The WTO Trade Effect

Rose (2004) showed that the WTO or its predecessor, the GATT, did not promote trade, based on conventional econometric analysis of gravity-type equations of trade. We argue that conclusions regarding the GATT/WTO trade effect based on gravity-type equations are arbitrary and subject to parametric misspecifications. We propose using nonparametric matching methods to estimate the `treatment effect' of GATT/WTO membership, and permutation-based inferential procedures for assessing statistical significance of the estimated effects. A sensitivity analysis following Rosenbaum (2002) is then used to evaluate the sensitivity of our estimation results to potential selection biases. Contrary to Rose (2004), we find the effect of GATT/WTO membership economically and statistically significant, and far greater than that of the Generalized System of Preferences (GSP).GATT/WTO, GSP, treatment effect, matching, permutation test, signed-rank test, sensitivity analysis

Determining Predictor Importance in Multilevel Models for Longitudinal Data: An Extension of Dominance Analysis

Longitudinal models are used not only to analyze the change of an outcome over time but also to describe what person-level and time-varying factors might influence this trend. Whenever a researcher is interested in the factors or predictors impacting an outcome, a common follow-up question asked is that of the relative importance of such factors. Hence, this study aimed to extend and evaluate Dominance Analysis (DA), a method used to determine the relative importance of predictors in various linear models (Budescu, 1993; Azen & Budescu, 2003; Azen, 2013), for use with longitudinal multilevel models. A simulation study was conducted to investigate the effect of number of measurement occasions (level-1 units), number of subjects (level-2 units), different levels of model complexity (i.e., number of predictors at level-1 and level-2), size of predictor coefficients, predictor collinearity levels, misspecification of the covariance structure, and measures of model fit on DA results and provide recommendations to researchers who wish to determine the relative importance of predictors in longitudinal multilevel models. Results indicated that number of subjects was the most important factor influencing the accuracy of DA in rank-ordering the model predictors, and that more than 50 subjects are needed to obtain adequate power and confidence in the reproducibility of DA results. The McFadden pseudo R² is recommended as the standard measure of fit to use when performing DA in multilevel longitudinal models. Finally, asymptotic standard error and percentile confidence intervals constructed through bootstrapping can be used to determine if one predictor significantly dominates another but might not provide sufficient power unless there are at least 200 subjects in the sample or the magnitude of the general dominance difference measure is greater than 0.01 using McFadden’s R²

The WTO Trade Effect

This paper reexamines the GATT/WTO membership effect on bilateral trade flows, using nonparametric methods including pair-matching, permutation tests, and a Rosenbaum (2002) sensitivity analysis. Together, these methods provide an estimation framework that is robust to misspecification biases, allows general forms of heterogeneous treatment effects, and addresses potential hidden selection biases. This is in contrast to most conventional parametric studies on this issue. Our results suggest large GATT/WTO trade-promoting e®ects, robust to various restricted matching criteria, alternative indicators for GATT/WTO involvement, different matching methodologies, non-random incidence of positive trade flows, and inclusion of multilateral resistance terms.Trade flow,Treatment effect,Matching,Permutation test,Signed-rank test,Sensitivity analysis

Modeling Persistent Trends in Distributions

We present a nonparametric framework to model a short sequence of probability distributions that vary both due to underlying effects of sequential progression and confounding noise. To distinguish between these two types of variation and estimate the sequential-progression effects, our approach leverages an assumption that these effects follow a persistent trend. This work is motivated by the recent rise of single-cell RNA-sequencing experiments over a brief time course, which aim to identify genes relevant to the progression of a particular biological process across diverse cell populations. While classical statistical tools focus on scalar-response regression or order-agnostic differences between distributions, it is desirable in this setting to consider both the full distributions as well as the structure imposed by their ordering. We introduce a new regression model for ordinal covariates where responses are univariate distributions and the underlying relationship reflects consistent changes in the distributions over increasing levels of the covariate. This concept is formalized as a "trend" in distributions, which we define as an evolution that is linear under the Wasserstein metric. Implemented via a fast alternating projections algorithm, our method exhibits numerous strengths in simulations and analyses of single-cell gene expression data.Comment: To appear in: Journal of the American Statistical Associatio

Statistical inference with paired observations and independent observations in two samples

A frequently asked question in quantitative research is how to compare two samples that include some combination of paired observations and unpaired observations. This scenario is referred to as `partially overlapping samples'. Most frequently the desired comparison is that of central location. Depending on the context, the research question could be a comparison of means, distributions, proportions or variances. Approaches that discard either the paired observations or the independent observations are customary. Existing approaches evoke much criticism. Approaches that make use of all available data are becoming more prominent. Traditional and modern approaches for the analyses for each of these research questions are reviewed. Novel solutions for each of the research questions are developed and explored using simulation. Results show that proposed tests which report a direct measurable difference between two groups provide the best solutions

Statistical and Technical Methodologies for Duplicated Multiple-Samples Preference and Attribute Intensity Sensory Ranking Test

Ranking tests are important preference and attribute difference tools for sensory evaluation. Replicated testing is used widely to reduce the number of panelists required in other sensory methods such as discrimination. The information regarding replications sensory ranking is limited. This research evaluated important statistical and technical aspects for the development of the foundation for duplicated sensory ranking tests. Three studies were accomplished: 1) A study of nonparametric analyses on real preference ranked data; 2) a sensitivity study of two samples serving protocols for duplicated visual ranking, and 3) protocols comparison in taste. In study 1, 125 panelists ranked in duplicates each of two sets of three orange juice samples. One set contained very different samples and the other similar samples. Five methods of data analysis were evaluated. With similar samples, analyzing duplicates separately yielded inconsistent conclusions across sample sizes. The Mack-Skillings test was more sensitive than the Friedman test and is more appropriate for analyzing duplicated rank data. Study 2 compared the sensitivity of duplicated yellow color intensity ranking served either in one or two sessions. Panelists (n=75) ranked both similar and different orange juice sets. For each set, rank sum data were obtained from (1) intermediate ranks from jointly re-ranked scores of two separate duplicates for each panelist, (2) joint ranked data of all panelists from the two replications in one serving session, and (3) median rank data of each panelist from two replications. Rank data (3) were analyzed by the Friedman test, while those from 1 and 2 by the M-S test. The similar-samples set had higher variation and inconsistency with one serving session, producing higher P-values than two serving sessions. Both M-S ranking protocols were more sensitive to color differences than Friedman on the medians. For study 3, an identical design was used to evaluate both serving protocols of duplicated sweetness ranking tests. Separate duplicates were more sensitive for color but not in sweetness, especially with confusable samples. This showed that the conducting duplicated ranking in a single session can be beneficial, but it should be tested for the products and attributes of interest before standardizing testing