automatic bandwidth choice and confidence intervals in nonparametric regression in the present paper we combine the issues of bandwidth choice and construction of confidence intervals in nonparametric regression main emphasis is put on fully data driven methods we modify the n consistent bandwidth selector of hahrdle hall and marron such that it is appropriate for heteroscedastic data and we show how one can optimally choose the bandwidth g of the pilot estimator m g then we consider classical confidence intervals based on kernel estimators with data driven bandwidths and compare their coverage accuracy we propose a method to put undersmoothing with a data driven bandwidth into practice and show that this procedure outperforms explicit bias correction

a new skewed link model for dichotomous quantal response data abstract the logit probit and student t links are widely used in modeling dichotomous quantal response data most of the commonly used link functions are symmetric except the complementary log log link however in some applications the overall fit can be significantly improved by the use of an asymmetric link in this article we propose a new skewed link model for analyzing binary response data with covariates introducing a skewed distribution for the underlying latent variable we develop a class of asymmetric link models for binary response data using a bayesian approach we first characterize the propriety of the posterior distributions using standard improper priors we further propose informative priors using historical data from a similar previous study we examine the proposed method through a large scale simulation study and use data from a prostate cancer study to demonstrate the use of historical data in bayesian model fitting and comparison of skewed link models

on model diagnostics using varying coefficient models summary the paper motivates the use of varying coefficient models for diagnostics in regression models with continuous and factorial covariates the varying coefficient model which is restricted only by assumptions concerning the smoothness of the effects is considered as the alternative to a parametric generalised linear model estimation is based on local likelihood smoothing and asymptotic properties of the estimators are derived for the investigation of the discrepancy between the parametric model and the smooth alternative a simple graphical procedure is proposed moreover tests are derived which allow for overall and componentwise investigation the componentwise tests may be used to examine interaction effects of continuous and factorial regressors

exact and approximate inferences for nonlinear mixed effects models with missing covariates nonlinear mixed effects nlme models are popular in many longitudinal studies including human immunodeficiency virus hiv viral dynamics pharmacokinetic analyses and studies of growth and decay in practice covariates in these studies often contain missing data and so standard complete data methods are not directly applicable in this article we propose monte carlo parameter expanded px em algorithms for exact and approximate likelihood inferences for nlme models with missing covariates when the missing data mechanism is ignorable we allow arbitrary missing data patterns and allow the covariates to be categorical continuous and mixed the px em algorithm maintains the simplicity and stability of the standard em algorithm and may converge much faster than em the approximate method is computationally more efficient and may be preferable to the exact method when the exact method exhibits convergence problems such as slow convergence or nonconvergence it becomes an exact method for linear mixed

a consistent nonparametric test of parametric regression functional form in fixed effects panel data models abstract we propose a consistent test for a linear functional form against a nonparametric alternative in a fixed effects panel data model we show that the test has a limiting standard normal distribution under the null hypothesis and show that the test is a consistent test we also establish the asymptotic validity of a bootstrap procedure which is used to better approximate the finite sample null distribution of the test statistic simulation results show that the proposed test performs well for panel data with a large number of cross sectional units and a finite number of observations across time

nonparametric bootstrap confidence intervals for discrete regression functions abstract this is a study of a binary response bootstrap for constructing confidence intervals in a nonparametric setup some first order asymptotic results are proved for this method which hold true when generalizing to the multinomial case this method is illustrated by means of an application to real medical data

nonparametric comparison of several regression functions exact and asymptotic theory a new test is proposed for the comparison of two regression curves f and g we prove an asymptotic normal law under fixed alternatives which can be applied for power calculations for constructing confidence regions and for testing precise hypotheses of a weighted l 2 distance between f and g in particular the problem of nonequal sample sizes is treated which is related to a peculiar formula of the area between two step functions these results are extended in various directions such as the comparison of k regression functions or the optimal allocation of the sample sizes when the total sample size is fixed the proposed pivot statistic is not based on a nonparametric estimator of the regression curves and therefore does not require the specification of any smoothing parameter

