Nonparametric Specification Research Articles

Uniform nonparametric inference for time series

This paper provides the first result for the uniform inference based on nonparametric series estimators in a general time-series setting. We develop a strong approximation theory for sample averages of mixingales with dimensions growing with the sample size. We use this result to justify the asymptotic validity of a uniform confidence band for series estimators and show that it can also be used to conduct nonparametric specification test for conditional moment restrictions. New results on the validity of heteroskedasticity and autocorrelation consistent (HAC) estimators with increasing dimension are established for making feasible inference. An empirical application on the unemployment volatility puzzle for the search and matching model is provided as an illustration.

SPECS: a non-parametric method to identify tissue-specific molecular features for unbalanced sample groups

BackgroundTo understand biology and differences among various tissues or cell types, one typically searches for molecular features that display characteristic abundance patterns. Several specificity metrics have been introduced to identify tissue-specific molecular features, but these either require an equal number of replicates per tissue or they can’t handle replicates at all.ResultsWe describe a non-parametric specificity score that is compatible with unequal sample group sizes. To demonstrate its usefulness, the specificity score was calculated on all GTEx samples, detecting known and novel tissue-specific genes. A webtool was developed to browse these results for genes or tissues of interest. An example python implementation of SPECS is available at https://github.com/celineeveraert/SPECS. The precalculated SPECS results on the GTEx data are available through a user-friendly browser at specs.cmgg.be.ConclusionsSPECS is a non-parametric method that identifies known and novel specific-expressed genes. In addition, SPECS could be adopted for other features and applications.

An empirical approach based on quantile regression for estimating citation ageing

Abstract An aspect of citation behavior, which has received longstanding attention in research, is how articles’ received citations evolve as time passes since their publication (i.e., citation ageing). Citation ageing has been studied mainly by the formulation and fit of mathematical models of diverse complexity. Commonly, these models restrict the shape of citation ageing functions and explicitly take into account factors known to influence citation ageing. An alternative—and less studied—approach is to estimate citation ageing functions using data-driven strategies. However, research following the latter approach has not been consistent in taking into account those factors known to influence citation ageing. In this article, we propose a model-free approach for estimating citation ageing functions which combines quantile regression with a non-parametric specification able to capture citation inflation. The proposed strategy allows taking into account field of research effects, impact level effects, citation inflation effects and skewness in the distribution of cites effects. To test our methodology, we collected a large dataset consisting of more than five million citations to 59,707 research articles spanning 12 dissimilar fields of research and, with this data in hand, tested the proposed strategy.

On competitive nonlinear pricing

We study a discriminatory limit-order book in which uninformed market makers compete in nonlinear tariffs to serve an informed insider. Our model allows for general nonparametric specifications of preferences and for arbitrary discrete distributions for the insider's private information. We show that adverse selection severely restricts possible equilibrium outcomes: in any pure-strategy equilibrium, tariffs must be linear and at most one type may trade, leading to an extreme form of market breakdown. As a result, such equilibria only exist under exceptional circumstances. The Bertrandlike logic underlying these results markedly differs from Cournot-like analyses of the limit-order book that postulate a continuum of types. We argue that these contrasting outcomes can be reconciled when one considers -equilibria of either the game with a large number of market makers or the game with a large number of insider types. Mixed-strategy equilibria, by contrast, lead to a new class of equilibrium predictions that calls for further analysis.

SemiCompRisks: An R Package for the Analysis of Independent and Cluster-correlated Semi-competing Risks Data.

