Identifying and interpreting the factors in factor models via sparsity: Different approaches
比较了稀疏主成分分析和因子旋转两种方法，用于识别因子结构并解释因子含义，无需事先施加限制。蒙特卡洛模拟显示方法在小样本下也准确，应用于国际商业周期和美国经济数据，得到一致且经济含义清晰的因子结构。
Summary This paper considers different approaches for identifying the factor structure and interpreting the factors without imposing their interpretation via restrictions: sparse PCA and factor rotations. We establish a new consistency result for the factors estimated by sparse PCA. Monte Carlo simulations show that our methods accurately estimate the factor structure, even in small samples. We apply them to large datasets about international business cycles and the US economy. For each empirical application, they identify the same factor structure, offering a clear economic interpretation. These exploratory methods can in particular justify or complement approaches that impose the factor structure a priori.