censored quantile regression with partially functional effects quantile regression offers a flexible approach to analyzing survival data allowing each covariate effect to vary with quantiles in practice constancy is often found to be adequate for some covariates in this paper we study censored quantile regression tailored to the partially functional effect setting with a mixture of varying and constant effects such a model can offer a simpler view regarding covariate survival association and moreover can enable improvement in estimation efficiency we propose profile estimating equations and present an iterative algorithm that can be readily and stably implemented asymptotic properties of the resultant estimators are established a simple resampling based inference procedure is developed and justified extensive simulation studies demonstrate efficiency gains of the proposed method over a naive two stage procedure the proposed method is illustrated via an application to a recent renal dialysis study

testing the predictor effect on a functional response abstractthis article examines the problem of nonparametric testing for the no effect of a random covariate or predictor on a functional response this means testing whether the conditional expectation of the response given the covariate is almost surely zero or not without imposing any model relating response and covariate the covariate could be univariate multivariate or functional our test statistic is a quadratic form involving univariate nearest neighbor smoothing and the asymptotic critical values are given by the standard normal law when the covariate is multidimensional or functional a preliminary dimension reduction device is used which allows the effect of the covariate to be summarized into a univariate random quantity the test is able to detect not only linear but nonparametric alternatives the responses could have conditional variance of unknown form and the law of the covariate does not need to be known an empirical study with simulated and real data shows that the test performs

breaking the curse of dimensionality in nonparametric testing abstract for tests based on nonparametric methods power crucially depends on the dimension of the conditioning variables and specifically decreases with this dimension this is known as the curse of dimensionality we propose a new general approach to nonparametric testing in high dimensional settings and we show how to implement it when testing for a parametric regression the resulting test behaves against directional local alternatives almost as if the dimension of the regressors was one it is also almost optimal against classes of one dimensional alternatives for a suitable choice of the smoothing parameter the test performs well in small samples compared to several other tests

nonparametric specification tests for conditional duration models this paper deals with the testing of autoregressive conditional duration acd models by gauging the distance between the parametric density and hazard rate functions implied by the duration process and their non parametric estimates we derive the asymptotic justification using the functional delta method for fixed and gamma kernels and then investigate the finite sample properties through monte carlo simulations although our tests display some size distortion bootstrapping suffices to correct the size without compromising their excellent power we show the practical usefulness of such testing procedures for the estimation of intraday volatility patterns

yield curve estimation by kernel smoothing methods we introduce a new method for the estimation of discount functions yield curves and forward curves from government issued coupon bonds our approach is nonparametric and does not assume a particular functional form for the discount function although we do show how to impose various restrictions in the estimation our method is based on kernel smoothing and is defined as the minimum of some localized population moment condition the solution to the sample problem is not explicit and our estimation procedure is iterative rather like the backfitting method of estimating additive nonparametric models we establish the asymptotic normality of our methods using the asymptotic representation of our estimator as an infinite series with declining coefficients the rate of convergence is standard for one dimensional nonparametric regression we investigate the finite sample performance of our method in comparison with other well established methods in a small simulation experiment

heteroscedastic one way anova and lack of fit tests recent articles have considered the asymptotic behavior of the one way analysis of variance anova f statistic when the number of levels or groups is large in these articles the results were obtained under the assumption of homoscedasticity and for the case when the sample or group sizes ni remain fixed as the number of groups a tends to infinity in this article we study both weighted and unweighted test statistics in the heteroscedastic case the unweighted statistic is new and can be used even with small group sizes we demonstrate that an asymptotic approximation to the distribution of the weighted statistic is possible only if the group sizes tend to infinity suitably fast in relation to a our investigation of local alternatives reveals a similarity between lack of fit tests for constant regression in the present case of replicated observations and the case of no replications which uses smoothing techniques the asymptotic theory uses a novel application of the projection principle to obtain

goodness of fit tests for the cure rate in a mixture cure model summary we consider models for time to event data that allow that an event e g a relapse of a disease never occurs for a certain percentage p of the population called the cure rate we suppose that these data are subject to random right censoring and we model the data using a mixture cure model in which the survival function of the uncured subjects is left unspecified the aim is to test whether the cure rate p as a function of the covariates satisfies a certain parametric model to do so we propose a test statistic that is inspired by a goodness of fit test for a regression function due to hardle mammen 1993 we show that the statistic is asymptotically normally distributed under the null hypothesis that the model is correctly specified and under local alternatives a bootstrap procedure is proposed to implement the test the good performance of the approach is confirmed with simulations for illustration we apply the test to data on the times between first and second births

