cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14519764.v1
title: Local Polynomial Order in Regression Discontinuity Designs<sup>1</sup>

description: Treatment effect estimates in regression discontinuity (RD) designs are often sensitive to the choice of bandwidth and polynomial order, the two important ingredients of widely used local regression methods. While Imbens and Kalyanaraman (2012) and Calonico, Cattaneo and Titiunik (2014) provide guidance on bandwidth, the sensitivity to polynomial order still poses a conundrum to RD practitioners. It is understood in the econometric literature that applying the argument of bias reduction does not help resolve this conundrum, since it would always lead to preferring higher orders. We therefore extend the frameworks of Imbens and Kalyanaraman (2012) and Calonico, Cattaneo and Titiunik (2014) and use the asymptotic mean squared error of the local regression RD estimator as the criterion to guide polynomial order selection. We show in Monte Carlo simulations that the proposed order selection procedure performs well, particularly in large sample sizes typically found in empirical RD applications. This procedure extends easily to fuzzy regression discontinuity and regression kink designs.
DOI: 10.6084/m9.figshare.14394343.v2
title: The PCDID Approach: Difference-in-Differences When Trends Are Potentially Unparallel and Stochastic

description: We develop a class of regression-based estimators, called Principal Components Difference-in-Differences (PCDID) estimators, for treatment effect estimation. Analogous to a control function approach, PCDID uses factor proxies constructed from control units to control for unobserved trends, assuming that the unobservables follow an interactive effects structure. We clarify the conditions under which the estimands in this regression-based approach represent useful causal parameters of interest. We establish consistency and asymptotic normality results of PCDID estimators under minimal assumptions on the specification of time trends. The PCDID approach is illustrated in an empirical exercise that examines the effects of welfare waiver programs on welfare caseloads in the United States.
DOI: 10.6084/m9.figshare.14039385
title: Estimation of Impulse Response Functions When Shocks Are Observed at a Higher Frequency Than Outcome Variables

description: This article proposes mixed-frequency distributed-lag (MFDL) estimators of impulse response functions in a setup where (i) the shock of interest is observed, (ii) the impact variable of interest is observed at a <i>lower</i> frequency (as a temporally aggregated or sequentially sampled variable), (iii) the data generating process (DGP) is given by a VAR model at the frequency of the shock, and (iv) the full set of relevant endogenous variables entering the DGP is unknown or unobserved. Consistency and asymptotic normality of the proposed MFDL estimators is established, and their small-sample performance is documented by a set of Monte Carlo experiments. The usefulness of MFDL estimator is then illustrated in three empirical applications: (i) the daily pass-through of shocks to crude oil prices observed at the daily frequency to U.S. gasoline consumer prices observed at the weekly frequency, (ii) the impact of shocks to global investors’ risk appetite on global capital flows, and (iii) the impact of monetary policy shocks on real activity.
DOI: 10.6084/m9.figshare.14447454
title: Nonparametric Instrumental Regression With Right Censored Duration Outcomes

description: This article analyzes the effect of a discrete treatment Z on a duration T. The treatment is not randomly assigned. The confounding issue is treated using a discrete instrumental variable explaining the treatment and independent of the error term of the model. Our framework is nonparametric and allows for random right censoring. This specification generates a nonlinear inverse problem and the average treatment effect is derived from its solution. We provide local and global identification properties that rely on a nonlinear system of equations. We propose an estimation procedure to solve this system and derive rates of convergence and conditions under which the estimator is asymptotically normal. When censoring makes identification fail, we develop partial identification results. Our estimators exhibit good finite sample properties in simulations. We also apply our methodology to the Illinois Reemployment Bonus Experiment.
DOI: 10.6084/m9.figshare.14447454.v1
title: Nonparametric Instrumental Regression With Right Censored Duration Outcomes

