In this tutorial we introduce recurrent neural networks (RNNs), and we describe the two most popular RNN architectures. These are the long short-term memory (LSTM) network and gated recurrent unit (GRU) network. Their common field of application is time series modeling, and we demonstrate their...

Behavioral reactions to terrorism may be displayed in different ways. While some individuals react by holding back on their financial and charitable activities following stressful events, others may express more generosity by increasing their giving due to solidarity and empathy with the...

We investigate the association between age and medical spending in the U.S. using data from the Medical Expenditure Panel Survey (MEPS). We estimate a partially linear seminonparametric model and construct "pure" life-cycle profiles of health spending simultaneously controlling for time effects...

In this paper we construct life-cycle profiles of U.S. health care spending using data from the Medical Expenditure Panel Survey (MEPS). We separate pure age effects on health expenditure from time effects (i.e. productivity effects, business cycle effects, etc.) and cohort effects (i.e. initial...

This study tested the unbiased pricing hypothesis, the theory of storage, and the ability of past futures’ prices to forecast future changes in spot prices in the copper, aluminum, nickel and lead markets for the period October 2011 through May 2021. Wavelets and a time-varying parameter model...

This paper develops a simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. The new approach can easily deal with the commonly encountered and widely discussed initial conditions problem, as well as the...

This paper introduces bias-corrected estimators for nonlinear panel data models with both time invariant and time varying heterogeneity. These include limited dependent variable models with both unobserved individual effects and endogenous explanatory variables, and sample selection models with...

This paper develops a simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. The new approach can easily deal with the commonly encountered and widely discussed "initial conditions problem," as well as the...