We introduce a new estimation framework which extends the Generalized Method of Moments (GMM) to settings where a subset of the parameters vary over time with unknown dynamics. To filter out the dynamic path of the time-varying parameter, we approximate the dynamics by an autoregressive process...

We introduce a class of quantile regression estimators for short panels. Our framework covers static and dynamic autoregressive models, models with general predetermined regressors, and models with multiple individual effects. We use quantile regression as a flexible tool to model the...

We develop optimal formulations for nonlinear autoregressive models by representing them as linear autoregressive models with time-varying temporal dependence coefficients. We propose a parameter updating scheme based on the score of the predictive likelihood function at each time point. The...

This paper compares two approaches to analyzing longitudinal discrete-time binary outcomes. Dynamic binary response models focus on state occupancy and typically specify low-order Markovian state dependence. Multi-spell duration models focus on transitions between states and typically allow for...

This paper considers the estimation of dynamic threshold regression models with fixed effects using short panel data. We examine a two-step method, where the threshold parameter is estimated nonparametrically at the N-rate and the remaining parameters are estimated by GMM at the √N-rate. We...