cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13207919.v1
title: Instrument Validity Tests with Causal Forests

description: Assumptions that are sufficient to identify local average treatment effects (LATEs) generate necessary conditions that allow instrument validity to be refuted. The degree to which instrument validity is violated, however, probably varies across subpopulations. In this paper, we use causal forests to search and test for such local violations of the LATE assumptions in a data-driven way. Unlike previous instrument validity tests, our procedure is able to detect local violations. We evaluate the performance of our procedure in simulations and apply it in two different settings: parental preferences for mixed-sex composition of children and the Vietnam draft lottery.
DOI: 10.6084/m9.figshare.13805729
title: The Locally Gaussian Partial Correlation

description: It is well known in econometrics and other fields that the dependence structure for jointly Gaussian variables can be fully captured using correlations, and that the <i>conditional</i> dependence structure in the same way can be described using partial correlations. The partial correlation does not, however, characterize conditional dependence in many non-Gaussian populations. This article introduces the local Gaussian partial correlation (LGPC), a new measure of conditional dependence. It is a local version of the partial correlation coefficient that characterizes conditional dependence in a large class of populations. It has some useful and novel properties besides: The LGPC reduces to the ordinary partial correlation for jointly normal variables, and it distinguishes between positive and negative conditional dependence. Furthermore, the LGPC can be used to study departures from conditional independence in specific parts of the distribution. We provide several examples of this, both simulated and real, and derive estimation theory under a local likelihood estimation framework. Finally, we indicate how the LGPC can be used to construct a powerful test for conditional independence, which, for example, can be used to detect nonlinear Granger causality in time series.
DOI: 10.6084/m9.figshare.13805729.v2
title: The Locally Gaussian Partial Correlation

description: It is well known in econometrics and other fields that the dependence structure for jointly Gaussian variables can be fully captured using correlations, and that the <i>conditional</i> dependence structure in the same way can be described using partial correlations. The partial correlation does not, however, characterize conditional dependence in many non-Gaussian populations. This article introduces the local Gaussian partial correlation (LGPC), a new measure of conditional dependence. It is a local version of the partial correlation coefficient that characterizes conditional dependence in a large class of populations. It has some useful and novel properties besides: The LGPC reduces to the ordinary partial correlation for jointly normal variables, and it distinguishes between positive and negative conditional dependence. Furthermore, the LGPC can be used to study departures from conditional independence in specific parts of the distribution. We provide several examples of this, both simulated and real, and derive estimation theory under a local likelihood estimation framework. Finally, we indicate how the LGPC can be used to construct a powerful test for conditional independence, which, for example, can be used to detect nonlinear Granger causality in time series.
DOI: 10.6084/m9.figshare.13805729.v1
title: The locally Gaussian partial correlation

description: It is well known in econometrics and other fields that the dependence structure for jointly Gaussian variables can be fully captured using correlations, and that the <i>conditional</i> dependence structure in the same way can be described using partial correlations. The partial correlation does not, however, characterize conditional dependence in many non-Gaussian populations. This paper introduces the local Gaussian partial correlation (LGPC), a new measure of conditional dependence. It is a local version of the partial correlation coefficient that characterizes conditional dependence in a large class of populations. It has some useful and novel properties besides: The LGPC reduces to the ordinary partial correlation for jointly normal variables, and it distinguishes between positive and negative conditional dependence. Furthermore, the LGPC can be used to study departures from conditional independence in specific parts of the distribution. We provide several examples of this, both simulated and real, and derive estimation theory under a local likelihood estimation framework. Finally, we indicate how the LGPC can be used to construct a powerful test for conditional independence, which, for example, can be used to detect nonlinear Granger causality in time series.
DOI: 10.6084/m9.figshare.13143129
title: Bayesian Model Averaging for Spatial Autoregressive Models Based on Convex Combinations of Different Types of Connectivity Matrices

description: There is a great deal of literature regarding use of nongeographically based connectivity matrices or combinations of geographic and non-geographic structures in spatial econometric models. We focus on convex combinations of weight matrices that result in a single weight matrix reflecting multiple types of connectivity, where coefficients from the convex combination can be used for inference regarding the relative importance of each type of connectivity in the global cross-sectional dependence scheme. We tackle the question of model uncertainty regarding selection of the best convex combination by Bayesian model averaging. We use Metropolis–Hastings guided Monte Carlo integration during MCMC estimation of the models to produce log-marginal likelihoods and associated posterior model probabilities. We focus on MCMC estimation, computation of posterior model probabilities, model averaged estimates of the parameters, scalar summary measures of the non-linear partial derivative impacts, and their associated empirical measures of dispersion.
DOI: 10.6084/m9.figshare.13143129.v2
title: Bayesian Model Averaging for Spatial Autoregressive Models Based on Convex Combinations of Different Types of Connectivity Matrices

