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Replication script for Iacus, King, Porro (2018), "A Theory of Statistical Inference for Matching Methods in Causal Research"
No full textReplication script for the example and plots in
Iacus, King, Porro (2018), "A Theory of Statistical Inference for Matching Methods in Causal Research
Replication script for Iacus, King, Porro (2018), "A Theory of Statistical Inference for Matching Methods in Causal Research"
No full textReplication script for the example and plots in
Iacus, King, Porro (2018), "A Theory of Statistical Inference for Matching Methods in Causal Research
Don't ask, just listen ... Using social networks to measure subjective well‐being
No full textHow do people feel about their lives and the societies in which they live? Are they happy, hopeful or concerned about the future? Surveys can help answer these questions, but Stefano M. Iacus and Giuseppe Porro argue for using social networks and sentiment analysis to let citizens speak for themselve
Can educational policies today change income inequality tomorrow?
No full textIn this paper we have proposed a methodology to predict future labour market outcomes for students still in school, surveyed by the PISA programme. This is made possible by the existence of another survey conducted in the adult population, reporting comparable information on interviewees backgrounds (in addition to gender, age and country of origin, we consider parental education and family educational resources). Using coarsened exact matching technique, we impute adult numeracy and literacy, which are then used to predict hourly wages and employment probabilities. In order to highlight the working of our model, we run a counterfactual experiment where we reduce the student/teacher ratio in schools, showing the likely impact on expected wage inequality 25 years later
A Japanese Subjective Well-Being Indicator Based on Twitter Data
Get PDFThis study presents for the first time the SWB-J index, a subjective well-being indicator for Japan based on Twitter data. The index is composed by eight dimensions of subjective well-being and is estimated relying on Twitter data by using human supervised sentiment analysis. The index is then compared with the analogous SWB-I index for Italy in order to verify possible analogies and cultural differences. Further, through structural equation models, we investigate the relationship between economic and health conditions of the country and the well-being latent variable and illustrate how this latent dimension affects the SWB-J and SWB-I indicators. It turns out that, as expected, economic and health welfare is only one aspect of the multidimensional well-being that is captured by the Twitter-based indicator
A theory of statistical inference for matching methods in causal research
Get PDFResearchers who generate data often optimize efficiency and robustness by choosing stratified over simple random sampling designs. Yet, all theories of inference proposed to justify matching methods are based on simple random sampling. This is all the more troubling because, although these theories require exact matching, most matching applications resort to some form of ex post stratification (on a propensity score, distance metric, or the covariates) to find approximate matches, thus nullifying the statistical properties these theories are designed to ensure. Fortunately, the type of sampling used in a theory of inference is an axiom, rather than an assumption vulnerable to being proven wrong, and so we can replace simple with stratified sampling, so long as we can show, as we do here, that the implications of the theory are coherent and remain true. Properties of estimators based on this theory are much easier to understand and can be satisfied without the unattractive properties of existing theories, such as assumptions hidden in data analyses rather than stated up front, asymptotics, unfamiliar estimators, and complex variance calculations. Our theory of inference makes it possible for researchers to treat matching as a simple form of preprocessing to reduce model dependence, after which all the familiar inferential techniques and uncertainty calculations can be applied. This theory also allows binary, multicategory, and continuous treatment variables from the outset and straightforward extensions for imperfect treatment assignment and different versions of treatments
On penalized estimation for dynamical systems with small noise
Get PDFWe consider a dynamical system with small noise for which the drift is parametrized by a finite dimensional parameter. For this model, we consider minimum distance estimation from continuous time observations under lp-penalty imposed on the parameters in the spirit of the Lasso approach, with the aim of simultaneous estimation and model selection. We study the consistency and the asymptotic distribution of these Lasso-type estimators for different values of p. For p = 1, we also consider the adaptive version of the Lasso estimator and establish its oracle properties
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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