A shared-parameter continuous-time hidden Markov and survival model for longitudinal data with informative dropout

A shared-parameter approach for jointly modeling longitudinal and survival data is proposed. With respect to available approaches, it allows for time-varying random effects that affect both the longitudinal and the survival processes. The distribution of these random effects is modeled according to a continuous-time hidden Markov chain so that transitions may occur at any time point. For maximum likelihood estimation, we propose an algorithm based on a discretization of time until censoring in an arbitrary number of time windows. The observed information matrix is used to obtain standard errors. We illustrate the approach by simulation, even with respect to the effect of the number of time windows on the precision of the estimates, and by an application to data about patients suffering from mildly dilated cardiomyopathy

Credible Threats in a Wage Bargaining Model with on-the-job Search

In standard equilibrium search models with strategic wage bargaining and on-the-job search, renegotiation is permitted without requirement of a credible threat. Workers trigger renegotiation whenever they have a new outside option that could raise their wages. In this note I modify the model to be consistent with renegotiation by mutual agreement and I show that estimating the model without imposing credible threats for renegotiation generates downward bias in the estimates of the bargaining power.Credible Threats; On-the-job search; Wage bargaining

Business Cycles and Wage Rigidity

This paper analyzes the impact of downward wage rigidity on the labor market. It shows that imposing downward wage rigidity in a matching model with cyclical fluctuations in productivity, endogenous match-destruction, and on-the-job search, quits are procyclical and layoffs countercyclical. It provides evidence that downward wage rigidity is empirically relevant in ten European countries. It finally shows that layoffs are countercyclical and quits are procyclical, as predicted by the model.Downward wage rigidity; Business cycles; Wage renegotiation

A global existence result for a Keller-Segel type system with supercritical initial data

We consider a parabolic-elliptic Keller-Segel type system, which is related to a simplified model of chemotaxis. Concerning the maximal range of existence of solutions, there are essentially two kinds of results: either global existence in time for general subcritical ($\|\rho_0\|_1<8\pi$) initial data, or blow--up in finite time for suitably chosen supercritical ($\|\rho_0\|_1>8\pi$) initial data with concentration around finitely many points. As a matter of fact there are no results claiming the existence of global solutions in the supercritical case. We solve this problem here and prove that, for a particular set of initial data which share large supercritical masses, the corresponding solution is global and uniformly bounded

Information matrix for hidden Markov models with covariates

For a general class of hidden Markov models that may include time-varying covariates, we illustrate how to compute the observed information matrix, which may be used to obtain standard errors for the parameter estimates and check model identifiability. The proposed method is based on the Oakes’ identity and, as such, it allows for the exact computation of the information matrix on the basis of the output of the expectation-maximization (EM) algorithm for maximum likelihood estimation. In addition to this output, the method requires the first derivative of the posterior probabilities computed by the forward-backward recursions introduced by Baum and Welch. Alternative methods for computing exactly the observed information matrix require, instead, to differentiate twice the forward recursion used to compute the model likelihood, with a greater additional effort with respect to the EM algorithm. The proposed method is illustrated by a series of simulations and an application based on a longitudinal dataset in Health Economics

Supercritical Mean Field Equations on convex domains and the Onsager's statistical description of two-dimensional turbulence

We are motivated by the study of the Microcanonical Variational Principle within the Onsager's description of two-dimensional turbulence in the range of energies where the equivalence of statistical ensembles fails. We obtain sufficient conditions for the existence and multiplicity of solutions for the corresponding Mean Field Equation on convex and "thin" enough domains in the supercritical (with respect to the Moser-Trudinger inequality) regime. This is a brand new achievement since existence results in the supercritical region were previously known \un{only} on multiply connected domains. Then we study the structure of these solutions by the analysis of their linearized problems and also obtain a new uniqueness result for solutions of the Mean Field Equation on thin domains whose energy is uniformly bounded from above. Finally we evaluate the asymptotic expansion of those solutions with respect to the thinning parameter and use it together with all the results obtained so far to solve the Microcanonical Variational Principle in a small range of supercritical energies where the entropy is eventually shown to be concave.Comment: 35 pages. In this version we have added an interesting remark (please see Remark 1.17 p. 9). We have also slightly modified the statement of Proposition 1.14 at p.8 so to include a part of it in a separate 4-line Remark just after it (please see Remark 1.15 p.9