Developmental evidence of acrocarpy in Hedwigia ciliata (Musci: Hedwigiaceae)

The growth habit of the Hedwigiaceae has been described variously as acrocarpous, pseudopleurocarpous, or pleurocarpous. Anatomical evidence presented here indicates that Hedwigia ciliata is acrocarpous. The archegonia are terminal on the main shoot, and the branching pattern is sympodial. The main axis of each plant thus consists of a succession of subterminal innovations, rather than a single shoot of indeterminate growth. Since the plants are plagiotropic and are pleurocarpous in appearance, this growth pattern can be also called pseudo-pleurocarpous

Bootstrap inference for K-nearest neighbour matching estimators

Abadie and Imbens (2008, Econometrica) showed that classical bootstrap schemes fail to provide correct inference for K-nearest neighbour (KNN) matching estimators of average causal effects. This is an interesting result showing that bootstrap should not be applied without theoretical justification. In this paper, we present two resampling schemes, which we show provide valid inference for KNN matching estimators. We resample "estimated individual causal effects" (EICE), i.e. the difference in outcome between matched pairs, instead of the original data. Moreover, by taking differences in EICEs ordered with respect to the matching covariate, we obtain a bootstrap scheme valid also with heterogeneous causal effects where mild assumptions on the heterogeneity are imposed. We provide proofs of the validity of the proposed resampling based inferences. A simulation study illustrates finite sample properties.Block bootstrap; subsampling; average causal/treatment effect

Bootstrap Inference for K-Nearest Neighbour Matching Estimators

Abadie and Imbens (2008, Econometrica) showed that classical bootstrap schemes fail to provide correct inference for K-nearest neighbour (KNN) matching estimators of average causal effects. This is an interesting result showing that bootstrap should not be applied without theoretical justification. In this paper, we present two resampling schemes, which we show provide valid inference for KNN matching estimators. We resample "estimated individual causal effects" (EICE), i.e. the difference in outcome between matched pairs, instead of the original data. Moreover, by taking differences in EICEs ordered with respect to the matching covariate, we obtain a bootstrap scheme valid also with heterogeneous causal effects where mild assumptions on the heterogeneity are imposed. We provide proofs of the validity of the proposed resampling based inferences. A simulation study illustrates finite sample properties.block bootstrap, subsampling, average causal/treatment effect

Causal inference taking into account unobserved confounding

Causal inference with observational data can be performed under an assumption of no unobserved confounders (unconfoundedness assumption). There is, however, seldom clear subject-matter or empirical evidence for such an assumption. We therefore develop uncertainty intervals for average causal effects based on outcome regression estimators and doubly robust estimators, which provide inference taking into account both sampling variability and uncertainty due to unobserved confounders. In contrast with sampling variation, uncertainty due unobserved confounding does not decrease with increasing sample size. The intervals introduced are obtained by deriving the bias of the estimators due to unobserved confounders. We are thus also able to contrast the size of the bias due to violation of the unconfoundedness assumption, with bias due to misspecification of the models used to explain potential outcomes. This is illustrated through numerical experiments where bias due to moderate unobserved confounding dominates misspecification bias for typical situations in terms of sample size and modeling assumptions. We also study the empirical coverage of the uncertainty intervals introduced and apply the results to a study of the effect of regular food intake on health. An R-package implementing the inference proposed is available.Comment: Biometrics 201

Access to financial services in Zambia

Despite the deep financial sector reforms undertaken in Zambia in the early 1990s, the expected benefits of establishing a market-based banking system has not materialized. In 2005 the banking system continued to be small and underdeveloped. Credit to the private sector by banks represented only 8 percent of GDP in 2005, which is slightly lower than the level registered in 1990. As in the early 1990s, only large corporations and a few small- and medium-size enterprises have access to credit in 2006. Moreover, less than 8 percent of Zambia's adult population had a bank account in 2005. And despite the open door policy to foreign financial institutions, which has been in place since Zambia's independence, only a few new banking products have been introduced by foreign banks to serve the needs of households and firms. This paper analyzes the factors that have prevented the development of a large and inclusive banking system in Zambia and highlights possible actions that may help improve access to finance in Zambia in both the short and long terms.Banks&Banking Reform,Financial Intermediation,Financial Crisis Management&Restructuring,Corporate Law,Banking Law

An emotional mess! Deciding on a framework for building a Dutch emotion-annotated corpus

Seeing the myriad of existing emotion models, with the categorical versus dimensional opposition the most important dividing line, building an emotion-annotated corpus requires some well thought-out strategies concerning framework choice. In our work on automatic emotion detection in Dutch texts, we investigate this problem by means of two case studies. We find that the labels joy, love, anger, sadness and fear are well-suited to annotate texts coming from various domains and topics, but that the connotation of the labels strongly depends on the origin of the texts. Moreover, it seems that information is lost when an emotional state is forcedly classified in a limited set of categories, indicating that a bi-representational format is desirable when creating an emotion corpus.Seeing the myriad of existing emotion models, with the categorical versus dimensional opposition the most important dividing line, building an emotion-annotated corpus requires some well thought-out strategies concerning framework choice. In our work on automatic emotion detection in Dutch texts, we investigate this problem by means of two case studies. We find that the labels joy, love, anger, sadness and fear are well-suited to annotate texts coming from various domains and topics, but that the connotation of the labels strongly depends on the origin of the texts. Moreover, it seems that information is lost when an emotional state is forcedly classified in a limited set of categories, indicating that a bi-representational format is desirable when creating an emotion corpus.P