Fully modified estimation of cointegrating vectors via var prewhitening: A simulation study

Summary In this paper we investigate, by simulation methods, the finite samples properties of the Fully Modified Least Squares (FMLS) estimator of cointegrating vectors when the long run covariance matrix is estimated via VAR prewhitening. We compare this estimator to the FMLS estimator based on an automatic or a fixed bandwidth kernel estimator of the long run covariance matrix. By and large, FMLS estimator based on VAR prewhitening perform better than FMLS based on fixed bandwidth or automatic bandwidth, with the latter behaving almost in the same way in finite samples. More importantly, the empirical distribution of a Wald test statistic built from VAR prewhitened FMLS is closer to the asymptoticχ ² distribution than those obtained from alternative kernel estimators. Thus, our findings strongly favor the use of VAR prewhitening in the FM correction of the OLS estimator. We would like to thank P. C. B. Phillips for his suggestions, two anonymous referees for detailed comments, and the participants of the IGIER (Milan), the University of Padova and the CIDE (Bologna) seminars for comments. This paper has been presented at the Econometric Society European Meeting 1984 held at Maastricht, Netherlands. We acknowledge financial support from MURST-Funds 40%. The usual disclaimers apply.     

A statistical framework for expression-based molecular classification in cancer

Summary. Genome-wide measurement of gene expression is a promising approach to the identification of subclasses of cancer that are currently not differentiable, but potentially biologically heterogeneous. This type of molecular classification gives hope for highly individualized and more effective prognosis and treatment of cancer. Statistically, the analysis of gene expression data from unclassified tumours is a complex hypothesis-generating activity, involving data exploration, modelling and expert elicitation. We propose a modelling framework that can be used to inform and organize the development of exploratory tools for classification. Our framework uses latent categories to provide both a statistical definition of differential expression and a precise, experiment-independent, definition of a molecular profile. It also generates natural similarity measures for traditional clustering and gives probabilistic statements about the assignment of tumours to molecular profiles.     

Demographic and Predeparture Factors Associated with Drinking and Alcohol-Related Consequences for College Students Completing Study Abroad Experiences

Abstract

Objective: Study abroad students are at risk for increased and problematic drinking behavior. As few efforts have been made to examine this at-risk population, the authors predicted drinking and alcohol-related consequences abroad from predeparture and site-specific factors. Participants: The sample consisted of 339 students completing study abroad programs. Methods: Participants filled out online measures at predeparture, abroad, and at postreturn. Results: The authors found that drinking and consequences abroad were predicted by a number of factors, including demographics (eg, younger age, male sex, Greek affiliation, white ethnicity), student factors (eg, low GPA, major area of study), study abroad site factors (eg, apartment living abroad, study in Europe), predeparture levels of drinking and consequences, sensation seeking, and goals related to social gathering. Conclusions: Findings can be used to inform campus policies for admission to study abroad programs as well as assist in the development of interventions targeted toward preventing risk for students during abroad experiences.     

“The adoption and implementation of performance measurement process in Italian public organisations: the influence of political,cultural and rational factors”

One of the main challenges in the field of performance measurement and management in public organisations is about how to use and manage performance measures effectively. This study aims to examine the stages involved in the process of utilisation of performance measures, i.e. adoption and implementation, and to investigate the political, cultural and rational factors that affect this process. For this purpose, a theoretical model from the literature has been extended and tested by using the data gathered through a survey involving the managers of several departments of Italian regions and municipal districts. The final sample has included 201 usable observations. The data analysis has been conducted using structural equation modelling. A significant cause-and-effect relationship was found between the stages of adoption and implementation. Moreover, it was found that adoption is driven significantly by rational factors. Otherwise actual implementation is influenced by factors addressed both by political considerations, such as external interest groups and by rational evaluations such as resources. The effects of other rational and cultural factors on implementation stage are mediated by adoption.     

Modeling trade policies under alternative market structures

The paper focuses on the importance of assumptions made about market structure and firm behavior in empirical trade policy analysis. The contribution to the relevant literature is 3-fold: first the paper develops two original models which incorporate imperfectly competitive market structures in a spatial modeling framework; then it proposes a procedure to identify the degree of market power in international trading which is consistent with observed prices and traded quantities, and applies it to the banana market; finally, it assesses how analysis of the implications of recent changes in the EU import regimes for bananas (the Economic Partnership Agreements and the December 2009 WTO agreement) is affected by the assumptions made on the prevailing market structure.     

A new rank correlation measure

A new rank correlation measure β _n is proposed, so as to develop a nonparametric test of independence for two variables. β _n is shown to be the symmetrized version of a measure earlier proposed by Borroni and Zenga (Stat Methods Appl 16:289–308, 2007). More specifically, β _n is built so that it can take the opposite sign, without changing its absolute value, when the ranking of one variable is reversed. Further, the meaning of the population equivalent of β _n is discussed. It is pointed out that this latter association measure vanishes not only at independence but, more generally, at indifference, that is when the two variables do not show any “tendency” to positive or negative dependence. The null distribution of β _n needs an independent study: hence, the finite null variance and a table of critical values are determined. Moreover, the asymptotic null distribution of β _n is derived. Finally, the performance of the test based on β _n is evaluated by simulation. β _n is shown to be a good competitor of some classical tests for the same problem.     

The power piecewise exponential model

In this paper an extension of the piecewise exponential distribution based on the distribution of the maximum of a random sample is considered. Properties of its density and hazard function are investigated. Maximum likelihood inference is discussed and the Fisher information matrix is identified. Results of two real data applications are reported, where model fitting is implemented by using maximum likelihood. The applications illustrate the better performance of the new distribution when compared with other recently proposed alternative models.     

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.     

On Bayesian learning via loss functions

We provide a decision theoretic approach to the construction of a learning process in the presence of independent and identically distributed observations. Starting with a probability measure representing beliefs about a key parameter, the approach allows the measure to be updated via the solution to a well defined decision problem. While the learning process encompasses the Bayesian approach, a necessary asymptotic consideration then actually implies the Bayesian learning process is best. This conclusion is due to the requirement of posterior consistency for all models and of having standardized losses between probability distributions. This is shown considering a specific continuous model and a very general class of discrete models.