partial identification and inference in censored quantile regression abstract in this paper we study partial identification and inference in a linear quantile regression where the dependent variable is subject to possibly unknown dependent censoring characterized by an archimedean copula an outer set of the identified set for the regression coefficient is characterized via inequality constraints for one parameter ordered families of archimedean copulas we construct a simple confidence set by inverting an asymptotically pivotal statistic a bootstrap confidence set is also constructed sensitivity of the identified set to possible misspecification of the true copula and the finite sample performance of the boostrap confidence set are investigated numerically

testing for parallelism among trends in multiple time series this paper considers the inference of trends in multiple nonstationary time series to test whether trends are parallel to each other we use a parallelism index based on the l2 distances between nonparametric trend estimators and their average a central limit theorem is obtained for the test statistic and the test s consistency is established we propose a simulation based approximation to the distribution of the test statistic which significantly improves upon the normal approximation the test is also applied to devise a clustering algorithm finally the finite sample properties of the test are assessed through simulations and the test methodology is illustrated by a cell phone download data collected in the united states

nonparametric tests of linearity for time series summary we introduce tests of linearity for time series based on nonparametric estimates of the conditional mean and the conditional variance the tests are compared to a number of parametric tests and to nonparametric tests based on the bispectrum asymptotic expressions give bad approximations and the null distribution under linearity is constructed using resampling of the best linear approximation the new tests perform well on the examples tested

consistent bootstrap tests of parametric regression functions abstract this paper introduces specification tests of parametric mean regression models the null hypothesis of interest is that the parametric regression function is correctly specified the proposed tests are generalizations of the kolmogorov smirnov and cramer von mises tests to the regression framework they are consistent against all alternatives to the null hypothesis powerful against 1 n local alternatives not dependent on any smoothing parameters and simple to compute a wild bootstrap procedure is suggested to obtain critical values for the tests and is justified asymptotically a small scale monte carlo experiment shows that our tests especially cramer von mises test have outstanding small sample performance compared to some of the existing tests

penalized quasi likelihood estimation in partial linear models consider a partial linear model where the expectation of a random variable y depends on covariates x z through f theta 0 x m 0 z with theta 0 an unknown parameter and m 0 an unknown function we apply the theory of empirical processes to derive the asymptotic properties of the penalized quasi likelihood estimator

generalized quasi likelihood ratio tests for semiparametric analysis of covariance models in longitudinal data abstractwe model generalized longitudinal data from multiple treatment groups by a class of semiparametric analysis of covariance models which take into account the parametric effects of time dependent covariates and the nonparametric time effects in these models the treatment effects are represented by nonparametric functions of time and we propose a generalized quasi likelihood ratio test procedure to test if these functions are identical our estimation procedure is based on profile estimating equations combined with local linear smoothers we find that the much celebrated wilks phenomenon which is well established for independent data still holds for longitudinal data if a working independence correlation structure is assumed in the test statistic however this property does not hold in general especially when the working variance function is misspecified our empirical study also shows that incorporating correlation into the test statistic does not necessarily improve the power of the test the

testing procedures for detection of linear dependencies in efficiency models the validity of many efficiency measurement methods rely upon the assumption that variables such as input quantities and output mixes are independent of or uncorrelated with technical efficiency however few studies have attempted to test these assumptions in a recent paper wilson 2003 investigates a number of independence tests and finds that they have poor size properties and low power in moderate sample sizes in this study we discuss the implications of these assumptions in three situations i bootstrapping non parametric efficiency models ii estimating stochastic frontier models and iii obtaining aggregate measures of industry efficiency we propose a semi parametric hausmann type asymptotic test for linear independence uncorrelation and use a monte carlo experiment to show that it has good size and power properties in finite samples we also describe how the test can be generalized in order to detect higher order dependencies such as heteroscedasticity so that the test can be used to test for full independence when the efficiency distribution has a finite number of moments finally an empirical illustration is provided using data on us electric power generation

