360 research outputs found
Comments on “Unobservable Selection and Coefficient Stability: Theory and Evidence” and “Poorly Measured Confounders are More Useful on the Left Than on the Right”
No full textAbstract–: We establish a link between the approaches proposed by Oster (2019) and Pei, Pischke, and Schwandt (2019) which contribute to the development of inferential procedures for causal effects in the challenging and empirically relevant situation where the unknown data-generation process is not included in the set of models considered by the investigator. We use the general misspecification framework recently proposed by De Luca, Magnus, and Peracchi (2018) to analyze and understand the implications of the restrictions imposed by the two approaches
New insights into the pathophysiology of cobalamin deficiency
No full textCobalamin-deficient (Cbl-D) central neuropathy in the rat is associated with a locally increased expression of neurotoxic tumour necrosis factor-α (TNF-α) and a locally decreased expression of neurotrophic epidermal growth factor (EGF). These recent findings suggest that cobalamin oppositely regulates the expression of TNF-α and EGF, and raise the possibility that these effects might be independent of its coenzyme function. Furthermore, adult Cbl-D patients have high levels of TNF-α and low levels of EGF in the serum and cerebrospinal fluid. Serum levels of TNF-α and EGF of cobalamin-treated patients normalize concomitantly with haematological disease remission. These observations suggest that cobalamin deficiency induces an imbalance in TNF-α and EGF levels in biological fluids that might have a role in the pathogenesis of the damage caused by pernicious anaemia
A Generalized Missing-indicator Approach to Regression with Imputed Covariates
No full textWe consider estimation of a linear regression model using data where some covariate values are missing but imputations are available to fill in the missing values. This situation generates a tradeoff between bias and precision when estimating the regression parameters of interest. Using only the subsample of complete observations does not cause bias but may imply a substantial loss of precision because the complete cases may be too few. On the other hand, filling in the missing values with imputations may cause bias. We provide the new Stata command gmi, which handles such tradeoff by using either model reduction or Bayesian model averaging techniques in the context of the generalized missing indicator approach recently proposed by Dardanoni, Modica, and Peracchi (2011, Journal of Econometrics 162: 362–368). If multiple imputations are available, gmi can also be combined with the built-in Stata prefix mi estimate to account for extra variability due to imputation. We illustrate the use of gmi with an empirical application in the health domain, where item nonresponse is substantial
A sample selection model for unit and item nonresponse in cross-sectional surveys
No full textWe consider a general sample selection model where unit and item nonresponse simultaneously affect a regression relationship of interest, and both types of nonresponse are potentially correlated. We estimate both parametric and semiparametric specifications of the model.
The parametric specification assumes that the errors in the latent regression equations follow a trivariate Gaussian distribution. The semiparametric specification avoids distributional assumptions about the underlying regression errors. In our empirical application, we estimate Engel curves for consumption expenditure using data from the first wave of SHARE (Survey on
Health, Aging and Retirement in Europe)
Estimating Engel curves under unit and item nonresponse
No full textThis paper estimates food Engel curves using data from the first wave of the Survey on Health, Aging
and Retirement in Europe (SHARE). Our statistical model simultaneously takes into account selectivity
due to unit and item nonresponse, endogeneity problems, and issues related to flexible specification of
the relationship of interest. We estimate both parametric and semiparametric specifications of the model.
The parametric specification assumes that the unobservables in the model follow a multivariate Gaussian
distribution, while the semiparametric specification avoids distributional assumptions about the unobservables
Height and the normal distribution: evidence from Italian military data
No full textResearchers modeling historical heights have typically relied on the restrictive assumption of a normal distribution, only the mean of which is affected by age, income, nutrition, disease, and similar influences. To avoid these restrictive assumptions, we develop a new semiparametric approach in which covariates are allowed to affect the entire distribution without imposing any parametric shape. We apply our method to a new database of height distributions for Italian provinces, drawn from conscription records, of unprecedented length and geographical disaggregation. Our method allows us to standardize distributions to a single age and calculate moments of the distribution that are comparable through time. Our method also allows us to generate counterfactual distributions for a range of ages, from which we derive age-height profiles. These profiles reveal how the adolescent growth spurt (AGS) distorts the distribution of stature, and they document the earlier and earlier onset of the AGS as living conditions improved over the second half of the nineteenth century. Our new estimates of provincial mean height also reveal a previously unnoticed "regime switch" from regional convergence to divergence in this period
Counter-Narratives Against Gender-Based Violence. A Twofold Perspective on Choices in Interactive Dramas
No full textRegardlessoftheincreasingnumberofinitiativesandactivistcounter- narratives aimed at fighting the hetero-normative patriarchal beliefs, the current Italian social structure still justifies gender-based violence, while the number of abuses remains chiefly unaltered. Within such a complex framework, this study intends to advance the discussion on how IDNs can contribute to sensitive topics such as Violence Against Women and Girls (VAWG) to trigger social change, showing the relevance of involving its protagonists for better addressing such a complex and urgent issue, increasing the possibilities of encouraging positive shifts in ideals and behaviours. This study reports on the co-design of an IDN that involved both survivors and volunteers from an anti-violence centre, and how their engagement provided fundamental insights on the necessity to describe survivors’ struggles sensibly and invite bystanders to reconsider their opinion on gender- based violence victims. Building on procedural rhetoric and narrative immersion, the artefact targets non-victims, putting them in women’s shoes to emphasise how violence is never caused by the victim’s choices but by those of the perpetrator. Ultimately, the results of the testing on 83 people who assessed the artefact with pre- and post-experience questionnaires are presented and discussed, showing the IDN effectiveness in tackling the topic and creating a meaningful experience
Weighted-average least squares estimation of generalized linear models
No full textThe weighted-average least squares (WALS) approach, introduced by Magnus et al. (2010) in the context of Gaussian linear models, has been shown to enjoy important advantages over other strictly Bayesian and strictly frequentist model-averaging estimators when accounting for problems of uncertainty in the choice of the regressors. In this paper we extend the WALS approach to deal with uncertainty about the specification of the linear predictor in the wider class of generalized linear models (GLMs). We study the large-sample properties of the WALS estimator for GLMs under a local misspecification framework, and the finite-sample properties of this estimator by a Monte Carlo experiment the design of which is based on a real empirical analysis of attrition in the first two waves of the Survey of Health, Aging and Retirement in Europe (SHARE)
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