Nonparametric Identification and Estimation of Earnings Dynamics using a Hidden Markov Model: Evidence from the PSID
This provides insights into earnings dynamics for economists and policymakers, though it is incremental as it builds on existing hidden Markov model approaches.
The paper tackles the problem of understanding earnings persistence by developing a hidden Markov model that decomposes earnings residuals into persistent and transitory components, finding nonlinear persistence, non-Gaussian properties, and ARCH effects in the PSID dataset.
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.