This paper presents a hidden Markov model designed to investigate the complex
nature of earnings persistence. The proposed model assumes that the residuals
of log-earnings consist of a persistent component and a transitory component,
both following general Markov processes. Nonparametric identification is
achieved through spectral decomposition of linear operators, and a modified
stochastic EM algorithm is introduced for model estimation. Applying the
framework to the Panel Study of Income Dynamics (PSID) dataset, we find that
the earnings process displays nonlinear persistence, conditional skewness, and
conditional kurtosis. Additionally, the transitory component is found to
possess non-Gaussian properties, resulting in a significantly asymmetric
distributional impact when high-earning households face negative shocks or
low-earning households encounter positive shocks. Our empirical findings also
reveal the presence of ARCH effects in earnings at horizons ranging from 2 to 8
years, further highlighting the complex dynamics of earnings persistence.

本文提出了一种隐马尔可夫模型，旨在研究收益持续性的复杂性质。应用该模型于收入动态面板研究数据集，研究发现收益过程表现出非线性持久性、条件偏度和条件峰度，与此同时，短期成分还具有非高斯性质。