description: There is a great deal of literature regarding use of nongeographically based connectivity matrices or combinations of geographic and non-geographic structures in spatial econometric models. We focus on convex combinations of weight matrices that result in a single weight matrix reflecting multiple types of connectivity, where coefficients from the convex combination can be used for inference regarding the relative importance of each type of connectivity in the global cross-sectional dependence scheme. We tackle the question of model uncertainty regarding selection of the best convex combination by Bayesian model averaging. We use Metropolis–Hastings guided Monte Carlo integration during MCMC estimation of the models to produce log-marginal likelihoods and associated posterior model probabilities. We focus on MCMC estimation, computation of posterior model probabilities, model averaged estimates of the parameters, scalar summary measures of the non-linear partial derivative impacts, and their associated empirical measures of dispersion.
DOI: 10.6084/m9.figshare.13143129.v1
title: Bayesian model averaging for spatial autoregressive models based on convex combinations of different types of connectivity matrices

description: There is a great deal of literature regarding use of non-geographically based connectivity matrices or combinations of geographic and non-geographic structures in spatial econometric models. We focus on convex combinations of weight matrices that result in a single weight matrix reflecting multiple types of connectivity, where coefficients from the convex combination can be used for inference regarding the relative importance of each type of connectivity in the global cross-sectional dependence scheme. We tackle the question of model uncertainty regarding selection of the best convex combination by Bayesian model averaging. We use Metropolis-Hastings guided Monte Carlo integration during MCMC estimation of the models to produce log-marginal likelihoods and associated posterior model probabilities. We focus on MCMC estimation, computation of posterior model probabilities, model averaged estimates of the parameters, scalar summary measures of the non-linear partial derivative impacts, and their associated empirical measures of dispersion.
DOI: 10.6084/m9.figshare.13341851.v1
title: Non-ignorable missing data, single index propensity score and profile synthetic distribution function

description: In missing data problems, missing not at random is difficult to handle since the response probability or propensity score is confounded with the outcome data model in the likelihood. Existing works often assume the propensity score is known up to a finite dimensional parameter. We relax this assumption and consider an unspecified single index model for the propensity score. A pseudo-likelihood based on the complete data is constructed by profiling out a synthetic distribution function that involves the unknown propensity score. The pseudo-likelihood gives asymptotically normal estimates. Simulations show the method compares favourably with existing methods.
DOI: 10.6084/m9.figshare.13373047
title: Assessing Causal Effects in a Longitudinal Observational Study With “Truncated” Outcomes Due to Unemployment and Nonignorable Missing Data

description: Important statistical issues pervade the evaluation of training programs’ effects for unemployed people. In particular, the fact that offered wages are observed and well-defined only for subjects who are employed (truncation by death), and the problem that information on the individuals’ employment status and wage can be lost over time (attrition) raise methodological challenges for causal inference. We present an extended framework for simultaneously addressing the aforementioned problems, and thus answering important substantive research questions, in training evaluation observational studies with covariates, a binary treatment and longitudinal information on employment status and wage, which may be missing due to the lost to follow-up. There are two key features of this framework: we use principal stratification to properly define the causal effects of interest and to deal with nonignorable missingness, and we adopt a Bayesian approach for inference. The proposed framework allows us to answer an open issue in economics: the assessment of the trend of reservation wage over the duration of unemployment. We apply our framework to evaluate causal effects of foreign language training programs in Luxembourg, using administrative data on the labor force (IGSS-ADEM dataset). Our findings might be an incentive for the employment agencies to better design and implement future language training programs.
DOI: 10.6084/m9.figshare.13373047.v2
title: Assessing Causal Effects in a Longitudinal Observational Study With “Truncated” Outcomes Due to Unemployment and Nonignorable Missing Data

description: Important statistical issues pervade the evaluation of training programs’ effects for unemployed people. In particular, the fact that offered wages are observed and well-defined only for subjects who are employed (truncation by death), and the problem that information on the individuals’ employment status and wage can be lost over time (attrition) raise methodological challenges for causal inference. We present an extended framework for simultaneously addressing the aforementioned problems, and thus answering important substantive research questions, in training evaluation observational studies with covariates, a binary treatment and longitudinal information on employment status and wage, which may be missing due to the lost to follow-up. There are two key features of this framework: we use principal stratification to properly define the causal effects of interest and to deal with nonignorable missingness, and we adopt a Bayesian approach for inference. The proposed framework allows us to answer an open issue in economics: the assessment of the trend of reservation wage over the duration of unemployment. We apply our framework to evaluate causal effects of foreign language training programs in Luxembourg, using administrative data on the labor force (IGSS-ADEM dataset). Our findings might be an incentive for the employment agencies to better design and implement future language training programs.
DOI: 10.6084/m9.figshare.13373047.v1
title: Assessing Causal Effects in a longitudinal observational study with “truncated” outcomes due to unemployment and nonignorable missing data

