Random regressions models to describe the genetic variation of milk yield over multiple parities in Buffaloes

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Jackknife for bias reduction in predictive regressions

One of the fundamental econometric models in finance is predictive regression. The standard least squares method produces biased coefficient estimates when the regressor is persistent and its innovations are correlated with those of the dependent variable. This article proposes a general and convenient method based on the jackknife technique to tackle the estimation problem. The proposed method reduces the bias for both single- and multiple-regressor models and for both short- and long-horizon regressions. The effectiveness of the proposed method is demonstrated by simulations. An empirical application to equity premium prediction using the dividend yield and the short rate highlights the differences between the results by the standard approach and those by the bias-reduced estimator. The significant predictive variables under the ordinary least squares become insignificant after adjusting for the finite-sample bias. These discrepancies suggest that bias reduction in predictive regressions is important in practical applications.

Semi-Parametric Weak Instrument Regressions with an Application to the Risk-Return Trade-off

Recent work shows that a low correlation between the instruments and the included variables leads to serious inference problems. We extend the local-to-zero analysis of models with weak instruments to models with estimated instruments and regressors and with higher-order dependence between instruments and disturbances. This makes this framework applicable to linear models with expectation variables that are estimated non-parametrically. Two examples of such models are the risk-return trade-off in finance and the impact of inflation uncertainty on real economic activity. Results show that inference based on Lagrange Multiplier (LM) tests is more robust to weak instruments than Wald-based inference. Using LM confidence intervals leads us to conclude that no statistically significant risk premium is present in returns on the S&P 500 index, excess holding yields between 6-month and 3-month Treasury bills, or in yen-dollar spot returns.

Finite-Sample Diagnostics for Multivariate Regressions with Applications to Linear Asset Pricing Models

In this paper, we propose several finite-sample specification tests for multivariate linear regressions (MLR) with applications to asset pricing models. We focus on departures from the assumption of i.i.d. errors assumption, at univariate and multivariate levels, with Gaussian and non-Gaussian (including Student t) errors. The univariate tests studied extend existing exact procedures by allowing for unspecified parameters in the error distributions (e.g., the degrees of freedom in the case of the Student t distribution). The multivariate tests are based on properly standardized multivariate residuals to ensure invariance to MLR coefficients and error covariances. We consider tests for serial correlation, tests for multivariate GARCH and sign-type tests against general dependencies and asymmetries. The procedures proposed provide exact versions of those applied in Shanken (1990) which consist in combining univariate specification tests. Specifically, we combine tests across equations using the MC test procedure to avoid Bonferroni-type bounds. Since non-Gaussian based tests are not pivotal, we apply the “maximized MC” (MMC) test method [Dufour (2002)], where the MC p-value for the tested hypothesis (which depends on nuisance parameters) is maximized (with respect to these nuisance parameters) to control the test’s significance level. The tests proposed are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995. Our empirical results reveal the following. Whereas univariate exact tests indicate significant serial correlation, asymmetries and GARCH in some equations, such effects are much less prevalent once error cross-equation covariances are accounted for. In addition, significant departures from the i.i.d. hypothesis are less evident once we allow for non-Gaussian errors.

Exact Skewness-Kurtosis Tests for Multivariate Normality and Goodness-of-fit in Multivariate Regressions with Application to Asset Pricing Models

We study the problem of testing the error distribution in a multivariate linear regression (MLR) model. The tests are functions of appropriately standardized multivariate least squares residuals whose distribution is invariant to the unknown cross-equation error covariance matrix. Empirical multivariate skewness and kurtosis criteria are then compared to simulation-based estimate of their expected value under the hypothesized distribution. Special cases considered include testing multivariate normal, Student t; normal mixtures and stable error models. In the Gaussian case, finite-sample versions of the standard multivariate skewness and kurtosis tests are derived. To do this, we exploit simple, double and multi-stage Monte Carlo test methods. For non-Gaussian distribution families involving nuisance parameters, confidence sets are derived for the the nuisance parameters and the error distribution. The procedures considered are evaluated in a small simulation experi-ment. Finally, the tests are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995.

Features of neighbourhood environments associated with obesity and related health behaviours in children: using multiple statistical approaches to identify obesogenic environments

