Educational disparities across regions: A multilevel analysis for Italy and Spain

In this paper, a multilevel analysis is applied to the OECD-PISA2006 data with the aim to compare factors affecting students’ achievement across Italy and Spain. The findings show that both countries are affected by strong internal regional heterogeneity, where some regions have achievement scores well above the OECD mean and other are placed among the worst performers in the OECD area. Although regional governments are more autonomous about educational policy-making in Spain, regional indicators exert a higher influence on educational results in Italy where educational system is strictly regulated by the national government.     

Modelling the Inverse Association between Breastfeeding and Contraceptive Use

Previous studies of the inverse association between breastfeeding and contraceptive use have relied on current status data on at least one of the variables. In this paper we suggest a new way of modelling the joint temporal association of the adoption of contraception and the termination of breastfeeding over the course of a birth interval. The approach is implemented with survey data from rural Mexico. It involves first estimating a competing-risks model in which both hazards are considered simultaneous, together with a set of relevant covariates. Conditional analyses are then carried out to estimate the hazards of each event conditional on the timing of the other. By comparing the covariates, we gauge the extent to which the occurrence of one event raises or lowers the hazard of the other. The results show that, in rural Mexico, the timing of the termination of breastfeeding and that of starting contraceptive use are closely related, and that there exists a surprisingly neat interval of 120 days in which the action takes place.     

Generalized order statistic processes and Pfeifer records

We introduce a uniform generalized order statistic process. It is a simple Markov process whose initial segment can be identified with a set of uniform generalized order statistics. A standard marginal transformation leads to a generalized order statistic process related to non-uniform generalized order statistics. It is then demonstrated that the nth variable in such a process has the same distribution as an nth Pfeifer record value. This process representation of Pfeifer records facilitates discussion of the possible limit laws for Pfeifer records and, in some cases, of sums thereof. Because of the close relationship between Pfeifer records and generalized order statistics, the results shed some light on the problem of determining the nature of the possible limiting distributions of the largest generalized order statistic.     

Change points in nonparametric regresion functions

Estimators of location and size of jumps or discontinuities in a regression function and/or its derivatives are proposed. The estimators are based on the analysis of residuals obtained from the locally weighted least squares regression. The proposed estimators adapt to both fixed and random designs. The asymptotic properties of the estimators are investigated. The method is illustrated through simulation studies.     

GMM Estimation of Count-Panel-Data Models With Fixed Effects and Predetermined Instruments

The “traditional” approach to the estimation of count-panel-data models with fixed effects is the conditional maximum likelihood estimator. The pseudo maximum likelihood principle can be used in these models to obtain orthogonality conditions that generate a robust estimator. This estimator is inconsistent, however, when the instruments are not strictly exogenous. This article proposes a generalized method of moments estimator for count-panel-data models with fixed effects, based on a transformation of the conditional mean specification, that is consistent even when the explanatory variables are predetermined. Two applications are discussed, the relationship between patents and research and development expenditures and the explanation of technology transfer.     

Weighting Elementary Prices in Consumer Price Index Construction Using Spatial Autocorrelation

The Consumer Price Indexes (CPI) are used in current economic systems to measure inflation. When constructing CPIs, however, official institutions have systematically overlooked the spatial dimension of elementary prices. Ignoring the fact that prices are collected at geographical locations implicitly implies considering prices as spatially independent, when in fact they are not. To solve this problem, this article proposes to weight basic price data by taking into account the spatial correlation they display. The weighted geometric and arithmetic means suggested generalize and improve the simple geometric and arithmetic means currently in use.     

A note on an integer valued time series model with Poisson–negative binomial marginal distribution

Abstract

This paper proposes a new model for autoregressive time series of counts in terms of a convolution of Poisson and negative binomial random variables, known as Poisson–negative binomial (PNB) distribution. The corresponding first-order integer valued time series models are developed and their properties are discussed. The geometric PNB and the geometric semi PNB distributions are also introduced and studied.     

Trend,Growth Rate,and Change Point Analysis—A Data Driven Approach

A data-driven technique is proposed to estimate the trend and relative growth rate of time series data. The method is based on the local linear regression smoother and the only assumption about the form of the trend and growth rate function is that they are smooth functions of time. We also extended the method for handling sudden shifts or changes in the trend or growth rate functions by adding dummy variables for the jumps. Simulation studies are carried out to see the performance of the proposed procedure. The method is applied to study the trend and growth rate of wheat production in India from 1951–2005.     

On Confidence Intervals for Process Capability Indices in a One-Way Random Model

In this article, we investigated the bootstrap calibrated generalized confidence limits for process capability indices C _pk for the one-way random effect model. Also, we derived Bissell's approximation formula for the lower confidence limit using Satterthwaite's method and calculated its coverage probabilities and expected values. Then we compared it with standard bootstrap (SB) method and generalized confidence interval method. The simulation results indicate that the confidence limit obtained offers satisfactory coverage probabilities. The proposed method is illustrated with the help of simulation studies and data sets.     

Bayesian bivariate survival analysis using the power variance function copula

Copula models have become increasingly popular for modelling the dependence structure in multivariate survival data. The two-parameter Archimedean family of Power Variance Function (PVF) copulas includes the Clayton, Positive Stable (Gumbel) and Inverse Gaussian copulas as special or limiting cases, thus offers a unified approach to fitting these important copulas. Two-stage frequentist procedures for estimating the marginal distributions and the PVF copula have been suggested by Andersen (Lifetime Data Anal 11:333–350, 2005), Massonnet et al. (J Stat Plann Inference 139(11):3865–3877, 2009) and Prenen et al. (J R Stat Soc Ser B 79(2):483–505, 2017) which first estimate the marginal distributions and conditional on these in a second step to estimate the PVF copula parameters. Here we explore an one-stage Bayesian approach that simultaneously estimates the marginal and the PVF copula parameters. For the marginal distributions, we consider both parametric as well as semiparametric models. We propose a new method to simulate uniform pairs with PVF dependence structure based on conditional sampling for copulas and on numerical approximation to solve a target equation. In a simulation study, small sample properties of the Bayesian estimators are explored. We illustrate the usefulness of the methodology using data on times to appendectomy for adult twins in the Australian NH&MRC Twin registry. Parameters of the marginal distributions and the PVF copula are simultaneously estimated in a parametric as well as a semiparametric approach where the marginal distributions are modelled using Weibull and piecewise exponential distributions, respectively.