Semi-competing risks refer to the setting where primary scientific interest lies in estimation and inference with respect to a non-terminal event, the occurrence of which is subject to a terminal event. In this paper, we present the R package SemiCompRisks that provides functions to perform the analysis of independent/clustered semi-competing risks data under the illness-death multi-state model. The package allows the user to choose the specification for model components from a range of options giving users substantial flexibility, including: accelerated failure time or proportional hazards regression models; parametric or non-parametric specifications for baseline survival functions; parametric or non-parametric specifications for random effects distributions when the data are cluster-correlated; and, a Markov or semi-Markov specification for terminal event following non-terminal event. While estimation is mainly performed within the Bayesian paradigm, the package also provides the maximum likelihood estimation for select parametric models. The package also includes functions for univariate survival analysis as complementary analysis tools.

Why fully efficient banks matter? A nonparametric stochastic frontier approach in the presence of fully efficient banks

A common assumption in the banking stochastic performance literature refers to the non-existence of fully efficient banks. This paper relaxes this strong assumption and proposes an alternative semiparametric zero-inefficiency stochastic frontier model. Specifically, we consider a nonparametric specification of the frontier whilst maintaining the parametric specification of the probability of fully efficient bank. We propose an iterative local maximum likelihood procedure that achieves the optimal convergence rates of both nonparametric frontier and the parameters contained in the probability of fully efficient bank. In an empirical application, we apply the proposed model and the estimation procedure to a global banking data set to derive new corrected measures of bank performance and productivity growth across the world. The results show that there is variability across regions, and the probability of fully efficient bank is mostly affected by bank-specific variables that are related to bank’s risk-taking attitude, whereas country-specific variables, such as inflation, also have an effect.

Geography of plant breeding systems, agroclimatic similarity, and agricultural productivity: evidence from Nigeria

AbstractAgroclimatic conditions and agricultural research and development (R&D) including plant breeding activities by the public sector are some of the most significant exogenous factors in the agricultural sector. However, the effects of the interactions between these two sets of factors on agricultural productivity have not been studied widely in developing countries, despite potentially important implications on their plant breeding strategies. Using three‐wave panel data of agricultural households in Nigeria and spatial data on various agroclimatic parameters, we show that agricultural productivity and technical efficiency at the agricultural household level is significantly positively affected by the similarity of agroclimatic conditions between locations where agricultural households are located, and locations where major plant breeding institutes are located. These results hold after controlling for various socioeconomic characteristics of these households, including their physical distances to the breeding institutes. Findings are robust across parametric and nonparametric specifications such as Data Envelopment Method, and after addressing potential endogeneity of agroclimatic similarity and agricultural inputs variables in the production function. Productivity effects due to the locations of plant breeding institutes and resulting agroclimatic similarity can be potentially sizable given Nigeria's past productivity growth speed.

Uniform Nonparametric Series Inference for Dependent Data with an Application to the Search and Matching Model

This paper concerns the uniform inference for nonparametric series estimators in time-series applications. We develop a strong approximation theory of sample averages of serially dependent random vectors with dimensions growing with the sample size. The strong approximation is first proved for heterogeneous martingale difference arrays and then extended to general mixingales via martingale approximation, readily accommodating a majority of applications in applied econometrics. We use these results to justify the asymptotic validity of a uniform confidence band for series estimators and show that it can also be used to conduct nonparametric specification test for conditional moment restrictions. The validity of high-dimensional heteroskedasticity and autocorrelation consistent (HAC) estimators is established for making feasible inference. The proposed method is broadly useful for forecast evaluation, empirical microstructure, dynamic stochastic equilibrium models and inference problems based on inter-section bounds. We demonstrate the empirical relevance of the proposed method by studying the Mortensen–Pissarides search and matching model for equilibrium unemployment, and shed new light on the unemployment volatility puzzle from an econometric perspective.

