Aggregation with Log-Linear Models
研究当个体行为为对数线性模型（如柯布-道格拉斯生产函数）时，加总成宏观模型所需的条件，并讨论条件不满足时的加总偏误，提供定理、检验和实证结果。
When economic theory suggests a log-linear specification for individual agents, e.g., Cobb-Douglas production, it is common to estimate the same log-linear model with aggregate data, invoking a representative agent assumption and thereby assuming away aggregation errors. This paper gives necessary and sufficient restrictions on the distribution of agents in an economy for log-linear agent models to aggregate into log-linear macro models, and discusses the aggregation bias resulting from violation of these restrictions. Theorems, tests, economic rationales, and empirical results are given. Included are connections to random walks and to cointegration. Analogous results for log-level models are derived.