State Dependence in a Multi-state Model of Employment

A multinomial choice framework is used to investigate the nature of women's transitions between full-time employment, part-time employment and non-employment. The stochastic framework allows time varying and time invariant unobserved preferences, and also controls for the possible endogenity of education, fertility and non-labor income. Significant positive true state dependence is found in both full-time and part-time employment. This finding is robust to the specification of unobserved preferences. The results are used the assess the dynamic effects of three temporary wage subsidies. All three policies have substantial effects on employment behavior for up to 6 years. However, obtaining a permanent increase in employment requires sustained or repeated interventions.Dynamic labor supply, Heterogeneity, Multinomial choice, State dependence.

Abstracting Asynchronous Multi-Valued Networks: An Initial Investigation

Multi-valued networks provide a simple yet expressive qualitative state based modelling approach for biological systems. In this paper we develop an abstraction theory for asynchronous multi-valued network models that allows the state space of a model to be reduced while preserving key properties of the model. The abstraction theory therefore provides a mechanism for coping with the state space explosion problem and supports the analysis and comparison of multi-valued networks. We take as our starting point the abstraction theory for synchronous multi-valued networks which is based on the finite set of traces that represent the behaviour of such a model. The problem with extending this approach to the asynchronous case is that we can now have an infinite set of traces associated with a model making a simple trace inclusion test infeasible. To address this we develop a decision procedure for checking asynchronous abstractions based on using the finite state graph of an asynchronous multi-valued network to reason about its trace semantics. We illustrate the abstraction techniques developed by considering a detailed case study based on a multi-valued network model of the regulation of tryptophan biosynthesis in Escherichia coli.Comment: Presented at MeCBIC 201

A Multi-Scan Labeled Random Finite Set Model for Multi-object State Estimation

State space models in which the system state is a finite set--called the multi-object state--have generated considerable interest in recent years. Smoothing for state space models provides better estimation performance than filtering by using the full posterior rather than the filtering density. In multi-object state estimation, the Bayes multi-object filtering recursion admits an analytic solution known as the Generalized Labeled Multi-Bernoulli (GLMB) filter. In this work, we extend the analytic GLMB recursion to propagate the multi-object posterior. We also propose an implementation of this so-called multi-scan GLMB posterior recursion using a similar approach to the GLMB filter implementation

Multi-state models for evaluating conversion options in life insurance

In this paper we propose a multi-state model for the evaluation of the conversion option contract. The multi-state model is based on age-indexed semi-Markov chains that are able to reproduce many important aspects that influence the valuation of the option such as the duration problem, the time non-homogeneity and the ageing effect. The value of the conversion option is evaluated after the formal description of this contract.Comment: Published at http://dx.doi.org/10.15559/17-VMSTA78 in the Modern Stochastics: Theory and Applications (https://www.i-journals.org/vtxpp/VMSTA) by VTeX (http://www.vtex.lt/

The multi-state hard core model on a regular tree

The classical hard core model from statistical physics, with activity $\lambda > 0$ and capacity $C=1$, on a graph $G$, concerns a probability measure on the set ${\mathcal I}(G)$ of independent sets of $G$, with the measure of each independent set $I \in {\mathcal I}(G)$ being proportional to $\lambda^{|I|}$. Ramanan et al. proposed a generalization of the hard core model as an idealized model of multicasting in communication networks. In this generalization, the {\em multi-state} hard core model, the capacity $C$ is allowed to be a positive integer, and a configuration in the model is an assignment of states from $\{0,\ldots,C\}$ to $V(G)$ (the set of nodes of $G$) subject to the constraint that the states of adjacent nodes may not sum to more than $C$. The activity associated to state $i$ is $\lambda^{i}$, so that the probability of a configuration $\sigma:V(G)\rightarrow \{0,\ldots, C\}$ is proportional to $\lambda^{\sum_{v \in V(G)} \sigma(v)}$. In this work, we consider this generalization when $G$ is an infinite rooted $b$-ary tree and prove rigorously some of the conjectures made by Ramanan et al. In particular, we show that the $C=2$ model exhibits a (first-order) phase transition at a larger value of $\lambda$ than the $C=1$ model exhibits its (second-order) phase transition. In addition, for large $b$ we identify a short interval of values for $\lambda$ above which the model exhibits phase co-existence and below which there is phase uniqueness. For odd $C$, this transition occurs in the region of \lambda = (e/b)^{1/\ceil{C/2}}, while for even $C$, it occurs around $\lambda=(\log b/b(C+2))^{2/(C+2)}$. In the latter case, the transition is first-order.Comment: Will appear in {\em SIAM Journal on Discrete Mathematics}, Special Issue on Constraint Satisfaction Problems and Message Passing Algorithm