Assessing the nonlinearity of the calorie-income relationship: An estimation strategy – With new insights on nutritional transition in Vietnam

Abstract Assessing the nonlinearity of the calorie-income relationship is a crucial issue when evaluating policies aimed at fighting against malnutrition. A natural choice would be to adopt a fully nonparametric specification of the relationship in order to let the data reveal its nonlinearity. But, we would be faced with the problem of the curse of dimensionality due to the presence of many control variables in addition to income. Here, we first propose to estimate generalized additive models where only income is supposed to enter nonlinearly in the specification. Second, we use a recent cross-validation procedure in order to choose among various competing specifications including the parametric double-log specification widely used in the literature in addition to GAM specifications. This methodology is implemented for each of the six waves of the Vietnam Household Living Standard Survey from 2004 to 2014. The calorie-income relationship is nonlinear whatever the wave. A strong response of calorie intake to an increase in income for poorest households is highlighted, showing that there is still room for income-based policies to fight against malnutrition. A byproduct of this methodology is the decomposition of the evolution of average calorie intake between the two waves in the part due to population change and that coming from the change in calorie-income relationship, shedding new light on the nutritional transition in Vietnam.

Foreign Policy Change in Latin America: Exploring a Middle Range Concept

In this article we examine patterns of change and continuity in Latin American foreign policies. We do so by asking two interrelated questions: How can we conceptually and empirically account for foreign policy change? And why do states change their foreign policies in Latin America? To answer these questions, we used the results of a new expert survey on foreign policy preferences in the region between 1980 and 2014. The results we obtained using both linear and non-parametric specifications are very clear and consistent: presidential institutions and ideology are what matters the most.

Specification testing in random coefficient models

In this paper, we suggest and analyze a new class of specification tests for random coefficient models. These tests allow to assess the validity of central structural features of the model, in particular linearity in coefficients, generalizations of this notion like a known nonlinear functional relationship, or degeneracy of the distribution of a random coefficient, that is, whether a coefficient is fixed or random, including whether an associated variable can be omitted altogether. Our tests are nonparametric in nature, and use sieve estimators of the characteristic function. We provide formal power analysis against global as well as against local alternatives. Moreover, we perform a Monte Carlo simulation study, and apply the tests to analyze the degree of nonlinearity in a heterogeneous random coefficients demand model. While we find some evidence against the popular QUAIDS specification with random coefficients, it is not strong enough to reject the specification at the conventional significance level. Nonparametric specification testing random coefficients unobserved heterogeneity sieve estimation characteristic function consumer demand C12 C14

Hypercongestion in production correspondences: an empirical exploration

ABSTRACTThis empirical contribution reviews the rather limited existing literature measuring congestion in production. It first compares current ways to measure congestion using nonparametric specifications of technologies. In particular, it focuses on the magnitude and incidence of the congestion detected in empirical studies using traditional radial efficiency measures. Thereafter, it shows the limitations of this radial measurement and how alternative measurement schemes may reveal higher amounts of congestion. Then, the new, more general methodology of measuring S-congestion is presented. In particular, we first present a numerical example to illustrate the way the S-disposable technologies allow to capture more extreme forms of congestion by setting empirically determined upper bounds to the wasting of inputs. Then, an empirical illustration is presented based on an existing sample of data. A final section concludes.

Random taste heterogeneity in discrete choice models: Flexible nonparametric finite mixture distributions

This study proposes a mixed logit model with multivariate nonparametric finite mixture distributions. The support of the distribution is specified as a high-dimensional grid over the coefficient space, with equal or unequal intervals between successive points along the same dimension; the location of each point on the grid and the probability mass at that point are model parameters that need to be estimated. The framework does not require the analyst to specify the shape of the distribution prior to model estimation, but can approximate any multivariate probability distribution function to any arbitrary degree of accuracy. The grid with unequal intervals, in particular, offers greater flexibility than existing multivariate nonparametric specifications, while requiring the estimation of a small number of additional parameters. An expectation maximization algorithm is developed for the estimation of these models. Multiple synthetic datasets and a case study on travel mode choice behavior are used to demonstrate the value of the model framework and estimation algorithm. Compared to extant models that incorporate random taste heterogeneity through continuous mixture distributions, the proposed model provides better out-of-sample predictive ability. Findings reveal significant differences in willingness to pay measures between the proposed model and extant specifications. The case study further demonstrates the ability of the proposed model to endogenously recover patterns of attribute non-attendance and choice set formation.