description: This article analyzes the effect of a discrete treatment Z on a duration T. The treatment is not randomly assigned. The confounding issue is treated using a discrete instrumental variable explaining the treatment and independent of the error term of the model. Our framework is nonparametric and allows for random right censoring. This specification generates a nonlinear inverse problem and the average treatment effect is derived from its solution. We provide local and global identification properties that rely on a nonlinear system of equations. We propose an estimation procedure to solve this system and derive rates of convergence and conditions under which the estimator is asymptotically normal. When censoring makes identification fail, we develop partial identification results. Our estimators exhibit good finite sample properties in simulations. We also apply our methodology to the Illinois Reemployment Bonus Experiment.
DOI: 10.6084/m9.figshare.14039385.v3
title: Estimation of Impulse Response Functions When Shocks Are Observed at a Higher Frequency Than Outcome Variables

description: This article proposes mixed-frequency distributed-lag (MFDL) estimators of impulse response functions in a setup where (i) the shock of interest is observed, (ii) the impact variable of interest is observed at a <i>lower</i> frequency (as a temporally aggregated or sequentially sampled variable), (iii) the data generating process (DGP) is given by a VAR model at the frequency of the shock, and (iv) the full set of relevant endogenous variables entering the DGP is unknown or unobserved. Consistency and asymptotic normality of the proposed MFDL estimators is established, and their small-sample performance is documented by a set of Monte Carlo experiments. The usefulness of MFDL estimator is then illustrated in three empirical applications: (i) the daily pass-through of shocks to crude oil prices observed at the daily frequency to U.S. gasoline consumer prices observed at the weekly frequency, (ii) the impact of shocks to global investors’ risk appetite on global capital flows, and (iii) the impact of monetary policy shocks on real activity.
DOI: 10.6084/m9.figshare.14579294
title: SVARs Identification Through Bounds on the Forecast Error Variance

description: This article identifies structural vector autoregressions (SVARs) through bound restrictions on the forecast error variance decomposition (FEVD). First, the article shows FEVD bounds correspond to quadratic inequality restrictions on the columns of the rotation matrix transforming reduced-form residuals into structural shocks. Second, the article establishes theoretical conditions such that bounds on the FEVD lead to a reduction in the width of the impulse response identified set relative to only imposing sign restrictions. Third, this article proposes a robust Bayesian approach to inference. Fourth, the article shows that elicitation of the bounds could be based on DSGE models with alternative parameterizations. Finally, an empirical application illustrates the potential usefulness of FEVD restrictions for obtaining informative inference in set-identified monetary SVARs and remove unreasonable implications of models identified through sign restrictions.
DOI: 10.6084/m9.figshare.14039385.v2
title: Estimation of Impulse Response Functions When Shocks Are Observed at a Higher Frequency Than Outcome Variables

description: This article proposes mixed-frequency distributed-lag (MFDL) estimators of impulse response functions in a setup where (i) the shock of interest is observed, (ii) the impact variable of interest is observed at a <i>lower</i> frequency (as a temporally aggregated or sequentially sampled variable), (iii) the data generating process (DGP) is given by a VAR model at the frequency of the shock, and (iv) the full set of relevant endogenous variables entering the DGP is unknown or unobserved. Consistency and asymptotic normality of the proposed MFDL estimators is established, and their small-sample performance is documented by a set of Monte Carlo experiments. The usefulness of MFDL estimator is then illustrated in three empirical applications: (i) the daily pass-through of shocks to crude oil prices observed at the daily frequency to U.S. gasoline consumer prices observed at the weekly frequency, (ii) the impact of shocks to global investors’ risk appetite on global capital flows, and (iii) the impact of monetary policy shocks on real activity.
DOI: 10.7284/147377
title: Multiplexed data collected during research cruise HRS2109 using a UDel UDEL SMS instrument system onboard the platform RV Hugh R. Sharp

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/147378
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise HRS2109 using a Hawaii UHDAS instrument system onboard the platform RV Hugh R. Sharp