single polymer brownian motor a simulation study numerical simulation is used to study a single polymer chain in a flashing ratchet potential to determine how the mechanism of this brownian motor system is affected by the presence of internal degrees of freedom the polymer is modeled by a freely jointed chain with n monomers in which the monomers interact via a repulsive lennard jones potential and neighboring monomers on the chain are connected by finite extensible nonlinear elastic bonds each monomer is acted upon by a 1d asymmetric piecewise linear potential of spatial period l comparable to the radius of gyration of the polymer this potential is also characterized by a localization time t on and by a free diffusion time t off we characterize the average motor velocity as a function of l t off and n to determine optimal parameter ranges and we evaluate motor performance in terms of finite dispersion peclet number rectification efficiency stall force and transportation of a load against a viscous drag we find that the polymer motor performs qualitatively better than a single particle in a flashing ratchet with increasing n the polymer loses velocity much more slowly than expected in the absence of internal degrees of freedom and the motor stall force increases linearly with n to understand these cooperative aspects of motor operation we analyze relevant rouse modes the experimental feasibility is analyzed and the parameters of the model are scaled to those of lambda dna finally in the context of experimental realization we present initial modeling results for a 2d flashing ratchet constructed using an electrode array and find good agreement with the results of 1d simulations although the polymer in the 2d potential sometimes briefly detaches from the electrode surface

monitoring profiles based on nonparametric regression methods the use statistical process control spc in monitoring and diagnosis of process and product quality profiles remains an important problem in various manufacturing industries the spc problem with a nonlinear profile is particularly challenging this article proposes a novel scheme to monitor changes in both the regression relationship and the variation of the profile online it integrates the multivariate exponentially weighted moving average procedure with the generalized likelihood ratio test based on nonparametric regression the proposed scheme not only provides an effective spc solution to handle nonlinear profiles which are common in industrial practice but it also resolves the latent problem in popular parametric monitoring methods of being unable to detect certain types of changes due to a misspecified out of control model our simulation results demonstrate the effectiveness and efficiency of the proposed monitoring scheme in addition a systematic diagnostic approach is provided to locate t

asymptotic distribution theory for nonparametric entropy measures of serial dependence entropy is a classical statistical concept with appealing properties establishing asymptotic distribution theory for smoothed nonparametric entropy measures of dependence has so far proved challenging in this paper we develop an asymptotic theory for a class of kernel based smoothed nonparametric entropy measures of serial dependence in a time series context we use this theory to derive the limiting distribution of granger and lin s 1994 normalized entropy measure of serial dependence which was previously not available in the literature we also apply our theory to construct a new entropy based test for serial dependence providing an alternative to robinson s 1991 approach to obtain accurate inferences we propose and justify a consistent smoothed bootstrap procedure the naive bootstrap is not consistent for our test our test is useful in for example testing the random walk hypothesis evaluating density forecasts and identifying important lags of a time series it is asymptotically locally more powerful than robinson s 1991 test as is confirmed in our simulation an application to the daily s p 500 stock price index illustrates our approach

a consistent characteristic function based test for conditional independence abstract y is conditionally independent of z given x if pr f y x z f y x 1 for all y on its support where f denotes the conditional density of y given x z or x this paper proposes a nonparametric test of conditional independence based on the notion that two conditional distributions are equal if and only if the corresponding conditional characteristic functions are equal we extend the test of su and white 2005 a hellinger metric nonparametric test for conditional independence discussion paper department of economics ucsd in two directions 1 our test is less sensitive to the choice of bandwidth sequences 2 our test has power against deviations on the full support of the density of x y z we establish asymptotic normality for our test statistic under weak data dependence conditions simulation results suggest that the test is well behaved in finite samples applications to stock market data indicate that our test can reveal some interesting nonlinear dependence that a traditional linear granger causality test fails to detect

testing against a linear regression model using ideas from shape restricted estimation summary a formal likelihood ratio hypothesis test for the validity of a parametric regression function is proposed using a large dimensional non parametric double cone alternative for example the test against a constant function uses the alternative of increasing or decreasing regression functions and the test against a linear function uses the convex or concave alternative the test proposed is exact and unbiased and the critical value is easily computed the power of the test increases to 1 as the sample size increases under very mild assumptions even when the alternative is misspecified i e the power of the test converges to 1 for any true regression function that deviates in a non degenerate way from the parametric null hypothesis we also formulate tests for the linear versus partial linear model and consider the special case of the additive model simulations show that our procedure behaves well consistently when compared with other methods although the alternative fit is non parametric no tuning parameters are involved supplementary materials with proofs and technical details are available on line