Extending the logit-mixed logit model for a combination of random and fixed parameters

The logit-mixed logit (LML) model, which allows the analyst to semi-parametrically specify the mixing distribution of preference heterogeneity, is a very recent advancement in logit-type choice models. In addition to generalize many previous semi- and non-parametric specifications, LML is computationally very efficient due to a computationally-convenient likelihood equation that does not require computation of choice probabilities in iterative optimization. However, the original LML formulation assumes all utility parameters to be random. This study extends LML to a combination of fixed and random parameters (LML-FR), and motivates such combination in random parameter choice models in general. We further show that the likelihood of the LML-FR specification loses its special properties, leading to a much higher estimation time. In an empirical application about preferences for alternative fuel vehicles in China, estimation time increased by a factor of 20–40 when introducing fixed parameters. Despite losses in computation efficiency, we show that the flexibility of LML-FR is essential for retrieving eventual multimodality of mixing distributions.

A Generalised Semiparametric Bayesian Fay–Herriot Model for Small Area Estimation Shrinking Both Means and Variances

In survey sampling, interest often lies in unplanned domains (or small areas), whose sample sizes may be too small to allow for accurate design-based inference. To improve the direct estimates by borrowing strength from similar domains, most small area methods rely on mixed effects regression models. This contribution extends the well known Fay–Herriot model (Fay and Herriot, 1979) within a Bayesian approach in two directions. First, the default normality assumption for the random effects is replaced by a nonparametric specification using a Dirichlet process. Second, uncertainty on variances is explicitly introduced, recognizing the fact that they are actually estimated from survey data. The proposed approach shrinks variances as well as means, and accounts for all sources of uncertainty. Adopting a flexible model for the random effects allows to accommodate outliers and vary the borrowing of strength by identifying local neighbourhoods where the exchangeability assumption holds. Through application to real and simulated data, we investigate the performance of the proposed model in predicting the domain means under different distributional assumptions. We also focus on the construction of credible intervals for the area means, a topic that has received less attention in the literature. Frequentist properties such as mean squared prediction error (MSPE), coverage and interval length are investigated. The experiments performed seem to indicate that inferences under the proposed model are characterised by smaller mean squared error than competing approaches; frequentist coverage of the credible intervals is close to nominal.

Labor productivity growth: disentangling technology and capital accumulation

How much of the convergence in labor productivity that we observe in manufacturing is due to convergence in technology versus convergence in capital-labor ratios? To shed light on this question, we introduce a nonparametric counterfactual decomposition of labor productivity growth into growth of the capital-labor ratio (K/L), technological productivity (TEP) and total factor productivity (TFP). Our nonparametric specification enables us to model technology allowing for heterogeneity across all relevant dimensions (i.e. countries, sectors and time). Using data spanning from the 1960s to the 2000s, covering 42 OECD and non OECD countries across 11 manufacturing sectors, we find TEP and TFP to account for roughly 46 and −6% of labor productivity growth respectively, on average. While technological growth at the world level is driven primarily by the US and a handful of other OECD countries, we find strong evidence of convergence in both technology and capital-labor ratios. Interestingly, very few of the usual growth determinants are found to enhance the process of technological catching-up.

Composite Quantile Regression for the Single-Index Model

Quantile regression is in the focus of many estimation techniques and is an important tool in data analysis. When it comes to nonparametric specifications of the conditional quantile (or more generally tail) curve one faces, as in mean regression, a dimensionality problem. We propose a projection based single index model specifi- cation. For very high dimensional regressors X one faces yet another dimensionality problem and needs to balance precision vs. dimension. Such a balance may be achieved by combining semiparametric ideas with variable selection techniques.