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/147375
title: GNSS (Global Navigation Satellite System) data collected during research cruise HRS2109 using a Furuno GP-37 instrument system onboard the platform RV Hugh R. Sharp

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/147376
title: INS (Inertial Navigation System) data collected during research cruise HRS2109 using a Applanix POS/MV-320 V5 instrument system onboard the platform RV Hugh R. Sharp

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/147380
title: Singlebeam Echosounder data collected during research cruise HRS2109 using a Knudsen 3260 instrument system onboard the platform RV Hugh R. Sharp

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.17632/5ntvjr7j4f.2
title: Supplementary Material

description: Supplementary material for manuscript titled "Changes in plasma and milk choline metabolite concentrations in response to the provision of various rumen-protected choline prototypes in lactating dairy cows"
DOI: 10.17632/cks86467mv.5
title: Supplementary data for the article:  Lightella neohaematopini: A new lineage of highly reduced endosymbionts coevolving with chipmunk lice of the genus Neohaematopinus

description:  Alignments:

1. Phylogenetic matrix - concatenated matrix of 14 ortholog sequences (AA) extracted from representative set of available proteomes for each major lineage of Enterobacteriales (analyzed by PhyML, MrBayes and PhyloBayes) in a fasta format
2. Coevolutionary matrix (5 proteins) - concatenated matrix of 5 ortholog sequences (AA) extracted from 12 assemblies of L. neohaematopini (analyzed by PhyML and MrBayes) in a fasta format
3. Coevolutionary matrix (50 proteins) - concatenated matrix of 50 ortholog sequences (AA) extracted from 11 assemblies of L. neohaematopini (analyzed by PhyML) in a fasta format
4. Neisseriales matrix -  concatenated matrix of 30 ortholog sequences (AA) extracted from representative set of available proteomes for Neisseriales (analyzed by PhyML and MrBayes) in a fasta format
5. Host matrices - individual matrices for 1,107 nuclear loci from Bell et al. 2021 (analyzed by IQ-TREE) in a fasta format

Phylogenetic trees folder:
Phylogenetic trees generated by PhyML, MrBayes and PhyloBayes in a newick format

KO numbers folder:
KO numbers generated by BLAST KOALA server for corresponding genomes

Detailed description of used methods can be found in the Materials and Methods section.
DOI: 10.17632/g93r34fvpx.1
title: COVID-19: TPB Explanatory Model of Protective Behavior Against COVID-19

description: Here you can find the SPSS data (cross-sectional) and all the material of a research in which an explanatory model of protective behaviors against COVID-19 transmission is assessed in the basis of the theory of planned behavior (TPB, Ajzen, 1991, 2020). All the data form part of a manuscript titled "A Planned Behavior Theory-Based Explanatory Model of Protective Behavior Against COVID-19, with an Age Perspective" accepted on 2022, June, in The Journal of Social Psychology. In the model skepticism, social norms about protective behaviors against COVID-19, and behavioral control on the protective measures act as independent variables; moreover, responsibility and the intention to behave in a protective way act as mediators in explaining protective behaviors against COVID-19 transmission. The model is invariant between younger (under 35) and older people (above 34)  but some path differences emerged: the perception that individuals have about the extent to which their reference group adopts protective behaviors directly influences individual protective behavior only for young but not for older people, for whom subjective social norms were only indirect predictors of such behavior. Additionally, the rates in all the variables supposed more risk taking regarding protective behaviors against COVID-19 for the group under 35. 