description: Important statistical issues pervade the evaluation of training programs’ effects for unemployed people. In particular the fact that offered wages are observed and well-defined only for subjects who are employed (truncation by death), and the problem that information on the individuals’ employment status and wage can be lost over time (attrition) raise methodological challenges for causal inference. We present an extended framework for simultaneously addressing the aforementioned problems, and thus answering important substantive research questions, in training evaluation observational studies with covariates, a binary treatment and longitudinal information on employment status and wage, which may be missing due to the lost to follow-up. There are two key features of this framework: we use principal stratification to properly define the causal effects of interest and to deal with non-ignorable missingness, and we adopt a Bayesian approach for inference. The proposed framework allows us to also at least partially answer an open issue in economics: the assessment of the trend of reservation wage over the duration of unemployment. We apply our framework to evaluate causal effects of foreign language training programs in Luxembourg, using administrative data on the labor force (IGSS-ADEM dataset). Our findings might be an incentive for the employment agencies to better design and implement future language training programs.
DOI: 10.6084/m9.figshare.13611039.v2
title: Adaptive Testing for Cointegration With Nonstationary Volatility

description: This article develops a class of adaptive cointegration tests for multivariate time series with nonstationary volatility. Persistent changes in the innovation variance matrix of a vector autoregressive model lead to size distortions in conventional cointegration tests, which may be resolved using the wild bootstrap, as shown in recent work by Cavaliere, Rahbek, and Taylor. We show that it also leads to the possibility of constructing tests with higher power, by taking the time-varying volatilities and correlations into account in the formulation of the likelihood function and the resulting likelihood ratio test statistic. We find that under suitable conditions, adaptation with respect to the volatility process is possible, in the sense that nonparametric volatility matrix estimation does not lead to a loss of asymptotic local power relative to the case where the volatilities are observed. The asymptotic null distribution of the test is nonstandard and depends on the volatility process; we show that various bootstrap implementations may be used to conduct asymptotically valid inference. Monte Carlo simulations show that the resulting test has good size properties, and higher power than existing tests. Empirical analyses of the U.S. term structure of interest rates and purchasing power parity illustrate the applicability of the tests.
DOI: 10.6084/m9.figshare.13611039
title: Adaptive Testing for Cointegration With Nonstationary Volatility

description: This article develops a class of adaptive cointegration tests for multivariate time series with nonstationary volatility. Persistent changes in the innovation variance matrix of a vector autoregressive model lead to size distortions in conventional cointegration tests, which may be resolved using the wild bootstrap, as shown in recent work by Cavaliere, Rahbek, and Taylor. We show that it also leads to the possibility of constructing tests with higher power, by taking the time-varying volatilities and correlations into account in the formulation of the likelihood function and the resulting likelihood ratio test statistic. We find that under suitable conditions, adaptation with respect to the volatility process is possible, in the sense that nonparametric volatility matrix estimation does not lead to a loss of asymptotic local power relative to the case where the volatilities are observed. The asymptotic null distribution of the test is nonstandard and depends on the volatility process; we show that various bootstrap implementations may be used to conduct asymptotically valid inference. Monte Carlo simulations show that the resulting test has good size properties, and higher power than existing tests. Empirical analyses of the U.S. term structure of interest rates and purchasing power parity illustrate the applicability of the tests.
DOI: 10.6084/m9.figshare.13611039.v1
title: Adaptive Testing for Cointegration with Nonstationary Volatility

description: This paper develops a class of adaptive cointegration tests for multivariate time series with nonstationary volatility. Persistent changes in the innovation variance matrix of a vector autoregressive model lead to size distortions in conventional cointegration tests, which may be resolved using the wild bootstrap, as shown by Cavaliere et al. (2010, 2014). We show that it also leads to the possibility of constructing tests with higher power, by taking the time-varying volatilities and correlations into account in the formulation of the likelihood function and the resulting likelihood ratio test statistic. We find that under suitable conditions, adaptation with respect to the volatility process is possible, in the sense that nonparametric volatility matrix estimation does not lead to a loss of asymptotic local power relative to the case where the volatilities are observed. The asymptotic null distribution of the test is nonstandard and depends on the volatility process; we show that various bootstrap implementations may be used to conduct asymptotically valid inference. Monte Carlo simulations show that the resulting test has good size properties, and higher power than existing tests. Empirical analyses of the US term structure of interest rates and purchasing power parity illustrate the applicability of the tests.
DOI: 10.6084/m9.figshare.13483246
title: Long Memory Factor Model: On Estimation of Factor Memories