Contexte: L'obésité chez les jeunes représente aujourd’hui un problème de santé publique à l’échelle mondiale. Afin d’identifier des cibles potentielles pour des stratégies populationnelles de prévention, les liens entre les caractéristiques du voisinage, l’obésité chez les jeunes et les habitudes de vie font de plus en plus l’objet d’études. Cependant, la recherche à ce jour comporte plusieurs incohérences. But: L’objectif général de cette thèse est d’étudier la contribution de différentes caractéristiques du voisinage relativement à l’obésité chez les jeunes et les habitudes de vie qui y sont associées. Les objectifs spécifiques consistent à: 1) Examiner les associations entre la présence de différents commerces d’alimentation dans les voisinages résidentiels et scolaires des enfants et leurs habitudes alimentaires; 2) Examiner comment l’exposition à certaines caractéristiques du voisinage résidentiel détermine l’obésité au niveau familial (chez le jeune, la mère et le père), ainsi que l’obésité individuelle pour chaque membre de la famille; 3) Identifier des combinaisons de facteurs de risque individuels, familiaux et du voisinage résidentiel qui prédisent le mieux l’obésité chez les jeunes, et déterminer si ces profils de facteurs de risque prédisent aussi un changement dans l’obésité après un suivi de deux ans. Méthodes: Les données proviennent de l’étude QUALITY, une cohorte québécoise de 630 jeunes, âgés de 8-10 ans au temps 1, avec une histoire d’obésité parentale. Les voisinages de 512 participants habitant la Région métropolitaine de Montréal ont été caractérisés à l’aide de : 1) données spatiales provenant du recensement et de bases de données administratives, calculées pour des zones tampons à partir du réseau routier et centrées sur le lieu de la résidence et de l’école; et 2) des observations menées par des évaluateurs dans le voisinage résidentiel. Les mesures du voisinage étudiées se rapportent aux caractéristiques de l’environnement bâti, social et alimentaire. L’obésité a été estimée aux temps 1 et 2 à l’aide de l’indice de masse corporelle (IMC) calculé à partir du poids et de la taille mesurés. Les habitudes alimentaires ont été mesurées au temps 1 à l'aide de trois rappels alimentaires. Les analyses effectuées comprennent, entres autres, des équations d'estimation généralisées, des régressions multiniveaux et des analyses prédictives basées sur des arbres de décision. Résultats: Les résultats démontrent la présence d’associations avec l’obésité chez les jeunes et les habitudes alimentaires pour certaines caractéristiques du voisinage. En particulier, la présence de dépanneurs et de restaurants-minutes dans le voisinage résidentiel et scolaire est associée avec de moins bonnes habitudes alimentaires. La présence accrue de trafic routier, ainsi qu’un faible niveau de prestige et d’urbanisation dans le voisinage résidentiel sont associés à l’obésité familiale. Enfin, les résultats montrent qu’habiter un voisinage obésogène, caractérisé par une défavorisation socioéconomique, la présence de moins de parcs et de plus de dépanneurs, prédit l'obésité chez les jeunes lorsque combiné à la présence de facteurs de risque individuels et familiaux. Conclusion: Cette thèse contribue aux écrits sur les voisinages et l’obésité chez les jeunes en considérant à la fois l'influence potentielle du voisinage résidentiel et scolaire ainsi que l’influence de l’environnement familial, en utilisant des méthodes objectives pour caractériser le voisinage et en utilisant des méthodes statistiques novatrices. Les résultats appuient en outre la notion que les efforts de prévention de l'obésité doivent cibler les multiples facteurs de risque de l'obésité chez les jeunes dans les environnements bâtis, sociaux et familiaux de ces jeunes.

Eco-labeling, rents, sales prices and occupancy rates: do LEED and Energy Star labeled offices obtain multiple premiums?

Drawing upon an updated and expanded dataset of Energy Star and LEED labeled commercial offices, this paper investigates the effect of eco-labeling on rental rates, sale prices and occupancy rates. Using OLS and robust regression procedures, hedonic modeling is used to test whether the presence of an eco-label has a significant positive effect on rental rates, sale prices and occupancy rates. The study suggests that estimated coefficients can be sensitive to outlier treatment. For sale prices and occupancy rates, there are notable differences between estimated coefficients for OLS and robust regressions. The results suggest that both Energy Star and LEED offices obtain rental premiums of approximately 3%. A 17% sale price premium is estimated for Energy Star labeled offices but no significant sale price premium is estimated for LEED labeled offices. Surprisingly, no significant occupancy premium is estimated for Energy Star labeled offices and a negative occupancy premium is estimated for LEED labeled offices.

Relations between coping and positive and negative outcomes in carers of persons with multiple sclerosis (MS)

This study examined relations between stress and coping predictors and negative and positive outcomes in MS caregiving. A total of 222 carers and their care-recipients completed questionnaires at Time 1 and three months later, Time 2 ( n = 155). Predictors included care-recipient characteristics ( age, time since diagnosis, course and life satisfaction), and Times 1 and 2 carer problems, stress appraisal and coping. Dependent variables were Time 2 negative ( anxiety, depression) and positive outcomes ( life satisfaction, positive affect, benefits). Regressions indicated that, overall, the hypothesised direct effects of stress appraisal and coping strategies on positive and negative outcomes were supported. The hypothesised stress-buffering effects of positive reframing coping were also supported. All but one of the coping strategies were related to both positive and negative outcomes; specifically, practical assistance coping emerged as a unique predictor of distress. Of the model predictors, care-recipient life satisfaction emerged as the strongest and most consistent predictor of both positive and negative outcomes except benefit finding. Findings support the role of care-recipient characteristics and the carer's appraisal and coping processes in shaping both positive and negative outcomes. The guiding framework and findings have the potential to inform interventions designed to promote well-being in carers.

Benefit finding in multiple sclerosis, and associations with positive and negative outcomes

This study examined the direct and stress-buffering effects of benefit finding on positive and negative outcomes. A total of 502 people with multiple sclerosis completed a questionnaire at Time 1 and, 3 months later, at Time 2 (n = 404). Measures of illness were collected at Time 1, and number of problems, stress appraisal, benefit finding, subjective health, and negative (global distress, negative affect) and positive (life satisfaction, positive affect, dyadic adjustment) outcomes were measured at Time 2. Factor analyses showed the Benefit Finding scale to have 2 dimensions: Personal Growth and Family Relations Growth. Hierarchical regressions showed that after controlling for the effects of demographics, illness, problems, and appraisal, benefit finding showed strong direct effects on the positive outcomes. Benefit finding did not have a direct effect on distress, or subjective health but had a weak association with negative affect. Family Relations Growth had a stress-buffering effect on distress.