To date, most multilevel methodologies can only unbiasedly model macro-micro situations, wherein group-level explanatory variables (e.g., city temperature) are used to predict an individual-level outcome variable (e.g., citizen personality). In contrast, this R package enables researchers to unbiasedly model micro-macro situations, wherein individual-level explanatory variables (and group-level explanatory variables) are used to predict a group-level outcome variable. In most micro-macro multilevel modeling, it is statistically biased to directly regress the group-level outcome variable on the unadjusted group means of individual-level explanatory variables (Croon & van Veldhoven, 2007). This R package is useful because it enables unbiased micro-macro multilevel modeling by producing the best linear unbiased predictors (BLUP) of the group means (i.e., the adjusted group means).
◘◘ Please cite Lu, Page-Gould, and Xu (2017) if you use this package.
◘ Link (upside and downside real, inflation, and short rate uncertainties)
◘◘ Please cite Xu (2019) if you use any data estimated from the model of this paper.
◘ Link (financial proxies to risk aversion and uncertainty; frequencies: daily and monthly)
◘◘ Please cite Bekaert, Engstrom, and Xu (2019) if you use any data estimated from the model of this paper.