This paper introduces a bivariate version of the generalized accelerated failure time model. It allows for simultaneity in the econometric sense that the two realized outcomes depend structurally on each other.
We develop a new direct approach to approximating suprema of general empirical processes by a sequence of suprema of Gaussian processes, without taking the route of approximating whole empirical processes in the supremum norm.
In this paper we evaluate the premise from the recent literature on Monte Carlo studies that an empirically motivated simulation exercise is informative about the actual ranking of various estimators when applied to a particular problem.
The authors propose a nonparametric test of the hypothesis of conditional independence between variables of interest based on a generalization of the empirical distribution function.
A comparison of hazard rates of duration outcomes before and after policy changes is hampered by non-identification if there is unobserved heteogeneity in the effects and no model structure is imposed. We develop a discontinuity approach that overcomes this by exploiting variation in the moment at which different cohorts are exposed to the policy change, i.e. by considering spells crossing the policy change.
We highlight the importance of randomisation bias, a situation where the process of participation in a social experiment has been affected by randomisation per se.
This paper investigates the asymptotic properties of the Gaussian quasi-maximum-likelihood estimators (QMLE's) of the GARCH model augmented by including an additional explanatory variable- the so-called GARCH-X model.
This paper identifies an entirely different mechanism for long memmory generation by showing that it can naturally arise when a large number of simply linear homogenous economic subsystems with a short memory are interconnected to form a network such that the outputs of each of the subsystem are fed into the inputs of others.
We establish the consistency and asymptotic normality for a class of estimators that are linear combinations of a set of √ n− consistent estimators whose cardinality increases with sample size.
