Estimating Discrete Markov Models From Various Incomplete Data Schemes

The parameters of a discrete stationary Markov model are transition probabilities between states. Traditionally, data consist in sequences of observed states for a given number of individuals over the whole observation period. In such a case, the estimation of transition probabilities is straightforwardly made by counting one-step moves from a given state to another. In many real-life problems, however, the inference is much more difficult as state sequences are not fully observed, namely the state of each individual is known only for some given values of the time variable. A review of the problem is given, focusing on Monte Carlo Markov Chain (MCMC) algorithms to perform Bayesian inference and evaluate posterior distributions of the transition probabilities in this missing-data framework. Leaning on the dependence between the rows of the transition matrix, an adaptive MCMC mechanism accelerating the classical Metropolis-Hastings algorithm is then proposed and empirically studied.Comment: 26 pages - preprint accepted in 20th February 2012 for publication in Computational Statistics and Data Analysis (please cite the journal's paper

The linked survival prospects of siblings : evidence for the Indian states

This paper reports an analysis of micro-data for India that shows a high correlation in infant mortality among siblings. In 13 of 15 states, we identify a causal effect of infant death on the risk of infant death of the subsequent sibling (a scarring effect), after controlling for mother-level heterogeneity. The scarring effects are large, the only other covariate with a similarly large effect being mother’s (secondary or higher) education. The two states in which evidence of scarring is weak are Punjab, the richest, and Kerala, the socially most progressive. The size of the scarring effect depends upon the sex of the previous child in three states, in a direction consistent with son-preference. Evidence of scarring implies that policies targeted at reducing infant mortality will have social multiplier effects by helping avoid the death of subsequent siblings. Comparison of other covariate effects across the states offers some interesting new insights

Birth Spacing and Neonatal Mortality in India: Dynamics, Frailty and Fecundity

A dynamic panel data model of neonatal mortality and birth spacing is analyzed, accounting for causal effects of birth spacing on subsequent mortality and of mortality on the next birth interval, while controlling for unobserved heterogeneity in mortality (frailty) and birth spacing (fecundity). The model is estimated using micro data on about 30000 children of 7000 Indian mothers, for whom a complete retrospective record of fertility and child mortality is available. Information on sterilization is used to identify an equation for completion of family formation that is needed to account for right-censoring in the data. We find clear evidence of frailty, fecundity, and causal effects of birth spacing on mortality and vice versa, but find that birth interval effects can explain only a limited share of the correlation between neonatal mortality of successive children in a family.fertility, birth spacing, childhood mortality, health, dynamic panel data models, siblings.

Handling non-ignorable dropouts in longitudinal data: A conditional model based on a latent Markov heterogeneity structure

We illustrate a class of conditional models for the analysis of longitudinal data suffering attrition in random effects models framework, where the subject-specific random effects are assumed to be discrete and to follow a time-dependent latent process. The latent process accounts for unobserved heterogeneity and correlation between individuals in a dynamic fashion, and for dependence between the observed process and the missing data mechanism. Of particular interest is the case where the missing mechanism is non-ignorable. To deal with the topic we introduce a conditional to dropout model. A shape change in the random effects distribution is considered by directly modeling the effect of the missing data process on the evolution of the latent structure. To estimate the resulting model, we rely on the conditional maximum likelihood approach and for this aim we outline an EM algorithm. The proposal is illustrated via simulations and then applied on a dataset concerning skin cancers. Comparisons with other well-established methods are provided as well

Stockholding: From Participation to Location and to Participation Spillovers

This paper provides a joint analysis of household stockholding participation, stock location among stockholding modes, and participation spillovers, using data from the US Survey of Consumer Finances. Our multivariate choice model matches observed participation rates, conditional and unconditional, and asset location patterns. Financial education and sophistication strongly affect direct stockholding and mutual fund participation, while social interactions affect stockholding through retirement accounts only. Household characteristics influence stockholding through retirement accounts conditional on owning retirement accounts, unlike what happens with stockholding through mutual funds. Although stockholding is more common among retirement account owners, this fact is mainly due to their characteristics that led them to buy retirement accounts in the first place rather than of any informational advantages gained through retirement account ownership itself. Finally, our results suggest that, taking stockholding as given, stock location is not arbitrary but crucially depends on investor characteristics.Stockholding, asset location, retirement accounts, household finance, multivariate probit, simulated maximum likelihood.

Is it Easier to Escape from Low Pay in Urban Areas? Evidence from the UK

In this paper we compare periods of low pay employment between urban and rural areas in the UK. Using the British Household Panel Survey, we estimate the probability that a period of low pay employment will end allowing for a number of possible outcomes, namely to a ‘high pay’ job, self-employment, unemployment and out of the labour force. The results show that there are statistically significant differences in the dynamics of low pay across urban and rural labour markets, particularly in terms of exits to high pay and out of the labour force. After controlling for different personal and job characteristics across markets, urban low pay durations are somewhat shorter on average, with a higher probability that urban workers will move to high pay. However, the results suggest that any urban-rural differences in the typical low pay experience are particularly concentrated among certain types of individuals, e.g. young workers, women without qualifications.Preprin

Birth Spacing and Neonatal Mortality in India: Dynamics, Frailty and Fecundity

fertility;birth spacing;childhood mortality;health;dynamic panel data models;siblings

The Impact of Default Dependency and Collateralization on Asset Pricing and Credit Risk Modeling

This article presents a comprehensive framework for valuing financial instruments subject to credit risk and collateralization. In particular, we focus on the impact of default dependence on asset pricing, as correlated default risk is one of the most pervasive threats to financial markets. Some well-known risky valuation models in the markets can be viewed as special cases of this framework. We introduce the concept of comvariance (or comrelation) into the area of credit risk modeling to capture the default relationship among three or more parties. Accounting for default correlations and comrelations becomes important, especially during the credit crisis. Moreover, we find that collateralization works well for financial instruments subject to bilateral credit risk, but fails for ones subject to multilateral credit risk