description: This article considers the estimation of the integration orders of the latent factors in an approximate factor model. Both the common factors and idiosyncratic error terms are potentially nonstationary fractionally integrated processes. We propose a two-stage approach to estimate the factor memories. We show the consistency and asymptotic normality of the proposed estimator. Applying the estimator to the log-squared returns of the U.S. financial institutions, we find evidence of long memory in the estimated factor. We also find that the factor becomes more persistent after 2007.
DOI: 10.6084/m9.figshare.13483246.v1
title: Long Memory Factor Model: On Estimation of Factor Memories

description: This article considers the estimation of the integration orders of the latent factors in an approximate factor model. Both the common factors and idiosyncratic error terms are potentially nonstationary fractionally integrated processes. We propose a two-stage approach to estimate the factor memories. We show the consistency and asymptotic normality of the proposed estimator. Applying the estimator to the log-squared returns of the U.S. financial institutions, we find evidence of long memory in the estimated factor. We also find that the factor becomes more persistent after 2007.
DOI: 10.6084/m9.figshare.14670173.v1
title: Additional file 1 of The role of fibrinogen in predicting reinfection after DAIR for periprosthetic joint infections

description: Additional file 1.
DOI: 10.6084/m9.figshare.14670173
title: Additional file 1 of The role of fibrinogen in predicting reinfection after DAIR for periprosthetic joint infections

description: Additional file 1.
DOI: 10.6084/m9.figshare.14670202.v1
title: Additional file 2 of Comprehensive machine-learning-based analysis of microRNA–target interactions reveals variable transferability of interaction rules across species

description: Additional file 2. Table S8. Feature importance.
DOI: 10.6084/m9.figshare.14670208
title: Additional file 4 of Comprehensive machine-learning-based analysis of microRNA–target interactions reveals variable transferability of interaction rules across species

description: Additional file 4. Table S9. Features and their definition.
DOI: 10.6084/m9.figshare.14670202
title: Additional file 2 of Comprehensive machine-learning-based analysis of microRNA–target interactions reveals variable transferability of interaction rules across species

description: Additional file 2. Table S8. Feature importance.
DOI: 10.6084/m9.figshare.14670205
title: Additional file 3 of Comprehensive machine-learning-based analysis of microRNA–target interactions reveals variable transferability of interaction rules across species

description: Additional file 3. Grid search params.yaml.
DOI: 10.6084/m9.figshare.14670205.v1
title: Additional file 3 of Comprehensive machine-learning-based analysis of microRNA–target interactions reveals variable transferability of interaction rules across species

description: Additional file 3. Grid search params.yaml.
DOI: 10.6084/m9.figshare.14670208.v1
title: Additional file 4 of Comprehensive machine-learning-based analysis of microRNA–target interactions reveals variable transferability of interaction rules across species

description: Additional file 4. Table S9. Features and their definition.
DOI: 10.6084/m9.figshare.14670197
title: Additional file 11 of Re-examination of two diatom reference genomes using long-read sequencing

description: Additional file 11: Supplementary Table 10. Details of full-length, putatively active LTR-RT discovery for the T. pseudonana reference and de novo Flye genomes.
DOI: 10.6084/m9.figshare.14670200
title: Additional file 12 of Re-examination of two diatom reference genomes using long-read sequencing

description: Additional file 12: Supplementary Table 11. Details of full-length, putatively active LTR-RT discovery for the P. tricornutum reference genome.
DOI: 10.6084/m9.figshare.14670203.v1
title: Additional file 13 of Re-examination of two diatom reference genomes using long-read sequencing

description: Additional file 13: Supplementary Table 12. Details of full-length, putatively active LTR-RT discovery for the P. tricornutum de novo Canu genome.
DOI: 10.6084/m9.figshare.14670200.v1
title: Additional file 12 of Re-examination of two diatom reference genomes using long-read sequencing

description: Additional file 12: Supplementary Table 11. Details of full-length, putatively active LTR-RT discovery for the P. tricornutum reference genome.
DOI: 10.6084/m9.figshare.14670203
title: Additional file 13 of Re-examination of two diatom reference genomes using long-read sequencing

description: Additional file 13: Supplementary Table 12. Details of full-length, putatively active LTR-RT discovery for the P. tricornutum de novo Canu genome.
DOI: 10.6084/m9.figshare.14670221.v1
title: Additional file 4 of Re-examination of two diatom reference genomes using long-read sequencing

description: Additional file 4: Supplementary Table 3. Chromosome assignment of “Bottom Drawer” contigs for Phaeodactylum tricornutum and Thalassiosira pseudonana.
DOI: 10.6084/m9.figshare.14670224
title: Additional file 5 of Re-examination of two diatom reference genomes using long-read sequencing

description: Additional file 5: Supplementary Table 4. Long-read contigs help close gaps in the Phaeodactylum tricornutum and Thalassiosira pseudonana reference genomes.