Flexible Approximation of Subjective Expectations Using Probability Questions
提出一种灵活方法，用弱假设逼近经济主体对连续随机变量的主观累积分布函数，可解析或数值计算矩和分位数，并通过美国收入预期数据验证其优于错误设定的参数方法。
We propose a flexible method to approximate the subjective cumulative distribution function of an economic agent about the future realization of a continuous random variable. The method can closely approximate a wide variety of distributions while maintaining weak assumptions on the shape of distribution functions. We show how moments and quantiles of general functions of the random variable can be computed analytically and/or numerically. We illustrate the method by revisiting the determinants of income expectations in the United States. A Monte Carlo analysis suggests that a quantile-based flexible approach can be used to successfully deal with censoring and possible rounding levels present in the data. Finally, our analysis suggests that the performance of our flexible approach matches that of a correctly specified parametric approach and is clearly better than that of a misspecified parametric approach.