Testing for differences in survey‐based density expectations: A compositional data approach
提出将调查中的密度预期视为成分数据，检验不同群体或不同时间点的密度预测差异。蒙特卡洛模拟显示该方法比基于KLIC的bootstrap方法更有效且计算更快，并用于欧洲央行和美国消费者调查数据。
Summary We propose to treat survey‐based density expectations as compositional data when testing either for heterogeneity in density forecasts across different groups of agents or for changes over time. Monte Carlo simulations show that the proposed test has more power relative to both a bootstrap approach based on the KLIC and an approach that involves multiple testing for differences of individual parts of the density. In addition, the test is computationally much faster than the KLIC‐based one, which relies on simulations, and allows for comparisons across multiple groups. Using density expectations from the ECB Survey of Professional Forecasters and the US Survey of Consumer Expectations, we show the usefulness of the test in detecting possible changes in density expectations over time and across different types of forecasters.