bootstrap of residual processes in regression to smooth or not to smooth summaryin this paper we consider regression models with centred errors independent of the covariates given independent and identically distributed data and given an estimator of the regression function which can be parametric or nonparametric in nature we estimate the distribution of the error term by the empirical distribution of estimated residuals to approximate the distribution of this estimator koul lahiri 1994 and neumeyer 2009 proposed bootstrap procedures based on smoothing the residuals before drawing bootstrap samples so far it has been an open question as to whether a classical nonsmooth residual bootstrap is asymptotically valid in this context here we solve this open problem and show that the nonsmooth residual bootstrap is consistent we illustrate the theoretical result by means of simulations which demonstrate the accuracy of this bootstrap procedure for various models testing procedures and sample sizes

covariate matched one sided tests for the difference between functional means abstract when testing hypotheses about the effects of different treatments variation among covariates can become confounded with that between treatments unless the treatments are applied using paired covariates in the context of unpaired covariates we propose implicit covariate matching methods for testing the hypothesis that one treatment effect is greater than another the methods are founded on the assumption that the mean treatment effect conditional on the covariate is a smooth function of the covariate they are implemented using new interpolation techniques for nonparametric curve estimation bootstrap arguments are used to construct critical points we show that even when the covariate distributions are identical for both treatments covariate matching of the type that we propose produces tests of greater power than methods that do not attempt matching our techniques have application to two sided hypothesis testing

unified empirical likelihood ratio tests for functional concurrent linear models and the phase transition from sparse to dense functional data summary we consider the problem of testing functional constraints in a class of functional concurrent linear models where both the predictors and the response are functional data measured at discrete time points we propose test procedures based on the empirical likelihood with bias corrected estimating equations to conduct both pointwise and simultaneous inferences the asymptotic distributions of the test statistics are derived under the null and local alternative hypotheses where sparse and dense functional data are considered in a unified framework we find a phase transition in the asymptotic null distributions and the orders of detectable alternatives from sparse to dense functional data specifically the tests proposed can detect alternatives of n order when the number of repeated measurements per curve is of an order larger than n 0 with n being the number of curves the transition points 0 for pointwise and simultaneous tests are different and both are smaller than the transition point in the estimation problem simulation studies and real data analyses are conducted to demonstrate the methods proposed

parametric approximations of nonparametric frontiers the maximal achievable level of output for a given level of inputs defines the production frontier that can serve as benchmark to evaluate individual firm efficiencies nonparametric envelopment estimators free disposal hull data envelopment analysis have been mostly used because they rely on very few assumptions whereas parametric forms for the frontier allow for richer economic interpretation most of the parametric approaches rely on standard regression fitting the shape of the center of the cloud of points in this paper we investigated a new approach which captures the shape of the cloud points near its boundary it offers parametric approximations of nonparametric frontiers we provide the statistical theory asymptotic some simulated examples show the advantages of our method compared with the usual regression type estimators c 2004 elsevier b v all rights reserved

testing a parametric model against a nonparametric alternative with identification through instrumental variables this paper is concerned with inference about a function g that is identified by a conditional moment restriction involving instrumental variables the paper presents a test of the hypothesis that g belongs to a finite dimensional parametric family against a nonparametric alternative the test does not require nonparametric estimation of g and is not subject to the ill posed inverse problem of nonparametric instrumental variables estimation under mild conditions the test is consistent against any alternative model in large samples its power is arbitrarily close to 1 uniformly over a class of alternatives whose distance from the null hypothesis is o n 1 2 where n is the sample size in monte carlo simulations the finite sample power of the new test exceeds that of existing tests

comparing nonparametric versus parametric regression fits in general there will be visible differences between a parametric and a nonparametric curve estimate it is therefore quite natural to compare these in order to decide whether the parametric model could be justified an asymptotic quantification is the distribution of the integrated squared difference between these curves we show that the standard way of bootstrapping this statistic fails we use and analyse a different form of bootstrapping for this task we call this method the wild bootstrap and apply it to fitting engel curves in expenditure data analysis

goodness of fit tests for parametric regression models several new tests are proposed for examining the adequacy of a family of parametric models against large nonparametric alternatives these tests formally check if the bias vector of residuals from parametric fits is negligible by using the adaptive neyman test and other methods the testing procedures formalize the traditional model diagnostic tools based on residual plots we examine the rates of contiguous alternatives that can be detected consistently by the adaptive neyman test applications of the procedures to the partially linear models are thoroughly discussed our simulation studies show that the new testing procedures are indeed powerful and omnibus the power of the proposed tests is comparable to the f test statistic even in the situations where the f test is known to be suitable and can be far more powerful than the f test statistic in other situations an application to testing linear models versus additive models is also discussed

