Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate
提出在预测总量时，将分解变量直接加入总量模型，而非组合分解预测或使用单变量总量模型。通过理论分析和蒙特卡洛模拟，发现加入分解信息能提高预测精度，并用美国部门通胀数据验证。
To forecast an aggregate, we propose adding disaggregate variables, instead of combining forecasts of those disaggregates or forecasting by a univariate aggregate model. New analytical results show the effects of changing coefficients, misspecification, estimation uncertainty, and mismeasurement error. Forecast-origin shifts in parameters affect absolute, but not relative, forecast accuracies; misspecification and estimation uncertainty induce forecast-error differences, which variable-selection procedures or dimension reductions can mitigate. In Monte Carlo simulations, different stochastic structures and interdependencies between disaggregates imply that including disaggregate information in the aggregate model improves forecast accuracy. Our theoretical predictions and simulations are corroborated when forecasting aggregate United States inflation pre and post 1984 using disaggregate sectoral data.