In this paper, we test the convergence hypothesis in a cross-section of 119 countries by means of bootstrap multimodality tests and nonparametric density estimation techniques. By looking at the density distribution of GDP across countries in 1970, 1980 and 1989, we find low mobility patterns of intra-distribution dynamics and increasing evidence for bimodality. The findings stand in sharp contrast with the convergence prediction.

Non parametric tests for treatment effects—compliant subjects onlya.

This paper examines whether variations in demographic structure have influenced stock prices. The study employs a nonparametric approach based on the Fourier Flexible Form representation, which relates variations in the entire age distribution to the normalized stock price under a flexible functional form. The main findings of this paper are that there is a significant impact from prime working-age consumers on the stock price, and that this impact is robust for all G5 countries (France, Germany, Japan, the UK and the USA). These findings survive many robust tests, and are consistent with the predictions from the life-cycle models.

This paper proposes a simple extension of nonparametric estimation methods for nonlinear budget-set models derived in Blomquist and Newey (2002) to censored dependent variables. The nonparametric method is applied to estimate female labor supply elasticities using data on married women from the 1985 and 1989 waves of the Panel Study of Income Dynamics, exploiting the substantial variation in budget sets caused by the Tax Reform Act of 1986 as a source of identification. The estimated wage elasticities from this new method are 0.56 overall and 0.27 on the intensive margin. The income elasticity estimates are close to ? 0.67 overall and ? 0.13 on the intensive margin. Compared with the linear labor supply model, the estimated elasticities are usually larger for the nonparametric specifications that account for nonlinear budget sets.

This dataset was used as part of results of an scientific article whom abstract is:

The relationship between international collaboration and scientific impact is studied in the context of South American universities. This study aims to comprehensively analyze the strength of this relationship using nonparametric statistical methods. The records are the 244300 papers published in journals indexed in Scopus (2011-2020) by researchers affiliated to 10 South American public universities and extracted with Scival support. There is a marked trend of collaborative work, since 93% of publications were collaborative at institutional, national or international level, with a higher percentage of international collaboration. A refined analysis of the geographic collaboration of publications in Q1 journals further evidences the frequency of international collaboration. In the top 4 collaborating partner institutions for each university, the presence of the Centre National de la Recherche Scientifique of France
(CNRS) is observed, followed by the National Council for Scientific and Technical Research of Argentina (Conicet). It is proven that there is a statistically significant relationship (p < .01) in each of the 10 universities between collaboration (number of
countries) and normalized impact (FWCI). The results confirmed the hypothesis of this study and the authors provide practical recommendations for science policy makers and researchers, including the promotion of strategic collaboration between different
institutional sectors of society to increase the impact of publications.

This paper shows that average wage gap decompositions between any two groups of workers can be carried out using nonparametric wage structures. It also proposes an algorithm to correct for sample selection in nonparametric models known as tree structures. This paper studies the wage gap between third-generation Mexican American and non-Hispanic white workers in the southwest. It is shown that the decomposition heavily depends on functional assumptions, and that different aproaches to flexibility may render sufficiently good and similar results

An econometric methodology is developed for nonparametric estimation of concave production technologies. The methodology, based on the principle of maximum likelihood, uses entropic distance and convex programming techniques to estimate production functions. Empirical applications are presented to demonstrate the feasibility of the methodology in small and large datasets.

All code used to generate figures in the main text and S1 Text, and supporting data files, as well as execution instructions.

This paper proposes a nonparametric method for evaluating treatment effects in the presence of both treatment endogeneity and attrition/non-response bias, based on two instrumental variables. Using a discrete instrument for the treatment and an instrument with rich (in general continuous) support for non-response/attrition, we identify the average treatment effect on compliers as well as the total population under the assumption of additive separability of observed and unobserved variables affecting the outcome. We suggest non- and semiparametric estimators and apply the latter to assess the treatment effect of gym training, which is instrumented by a randomized cash incentive paid out conditional on visiting the gym, on self-assessed health among students at a Swiss university. The measurement of health is prone to non-response, which is instrumented by a cash lottery for participating in the follow-up survey.

