Marginal zero-inflated regression models for count data

Data sets with excess zeroes are frequently analyzed in many disciplines. A common framework used to analyze such data is the zero-inflated (ZI) regression model. It mixes a degenerate distribution with point mass at zero with a non-degenerate distribution. The estimates from ZI models quantify the effects of covariates on the means of latent random variables, which are often not the quantities of primary interest. Recently, marginal zero-inflated Poisson (MZIP; Long et al. [A marginalized zero-inflated Poisson regression model with overall exposure effects. Stat. Med. 33 (2014), pp. 5151–5165]) and negative binomial (MZINB; Preisser et al., 2016) models have been introduced that model the mean response directly. These models yield covariate effects that have simple interpretations that are, for many applications, more appealing than those available from ZI regression. This paper outlines a general framework for marginal zero-inflated models where the latent distribution is a member of the exponential dispersion family, focusing on common distributions for count data. In particular, our discussion includes the marginal zero-inflated binomial (MZIB) model, which has not been discussed previously. The details of maximum likelihood estimation via the EM algorithm are presented and the properties of the estimators as well as Wald and likelihood ratio-based inference are examined via simulation. Two examples presented illustrate the advantages of MZIP, MZINB, and MZIB models for practical data analysis.     

An Empirical Analysis of Backlog,Inventory, Production,and Price Adjustments: An Application of Recursive Systems of Log-Linear Models

This article presents an empirical analysis of firms' order backlogs, inventories, production, and price adjustments to unanticipated demand shocks. The data are obtained from quarterly INSEE Business Survey Tests on firms' realizations, expectations, and appraisals of some various economic variables. The analysis is based on the formulation and the estimation of a recursive system of conditional log-linear probability models.     

A time series model with random coefficients

There are many time series applications where an experi­menter observes the simultaneous responses of several sub­systems over time. In these instances one is often not interested in the parameters of individual subsystems, but rather in an overall characterization of the system in question. Under the assumption that subsystems are independent and first order autoregressive, the present paper presents two methods for estimating the distribution of the subsystem coefficients.     

The measurement of capital utilisation rates

A strategy using spline function interpolation is developed f o r estimating capital utilisation rates . Cobb-Douglas, CES and translog functional forms are used in estimation. Tests for functional forms are conducted leading t o t h e s e l e c t i o n of the Cobb-Douglas form. Quarterly series of estimated utilisation rates and excess capacity measures are presented.     

A New Approach to Social Assistance: Latin America's Experience with Conditional Cash Transfer Programmes

Conditional cash transfers are a departure from more traditional approaches to social assistance that represents an innovative and increasingly popular channel for the delivery of social services. Conditional cash transfers provide money to poor families contingent upon certain behaviour, usually investments in human capital such as sending children to school or bringing them to health centres on a regular basis. They seek both to address traditional short-term income support objectives and promote the longer-term accumulation of human capital by serving as a demand-side complement to the supply of health and education services. Evaluation results reveal that this innovative design has been quite successful in addressing many of the failures in delivering social assistance such as poor poverty targeting, disincentive effects and limited welfare impacts. There is clear evidence of success from the first generation of programmes in Brazil, Colombia, Mexico and Nicaragua in increasing enrolment rates, improving preventive healthcare and raising household consumption. Despite this promising evidence, many questions remain unanswered about conditional cash transfer programmes, including the replicability of their success under different