intersection bounds estimation and inference we develop a practical and novel method for inference on intersection bounds namely bounds defined by either the infimum or supremum of a parametric or nonparametric function or equivalently the value of a linear programming problem with a potentially infinite constraint set we show that many bounds characterizations in econometrics for instance bounds on parameters under conditional moment inequalities can be formulated as intersection bounds our approach is especially convenient for models comprised of a continuum of inequalities that are separable in parameters and also applies to models with inequalities that are nonseparable in parameters since analog estimators for intersection bounds can be severely biased in finite samples routinely underestimating the size of the identified set we also offer a median bias corrected estimator of such bounds as a by product of our inferential procedures we develop theory for large sample inference based on the strong approximation of a sequence of series or kernel based empirical processes by a sequence of penultimate gaussian processes these penultimate processes are generally not weakly convergent and thus are non donsker our theoretical results establish that we can nonetheless perform asymptotically valid inference based on these processes our construction also provides new adaptive inequality moment selection methods we provide conditions for the use of nonparametric kernel and series estimators including a novel result that establishes strong approximation for any general series estimator admitting linearization which may be of independent interest

constructing a bootstrap confidence interval for the unknown concentration in radioimmunoassay the statistical problem associated with radioimmunoassay is known as calibration or inverse regression in the current study we propose a bootstrap procedure aimed at constructing an inverse confidence interval for the univariate calibration problem the calibration curve is estimated either parametrically or by non parametric regression the methods are illustrated by an example

testing for smooth structural changes in time series models via nonparametric regression checking parameter stability of econometric models is a long standing problem almost all existing structural change tests in econometrics are designed to detect abrupt breaks little attention has been paid to smooth structural changes which may be more realistic in economics we propose a consistent test for smooth structural changes as well as abrupt structural breaks with known or unknown change points the idea is to estimate smooth time varying parameters by local smoothing and compare the fitted values of the restricted constant parameter model and the unrestricted time varying parameter model the test is asymptotically pivotal and does not require prior information about the alternative a simulation study highlights the merits of the proposed test relative to a variety of popular tests for structural changes in an application we strongly reject the stability of univariate and multivariate stock return prediction models in the postwar and post oil shocks periods

bootstrap and wild bootstrap for high dimensional linear models in this paper two bootstrap procedures are considered for the estimation of the distribution of linear contrasts and of f test statistics in high dimensional linear models an asymptotic approach will be chosen where the dimension p of the model may increase for sample size n rightarrow infty the range of validity will be compared for the normal approximation and for the bootstrap procedures furthermore it will be argued that the rates of convergence are different for the bootstrap procedures in this asymptotic framework this is in contrast to the usual asymptotic approach where p is fixed

uniform nonparametric inference for time series abstract 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

testing for trends in high dimensional time series the article considers statistical inference for trends of high dimensional time series based on a modified l2 distance between parametric and nonparametric trend estimators we propose a de diagon

tests of additional conditional moment restrictions abstract the primary focus of this article is the provision of tests for the validity of a set of conditional moment constraints additional to those defining the maintained hypothesis that are relevant for independent cross sectional data contexts the point of departure and principal contribution of the paper is the explicit and full incorporation of the conditional moment information defining the maintained hypothesis in the design of the test statistics thus the approach mirrors that of the classical parametric likelihood setting by defining restricted tests in contradistinction to unrestricted tests that partially or completely fail to incorporate the maintained information in their formulation the framework is quite general allowing the parameters defining the additional and maintained conditional moment restrictions to differ and permitting the conditioning variates to differ likewise gmm and generalised empirical likelihood test statistics are suggested the asymptotic properties of the statistics are described under both null hypothesis and a suitable sequence of local alternatives an extensive set of simulation experiments explores the practical efficacy of the various test statistics in terms of empirical size and size adjusted power confirming the superiority of restricted over unrestricted tests a number of restricted tests possess both sufficiently satisfactory empirical size and power characteristics to allow their recommendation for econometric practice

rectification efficiency of a brownian motor the energy balance of a brownian motor is discussed based on a langevin equation without the overdamped approximation energetics of the system suggests that the frictional dissipation energy associated with the unidirectional movement should be counted as a part of the useful energy for the rectification process of a brownian motor this leads to a new definition of the efficiency which is applicable contrary to the conventional one even if the external load is absent for the so called flashing ratchet model we numerically solve the langevin equation for various situations and discuss both the temperature and the friction strength dependence of the rectification efficiency and the role of the duty ratio