The file named "TPB_DataRepository" is the SPSS data file, with all the variables used for the study.
In the file named Measures_DataRepository" you can find all the instruments used for the study or the way to give access to them freely.
DOI: 10.17632/k6t8rkh4pd.2
title: High-precision vertical movement  of the Tibetan Plateau

description: The file contains vertical leveling rates of the Tibetan Plateau and its adjacent region (Vertical_rate.txt) build by Wu et al. (2022),  from leveling survey in Tibetan Plateau and its adjacent region. 
Cite the following article:
Wu, Y., Zheng, Z., Nie, J., Chang, L., Su, G., Yin, H., et al. (2022). High-precision vertical movement and three-dimensional deformation pattern of the Tibetan Plateau. Journal of Geophysical Research: Solid Earth, 127, e2021JB023202. https://doi.org/10.1029/2021JB023202
DOI: 10.17632/zf3jtjrwdx.1
title: Data for: Attributes and person types admired by Greek emerging adults: Relative prevalence, differences by gender and socio-economic status

description: Design: These data come from a mixed-method study, in which participants were asked to answer to questions in their own words. These answers were then coded. Quantitative and qualitative data are presented to answer the research questions and hypotheses. 

Participants: The sample comprised 198 Greek emerging adults, aged 18 to 25 (M = 19.46, SD = 1.38). From these, 99 (50.0%) were female, 94 (47.48%) were male and the remaining 5 (2.52%) did not report their gender. 

Measures: Participants were asked to nominate a person that they admire, write who that person is and the gender of that person, and describe a maximum of five qualities or attributes that they admired. In the present dataset, the interested reader can find the answers to these questions (in Greek) and the codes assigned to each answer (in English). One set of codes concerns the admired person's type (column J), and another set of codes concerns the admired attributes (columns M to Z).
DOI: 10.17632/vttfs7cc6d.1
title: Remote sensing product for net primary production in Tampa Bay, Florida

description: Datasets related to the study "Estimating estuarine primary production using satellite data and machine learning" by Xu, Min et al. 2022, in the International Journal of Applied Earth Observation and Geoinformation.
DOI: 10.6084/m9.figshare.14484571
title: Binary Conditional Forecasts

description: While conditional forecasting has become prevalent both in the academic literature and in practice (e.g., bank stress testing, scenario forecasting), its applications typically focus on continuous variables. In this article, we merge elements from the literature on the construction and implementation of conditional forecasts with the literature on forecasting binary variables. We use the Qual-VAR, whose joint VAR-probit structure allows us to form conditional forecasts of the latent variable which can then be used to form probabilistic forecasts of the binary variable. We apply the model to forecasting recessions in real-time and investigate the role of monetary and oil shocks on the likelihood of two U.S. recessions.
DOI: 10.6084/m9.figshare.14046591.v2
title: Quasi-Experimental Evaluation of Alternative Sample Selection Corrections

description: Researchers routinely use datasets where outcomes of interest are unobserved for some cases, potentially creating a sample selection problem. Statisticians and econometricians have proposed many selection correction methods to address this challenge. We use a natural experiment to evaluate different sample selection correction methods’ performance. From 2007, the state of Michigan required that all students take a college entrance exam, increasing the exam-taking rate from 64% to 99% and largely eliminating selection into exam-taking. We apply different selection correction methods, using different sets of covariates, to the selected exam score data from before 2007. We compare the estimated coefficients from the selection-corrected models to those from OLS regressions using the complete exam score data from after 2007 as a benchmark. We find that less restrictive semiparametric correction methods typically perform better than parametric correction methods but not better than simple OLS regressions that do not correct for selection. Performance is generally worse for models that use only a few discrete covariates than for models that use more covariates with less coarse distributions.
DOI: 10.6084/m9.figshare.14135200
title: Semiparametric Quantile Models for Ascending Auctions With Asymmetric Bidders

