dynamic generalization of the multiple indicator (or factor analysis) model. It is shown to be a special case of the general state space model and can be estimated by maximum likelihood methods using the Kalman filter algorithm. The model is used to obtain estimates of the unobserved metropolitan wage rate for Los Angeles, based on observations of sectoral wages within the Standard Metropolitan Statistical Area. Hypothesis tests, model diagnostics, and out-of-sample forecasts are used to evaluate the model.