Consistent nonparametric specification tests for stochastic volatility models based on the return distribution

This paper develops nonparametric specification tests for stochastic volatility models by comparing the nonparametically estimated return density and distribution functions with their parametric counterparts. Asymptotic null distributions of the tests are derived and the tests are shown to be consistent. Extensive Monte Carlo experiments are performed to study the finite sample properties of the tests. The proposed tests are applied in a number of empirical examples.

A Semi-parametric Transformation Frailty Model for Semi-competing Risks Survival Data.

In the analysis of semi-competing risks data interest lies in estimation and inference with respect to a so-called non-terminal event, the observation of which is subject to a terminal event. Multi-state models are commonly used to analyse such data, with covariate effects on the transition/intensity functions typically specified via the Cox model and dependence between the non-terminal and terminal events specified, in part, by a unit-specific shared frailty term. To ensure identifiability, the frailties are typically assumed to arise from a parametric distribution, specifically a Gamma distribution with mean 1.0 and variance, say, σ2. When the frailty distribution is misspecified, however, the resulting estimator is not guaranteed to be consistent, with the extent of asymptotic bias depending on the discrepancy between the assumed and true frailty distributions. In this paper, we propose a novel class of transformation models for semi-competing risks analysis that permit the non-parametric specification of the frailty distribution. To ensure identifiability, the class restricts to parametric specifications of the transformation and the error distribution; the latter are flexible, however, and cover a broad range of possible specifications. We also derive the semi-parametric efficient score under the complete data setting and propose a non-parametric score imputation method to handle right censoring; consistency and asymptotic normality of the resulting estimators is derived and small-sample operating characteristics evaluated via simulation. Although the proposed semi-parametric transformation model and non-parametric score imputation method are motivated by the analysis of semi-competing risks data, they are broadly applicable to any analysis of multivariate time-to-event outcomes in which a unit-specific shared frailty is used to account for correlation. Finally, the proposed model and estimation procedures are applied to a study of hospital readmission among patients diagnosed with pancreatic cancer.

Hierarchical Models for Semicompeting Risks Data With Application to Quality of End-of-Life Care for Pancreatic Cancer

ABSTRACTReadmission following discharge from an initial hospitalization is a key marker of quality of healthcare in the United States. For the most part, readmission has been studied among patients with “acute” health conditions, such as pneumonia and heart failure, with analyses based on a logistic-Normal generalized linear mixed model. Naïve application of this model to the study of readmission among patients with “advanced” health conditions such as pancreatic cancer, however, is problematic because it ignores death as a competing risk. A more appropriate analysis is to imbed such a study within the semicompeting risks framework. To our knowledge, however, no comprehensive statistical methods have been developed for cluster-correlated semicompeting risks data. To resolve this gap in the literature we propose a novel hierarchical modeling framework for the analysis of cluster-correlated semicompeting risks data that permits parametric or nonparametric specifications for a range of components giving analysts substantial flexibility as they consider their own analyses. Estimation and inference is performed within the Bayesian paradigm since it facilitates the straightforward characterization of (posterior) uncertainty for all model parameters, including hospital-specific random effects. Model comparison and choice is performed via the deviance information criterion and the log-pseudo marginal likelihood statistic, both of which are based on a partially marginalized likelihood. An efficient computational scheme, based on the Metropolis-Hastings-Green algorithm, is developed and had been implemented in the R package SemiCompRisks. A comprehensive simulation study shows that the proposed framework performs very well in a range of data scenarios, and outperforms competitor analysis strategies. The proposed framework is motivated by and illustrated with an ongoing study of the risk of readmission among Medicare beneficiaries diagnosed with pancreatic cancer. Using data on n = 5298 patients at J=112 hospitals in the six New England states between 2000–2009, key scientific questions we consider include the role of patient-level risk factors on the risk of readmission and the extent of variation in risk across hospitals not explained by differences in patient case-mix. Supplementary materials for this article are available online.