description: The article proposes a parsimonious and flexible semiparametric quantile regression specification for asymmetric bidders within the independent private value framework. Asymmetry is parameterized using powers of a parent private value distribution, which is generated by a quantile regression specification. As noted in Cantillon, this covers and extends models used for efficient collusion, joint bidding and mergers among homogeneous bidders. The specification can be estimated for ascending auctions using the winning bids and the winner’s identity. The estimation is in two stage. The asymmetry parameters are estimated from the winner’s identity using a simple maximum likelihood procedure. The parent quantile regression specification can be estimated using simple modifications of Gimenes. Specification testing procedures are also considered. A timber application reveals that weaker bidders have 30% less chances to win the auction than stronger ones. It is also found that increasing participation in an asymmetric ascending auction may not be as beneficial as using an optimal reserve price as would have been expected from a result of Bulow and Klemperer valid under symmetry.
DOI: 10.6084/m9.figshare.14135200.v2
title: Semiparametric Quantile Models for Ascending Auctions With Asymmetric Bidders

description: The article proposes a parsimonious and flexible semiparametric quantile regression specification for asymmetric bidders within the independent private value framework. Asymmetry is parameterized using powers of a parent private value distribution, which is generated by a quantile regression specification. As noted in Cantillon, this covers and extends models used for efficient collusion, joint bidding and mergers among homogeneous bidders. The specification can be estimated for ascending auctions using the winning bids and the winner’s identity. The estimation is in two stage. The asymmetry parameters are estimated from the winner’s identity using a simple maximum likelihood procedure. The parent quantile regression specification can be estimated using simple modifications of Gimenes. Specification testing procedures are also considered. A timber application reveals that weaker bidders have 30% less chances to win the auction than stronger ones. It is also found that increasing participation in an asymmetric ascending auction may not be as beneficial as using an optimal reserve price as would have been expected from a result of Bulow and Klemperer valid under symmetry.
DOI: 10.6084/m9.figshare.14046591
title: Quasi-Experimental Evaluation of Alternative Sample Selection Corrections

description: Researchers routinely use datasets where outcomes of interest are unobserved for some cases, potentially creating a sample selection problem. Statisticians and econometricians have proposed many selection correction methods to address this challenge. We use a natural experiment to evaluate different sample selection correction methods’ performance. From 2007, the state of Michigan required that all students take a college entrance exam, increasing the exam-taking rate from 64% to 99% and largely eliminating selection into exam-taking. We apply different selection correction methods, using different sets of covariates, to the selected exam score data from before 2007. We compare the estimated coefficients from the selection-corrected models to those from OLS regressions using the complete exam score data from after 2007 as a benchmark. We find that less restrictive semiparametric correction methods typically perform better than parametric correction methods but not better than simple OLS regressions that do not correct for selection. Performance is generally worse for models that use only a few discrete covariates than for models that use more covariates with less coarse distributions.
DOI: 10.6084/m9.figshare.14272744
title: Robust Inference for Diffusion-Index Forecasts With Cross-Sectionally Dependent Data

description: In this article, we propose the time-series average of spatial HAC estimators for the variance of the estimated common factors under the approximate factor structure. Based on this, we provide the confidence interval for the conditional mean of the diffusion-index forecasting model with cross-sectional heteroscedasticity and dependence of the idiosyncratic errors. We establish the asymptotics under very mild conditions, and no prior information about the dependence structure is needed to implement our procedure. We employ a bootstrap to select the bandwidth parameter. Simulation studies show that our procedure performs well in finite samples. We apply the proposed confidence interval to the problem of forecasting the unemployment rate using data by Ludvigson and Ng.
DOI: 10.6084/m9.figshare.14272744.v2
title: Robust Inference for Diffusion-Index Forecasts With Cross-Sectionally Dependent Data

description: In this article, we propose the time-series average of spatial HAC estimators for the variance of the estimated common factors under the approximate factor structure. Based on this, we provide the confidence interval for the conditional mean of the diffusion-index forecasting model with cross-sectional heteroscedasticity and dependence of the idiosyncratic errors. We establish the asymptotics under very mild conditions, and no prior information about the dependence structure is needed to implement our procedure. We employ a bootstrap to select the bandwidth parameter. Simulation studies show that our procedure performs well in finite samples. We apply the proposed confidence interval to the problem of forecasting the unemployment rate using data by Ludvigson and Ng.