Multivariate regression analysis of panel data with binary outcomes applied to unemployment data

Summary  In panel studies binary outcome measures together with time stationary and time varying explanatory variables are collected over time on the same individual. Therefore, a regression analysis for this type of data must allow for the correlation among the outcomes of an individual. The multivariate probit model of Ashford and Sowden (1970) was the first regression model for multivariate binary responses. However, a likelihood analysis of the multivariate probit model with general correlation structure for higher dimensions is intractable due to the maximization over high dimensional integrals thus severely restricting ist applicability so far. Czado (1996) developed a Markov Chain Monte Carlo (MCMC) algorithm to overcome this difficulty. In this paper we present an application of this algorithm to unemployment data from the Panel Study of Income Dynamics involving 11 waves of the panel study. In addition we adapt Bayesian model checking techniques based on the posterior predictive distribution (see for example Gelman et al. (1996)) for the multivariate probit model. These help to identify mean and correlation specification which fit the data well. C. Czado was supported by research grant OGP0089858 of the Natural Sciences and Engineering Research Council of Canada.     

Estimation of long-run inefficiency levels: a dynamic frontier approach

Cornwell, Schmidt, and Sickles (1990) and Kumbhakar (1990), among others, developed stochasticfrontier production models which allow firm specific inefficiency levels to change over time. These studies assumed arbitrary restrictions on the short-run dynamics of efficiency levels which have little theoretical justification. Further, the models are inappropriate for estimation of long-run efficiencies. We consider estimation of an alternative frontier model in which firmspecific technical inefficiency levels are autoregressive. This model is particularly useful to examine a potential dynamic link between technical innovations and production inefficiency levels. We apply our methodology to a panel of US airlines.     

The Influence of Intimate Violence on Transitions out of Cohabitation

Does the experience of violence in a cohabiting union lead participants away from marriage and toward separation, or does violence have only minimal impact once other characteristics of unions and their participants are controlled? This issue is examined using a sample of 411 cohabiting couples followed in both waves of the National Survey of Families and Households. Marriage and separation are treated as competing risks. Results show that violence does have an effect, although dissimilar effects emerge for transitions to separation, as opposed to marriage. Net of other factors, intense male violence—male violence that is more severe than the female partner's—raises the hazard of separation. In contrast, female violence, but not male violence, lowers the rate of marriage. The findings appear robust to a variety of operationalizations of partner violence.     

Bivariate income distributions with lognormal conditionals

In this paper, the most general bivariate distribution with lognormal conditionals is fully characterized, using the methodology proposed by [3]. The properties of the new family are studied in detail, including marginal and conditional distributions, regression functions, dependence measures, moments and inequality measures. The new distribution is very broad, and contains as a particular case the classical bivariate lognormal distribution. Several subfamilies are studied and a generalization of the basic model is discussed. Finally, we present an empirical application. We estimate and compare the basic model proposed in the paper with a classical model, using data from the European Community Household Panel in different periods of time.     

Stochastic Production Frontier and Technical Inefficiency: A Sensitivity Analysis

The present paper focuses attention on the sensitivity of technical inefficiency to most commonly used one-sided distributions of the inefficiency error term, namely the truncated normal, the half-normal, and the exponential distributions. A generalized version of the half-normal, which does not embody the zero-mean restriction, is also explored. For each distribution, the likelihood function and the counterpart of the estimator of technical efficiency are explicitly stated (Jondrow, J., Lovell, C. A. K., Materov, I. S., Schmidt, P. ([1982]), On estimation of technical inefficiency in the stochastic frontier production function model, J. Econometrics19:233-238). Based on our panel data set, related to Tunisian manufacturing firms over the period 1983-1993, formal tests lead to a strong rejection of the zero-mean restriction embodied in the half normal distribution. Our main conclusion is that the degree of measured inefficiency is very sensitive to the postulated assumptions about the distribution of the one-sided error term. The estimated inefficiency indices are, however, unaffected by the choice of the functional form for the production function.     

Two‐Wave Panel Analysis: Comparing Statistical Methods for Studying the Effects of Transitions

Study of the effect of transitions on individual and family outcomes is central to understanding families over the life course. There is little consensus, however, on the appropriate statistical methods needed to study transitions in panel data. This article compares lagged dependent variable (LDV) and change score (CS) methods for analyzing the effect of events in two‐wave panel data. The methods are described, and their performances are compared both with a simulation and a substantive example using the National Survey of Families and Households two‐wave panel. The results suggest that CS methods have advantages over LDV techniques in estimating the effect of events on outcomes in two‐wave panel data.     

二元结构、实际汇率与巴拉萨—萨缪尔森效应——基于面板门限模型的实证分析

运用Hansen提出的面板门限模型,以二元结构为门限变量,研究了43个国家1980~2010年不同收入国家,基于二元结构变化的巴拉萨—萨缪尔森效应的大小(B-S效应),研究发现:二元结构对B-S效应的影响存在明显的双门限效应,高收入国家不存在二元结构问题,其B-S效应显著;中等收入国家基本上跨过了第一门限值,其B-S效应存在,但弱于高收入国家,而低收入国家基本不存在B-S效应;表明在不同二元结构下,由于工资不会同劳动生产率同步增长,劳动生产率对实际汇率的存在不完全传导机制,可以解释"人民币汇率之谜"。     

A bias analysis of Weibull models under heaped data

Retrospectively collected duration data are often reported incorrectly. An important type of such an error is heaping—respondents tend to round-off or round-up the data according to some rule of thumb. For two special cases of the Weibull model we study the behaviour of the ‘naive estimators’, which simply ignore the measurement error due to heaping, and derive closed expressions for the asymptotic bias. These results give a formal justification of empirical evidence and simulation-based findings reported in the literature. Additionally, situations where a remarkable bias has to be expected can be identified, and an exact bias correction can be performed.     

Panel regression models for measuring multidimensional poverty dynamics

This work concerns the study of poverty dynamics and the analysis of the influencing socio-demographic factors. A fuzzy and multidimensional approach has been chosen in order to define two different poverty measures. A panel regression model has been estimated and particular attention has been paid to the treatment of the unobservable heterogeneity among longitudinal units. The specified model combines autoregression with variance components. The empirical analysis has been conducted using the data set of the British Household Panel Survey (BHPS) from 1991 to 1997. This work was co-financed by Murst funds for the projects “Occupazione e disoccupazione in Italia: misura e analisi dei comportamenti”. The paper is the result of the common work of all the authors; in particular G. Betti has written Sects. 2,5.1 and 5.3.1; A. D’Agostino has written sections 4, 5.2 and 5.4; L. Neri has written Sects. 1, 3, 5.3.2 and 6.     

Robust scale estimation in the error-components model using the empirical characteristic function

Robust estimators of the scale parameters in the error-components model are described. The new estimators are based on the empirical characteristic functions of appropriate sets of residuals and are affine equivariant, consistent and asymptotically normal. The robustness of the new estimators is investigated via influence-function calculations. The results of Monte Carlo experiments and an example based on real data illustrate the usefulness of the estimators.