Nonlinear models and small sample performance of the generalized method of moments

In this paper I explore the issue of nonlinearity (both in the data generation process and in the functional form that establishes the relationship between the parameters and the data) regarding the poor performance of the Generalized Method of Moments (GMM) in small samples. To this purpose I build a sequence of models starting with a simple linear model and enlarging it progressively until I approximate a standard (nonlinear) neoclassical growth model. I then use simulation techniques to find the small sample distribution of the GMM estimators in each of the models.GMM, small sample, simulation

Supply chain management as a competitive advantage in the Spanish grocery sector

Adversarial relationships have long dominated business relationships, but Supply Chain Management (SCM) entails a new perspective. SCM requires a movement away from arms-length relationships toward partnership style relations. SCM involves integration, co-ordination and collaboration across organisations and throughout the supply chain. It means that SCM requires internal (intraorganisational) and external (interorganisational) integration. This paper analyses the relationship between internal and external integration processes, their effect on firms’ performance and their contribution to the achievement of a competitive advantage. Performance improvements are analysed through costs, stock out and lead time reductions. And, the achievement of a better competitive position is measured by comparing the firm’s performance with its competitors’ performance. To analyse this, an empirical study has been conducted in the Spanish grocery sector.Supply chain management, logistics integration processes, Internal and externalintegration, competitive advantage, logistics performance

Logistics-production, logistics-marketing and external integration: Their impact on performance

Highly competitive environments are leading companies to implement Supply Chain Management (SCM) to improve performance and gain a competitive advantage. SCM involves integration, co-ordination and collaboration across organisations and throughout the supply chain. It means that SCM requires internal (intraorganisational) and external (interorganisational) integration. This paper examines the Logistics-Production and Logistics- Marketing interfaces and their relation with the external integration process. The study also investigates the causal impact of these internal and external relationships on the company’s logistical service performance. To analyse this, an empirical study was conducted in the Spanish Fast Moving Consumer Goods (FMCG) sector.Logistics integration processes, internal and external integration, logistics performance

Lyfe-cycle effects on household expenditures: A latent-variable approach

Using data from the Spanish household budget survey, we investigate life- cycle effects on several product expenditures. A latent-variable model approach is adopted to evaluate the impact of income on expenditures, controlling for the number of members in the family. Two latent factors underlying repeated measures of monetary and non-monetary income are used as explanatory variables in the expenditure regression equations, thus avoiding possible bias associated to the measurement error in income. The proposed methodology also takes care of the case in which product expenditures exhibit a pattern of infrequent purchases. Multiple-group analysis is used to assess the variation of key parameters of the model across various household life-cycle typologies. The analysis discloses significant life-cycle effects on the mean levels of expenditures; it also detects significant life-cycle effects on the way expenditures are affected by income and family size. Asymptotic robust methods are used to account for possible non-normality of the data.Structural equations, multi-group analysis, life cycle effects, product expenditures

An Empirical Evaluation of Five Small Area Estimators

This paper compares five small area estimators. We use Monte Carlo simulation in the context of both artificial and real populations. In addition to the direct and indirect estimators, we consider the optimal composite estimator with population weights, and two composite estimators with estimated weights: one that assumes homogeneity of within area variance and squared bias and one that uses area-specific estimates of variance and squared bias. In the study with real population, we found that among the feasible estimators, the best choice is the one that uses area-specific estimates of variance and squared bias.Regional statistics, small areas, root mean square error, direct, indirect and composite estimators.

An empirical evaluation of small area estimators

This paper investigates the comparative performance of five small area estimators. We use Monte Carlo simulation in the context of both theoretical and empirical populations. In addition to the direct and indirect estimators, we consider the optimal composite estimator with population weights, and two composite estimators with estimated weights: one that assumes homogeneity of within area variance and square bias, and another one that uses area specific estimates of variance and square bias. It is found that among the feasible estimators, the best choice is the one that uses area specific estimates of variance and square bias.Regional statistics, small areas, root mean square error, direct, indirect and composite estimators

On the performance of small-area estimators: Fixed vs. random area parameters

Most methods for small-area estimation are based on composite estimators derived from design- or model-based methods. A composite estimator is a linear combination of a direct and an indirect estimator with weights that usually depend on unknown parameters which need to be estimated. Although model-based small-area estimators are usually based on random-effects models, the assumption of fixed effects is at face value more appropriate.Model-based estimators are justified by the assumption of random (interchangeable) area effects; in practice, however, areas are not interchangeable. In the present paper we empirically assess the quality of several small-area estimators in the setting in which the area effects are treated as fixed. We consider two settings: one that draws samples from a theoretical population, and another that draws samples from an empirical population of a labor force register maintained by the National Institute of Social Security (NISS) of Catalonia. We distinguish two types of composite estimators: a) those that use weights that involve area specific estimates of bias and variance; and, b) those that use weights that involve a common variance and a common squared bias estimate for all the areas. We assess their precision and discuss alternatives to optimizing composite estimation in applications.Small area estimation, composite estimator, Monte Carlo study, random effect model, BLUP, empirical BLUP

Improving small area estimation by combining surveys: new perspectives in regional statistics

A national survey designed for estimating a specific population quantity is sometimes used for estimation of this quantity also for a small area, such as a province. Budget constraints do not allow a greater sample size for the small area, and so other means of improving estimation have to be devised. We investigate such methods and assess them by a Monte Carlo study. We explore how a complementary survey can be exploited in small area estimation. We use the context of the Spanish Labour Force Survey (EPA) and the Barometer in Spain for our study.Composite estimator, complementary survey, mean squared error, official statistics, regional statistics, small area

Product expenditure patterns in the ECPF survey: an analysis using multiple group latent-variables models

Using data form the Spanish household budget survey, we investigate some aspects of household heterogeneity on several product expenditures. We adopt a latent-variable model approach to evaluate the impact of income on expenditures, controlling for the number of members in the family. Two latent factors underlying repeated measures of monetary and non-monetary income are used as explanatory variables in the expenditure regression equations, thus avoiding possible bias associated to the measurement error in income. The proposed methodology also takes care of the case in which product expenditures exhibit a pattern of infrequent purchases. Multiple-group analysis is used to assess the variation of key parameters of the model across various household typologies. The analysis discloses significant variations across groups on the mean levels of expenditures and on the way income and family size affect expenditures. Asymptotic robust methods are used to account for possible non-normality of the data

Product expenditure patterns in the ECPF survey: an analysis using multiple group latent-variables models

Using data form the Spanish household budget survey, we investigate some aspects of household heterogeneity on several product expenditures. We adopt a latent-variable model approach to evaluate the impact of income on expenditures, controlling for the number of members in the family. Two latent factors underlying repeated measures of monetary and non-monetary income are used as explanatory variables in the expenditure regression equations, thus avoiding possible bias associated to the measurement error in income. The proposed methodology also takes care of the case in which product expenditures exhibit a pattern of infrequent purchases. Multiple-group analysis is used to assess the variation of key parameters of the model across various household typologies. The analysis discloses significant variations across groups on the mean levels of expenditures and on the way income and family size affect expenditures. Asymptotic robust methods are used to account for possible non-normality of the data