This paper proposes a test statistic for discriminating between two partly non-linear regression models whose parametric components are non-nested. The statistic has the form of a J-test based on a parameter which artificially nests the null and alternative hypotheses. We study in detail the realistic case where all regressors in the non-linear part are discrete and then no smoothing is required on estimating the non-parametric components. We also consider the general case where continuous and discrete regressors are present. The performance of the test in finite samples is discussed in the context of some Monte Carlo experiments. The test is well motivated for specification testing of Engel curves. We provide an application using data from the 1980 Spanish Expenditure Survey.

Descriptive statistics of the used data sets.

We analyze the evolution of earnings mobility in Germany between 2011 and 2018. We use transition matrices and parametric and semi-nonparametric copula models to assess the impact of the introduction of the national minimum wage on January 1, 2015, on individual positional persistence in the wage distribution. We find a drop in mobility at the bottom of the distribution. This is confirmed both by the parametric and the semi-nonparametric methods used. Prediction accuracy of the semi-nonparametric model is higher than that of the fully parametric model.

I examine the effects of insurance status and managed care on hospitalization spells, and develop a new approach for sample selection problems in parametric duration models. MLE of the Flexible Parametric Selection (FPS) model does not require numerical integration or simulation techniques. I discuss application to the exponential, Weibull, log-logistic and gamma duration models. Applying the model to the hospitalization data indicates that the FPS model may be preferred even in cases in which other parametric approaches are available.

Empirical implementation of labour supply theories is usually based on actual hours of work. This requires strong assumptions on the impact of labour demand. To avoid these assumptions, subjective data on desired labour supply can be used. In this paper we investigate whether respondents' answers to survey questions on the desired number of working hours contain additional information on the respondents' preferences. Using panel data for the Netherlands, we analyse whether deviations between desired hours and actual hours of work help to predict future changes in the respondents' actual working hours. We use parametric and recently developed non-parametric tests. The results show that information on desired working hours is helpful in explaining female labour supply. For males the evidence is mixed.

We revisit Fair's (1978) theory of extramarital affairs using robust nonparametric methods developed for the analysis of categorical data. We find evidence suggesting that the number of years married is not a relevant predictor of the propensity to engage in extramarital affairs having controlled for other factors. This finding runs counter to the prevailing wisdom gleaned from misspecified parametric models.

Values are given in medians and 25th and 75th centiles.

BackgroundThe advance of omics technologies has made possible to measure several data modalities on a system of interest. In this work, we illustrate how the Non-Parametric Combination methodology, namely NPC, can be used for simultaneously assessing the association of different molecular quantities with an outcome of interest. We argue that NPC methods have several potential applications in integrating heterogeneous omics technologies, as for example identifying genes whose methylation and transcriptional levels are jointly deregulated, or finding proteins whose abundance shows the same trends of the expression of their encoding genes.ResultsWe implemented the NPC methodology within “omicsNPC”, an R function specifically tailored for the characteristics of omics data. We compare omicsNPC against a range of alternative methods on simulated as well as on real data. Comparisons on simulated data point out that omicsNPC produces unbiased / calibrated p-values and performs equally or significantly better than the other methods included in the study; furthermore, the analysis of real data show that omicsNPC (a) exhibits higher statistical power than other methods, (b) it is easily applicable in a number of different scenarios, and (c) its results have improved biological interpretability.ConclusionsThe omicsNPC function competitively behaves in all comparisons conducted in this study. Taking into account that the method (i) requires minimal assumptions, (ii) it can be used on different studies designs and (iii) it captures the dependences among heterogeneous data modalities, omicsNPC provides a flexible and statistically powerful solution for the integrative analysis of different omics data.

Test errors of the nonparametric models and the parametric benchmark models for mid cap data set.

Statistical results of nonparametric Mann-Whitney U tests.

Non-parametric Wilcoxon Signed Rank Tests for the experiments in this article [1].