The resilient frugivorous fauna of an urban forest fragment and its potential role in vegetation enrichment

Urban Ecosystems - Anthropocentric defaunation affects critical ecological processes, such as seed dispersal, putting ecosystems and biomes at risk, and leading to habitat impoverishment. Diverse...     

Do Cooperatives with Better Economic–Financial Indicators also have Better Socioeconomic Performance?

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - The overall performance of cooperatives is related to economic–financial results and actions focused on benefits for...     

Fractional approaches for the distribution of innovation sequence of INAR(1) processes

Abstract

In this paper, we present a fractional decomposition of the probability generating function of the innovation process of the first-order non-negative integer-valued autoregressive [INAR(1)] process to obtain the corresponding probability mass function. We also provide a comprehensive review of integer-valued time series models, based on the concept of thinning operators with geometric-type marginals. In particular, we develop two fractional approaches to obtain the distribution of innovation processes of the INAR(1) model and show that the distribution of the innovations sequence has geometric-type distribution. These approaches are discussed in detail and illustrated through a few examples.     

Measuring is Believing! Improving Conventional Indicators of Welfare State Development

Welfare effort (social spending as a percentage of GDP) has conventionally been the preferred measure for comparisons in space and time of the level of development of welfare states. However, a wide variety of other measuring systems are potentially available in this field, because there are different sources and different methods for calculating social spending, so a variety of indicators can be calculated, each of which best reflects a different aspect of social protection systems. This paper presents two innovations: first it proposes options other than those generally used to measure the relative development of welfare states. Secondly, it uses those new measurements to show that some widely accepted conclusions on the relative development of welfare states need to be reviewed.     

Alternative diff-in-diffs estimators with several pretreatment periods

This paper deals with the identification of treatment effects using difference-in-differences estimators when several pretreatment periods are available. We define a family of identifying nonnested assumptions that lead to alternative difference-in-differences estimators. We show that the most usual difference-in-differences estimators imply equivalence conditions for the identifying nonnested assumptions. We further propose a model that can be used to test multiple equivalence conditions without imposing any of them. We conduct a Monte Carlo analysis and apply our approach to several recent papers to show its practical relevance.     

Label‐ and Level‐Invariant Graphical Log‐Linear Models

We introduce two types of graphical log‐linear models: label‐ and level‐invariant models for triangle‐free graphs. These models generalise symmetry concepts in graphical log‐linear models and provide a tool with which to model symmetry in the discrete case. A label‐invariant model is category‐invariant and is preserved after permuting some of the vertices according to transformations that maintain the graph, whereas a level‐invariant model equates expected frequencies according to a given set of permutations. These new models can both be seen as instances of a new type of graphical log‐linear model termed the restricted graphical log‐linear model, or RGLL, in which equality restrictions on subsets of main effects and first‐order interactions are imposed. Their likelihood equations and graphical representation can be obtained from those derived for the RGLL models.     

Three Bartlett-type corrections for score statistics in symmetric nonlinear regression models

We present simple matrix formulae for corrected score statistics in symmetric nonlinear regression models. The corrected score statistics follow more closely a χ ² distribution than the classical score statistic. Our simulation results indicate that the corrected score tests display smaller size distortions than the original score test. We also compare the sizes and the powers of the corrected score tests with bootstrap-based score tests.