moment restriction based econometric methods an overview the 2011 special issue of the journal of econometrics discusses papers based on moment restriction based econometric methods in the first paper peter robinson of london school of economics considers asymptotic theory for nonparametric regression with spatial data specifically nonparametric regression with spatial or spatio temporal data the author discusses application of the established conditions to spatial autoregressive models and models defined on a regular lattice liqun wang and cheng hsiao consider method of moments estimation and identifiability of semiparametric nonlinear errors in variables models their paper deals with a nonlinear errors in variables model where the distributions of the unobserved predictor variables and of the measurement errors are nonparametric shih hsun hsu and chung ming kuan examine estimation of conditional moment restrictions without assuming parameter identifiability in the implied unconditional moments

nonparametric inference for counterfactual means bias correction confidence sets and weak iv abstract this paper supplements manski 1990 and manski and pepper 2000 and contributes to the literature by introducing the concept of weak iv for the partially identified mean counterfactual outcomes when an instrumental variable iv or a monotone instrumental variable miv is available iv or miv assumption respectively developing asymptotically uniformly valid confidence sets for the counterfactual mean outcomes and average treatment effects under the assumptions correcting biases of estimates of bounds on the counterfactual mean outcomes under the assumptions we apply the confidence sets to further examining the effect of family intactness on a child s high school graduation originally studied in manski et al 1992

testing conditional independence via rosenblatt transforms this paper proposes new tests of conditional independence of two random variables given a single index involving an unknown finite dimensional parameter the tests employ rosenblatt transforms and are shown to be distribution free while retaining computational convenience some results from monte carlo simulations are presented and discussed

a test for model specification of diffusion processes we propose a test for model specification of a parametric diffusion process based on a kernel estimation of the transitional density of the process the empirical likelihood is used to formulate a statistic for each kernel smoothing bandwidth which is effectively a studentized l2 distance between the kernel transitional density estimator and the parametric transitional density implied by the parametric process to reduce the sensitivity of the test on smoothing bandwidth choice the final test statistic is constructed by combining the empirical likelihood statistics over a set of smoothing bandwidths to better capture the finite sample distribution of the test statistic and data dependence the critical value of the test is obtained by a parametric bootstrap procedure properties of the test are evaluated asymptotically and numerically by simulation and by a real data example

wavelet based testing for serial correlation of unknown form in panel models wavelet analysis is a new mathematical method developed as a unified field of science over the last decade or so as a spatially adaptive analytic tool wavelets are useful for capturing serial correlation where the spectrum has peaks or kinks as can arise from persistent dependence seasonality and other kinds of periodicity this paper proposes a new class of generally applicable wavelet based tests for serial correlation of unknown form in the estimated residuals of a panel regression model where error components can be one way or two way individual and time effects can be fixed or random and regressors may contain lagged dependent variables or deterministic stochastic trending variables our tests are applicable to unbalanced heterogenous panel data they have a convenient null limit n 0 1 distribution no formulation of an alternative model is required and our tests are consistent against serial correlation of unknown form even in the presence of substantial in homogeneity in serial correlation across individuals this is in contrast to existing serial correlation tests for panel models which ignore inhomogeneity in serial correlation across individuals by assuming a common alternative and thus have no power against the alternatives where the average of serial correlations among individuals is close to zero we propose and justify a data driven method to choose the smoothing parameter the finest scale in wavelet spectral estimation making the tests completely operational in practice the data driven finest scale automatically converges to zero under the null hypothesis of no serial correlation and diverges to infinity as the sample size increases under the alternative ensuring the consistency of our tests simulation shows that our tests perform well in small and finite samples relative to some existing tests

testing parametric assumptions on band or time limited signals under noise this paper considers the problem of testing parametric assumptions on signals f from which only noisy observations y sub k f spl tau k spl epsi sub k are available and where the signal is assumed to be either band limited or time limited to this end the signal is reconstructed by an estimator based on the whittaker shannon ws sampling theorem with oversampling as test statistic the minimal l sub 2 distance between the estimated signal and the parametric model is used to construct appropriate tests the asymptotic distribution of the test statistic is derived both under the hypothesis of the validity of the parametric model and under fixed local alternatives as a byproduct the asymptotic distribution of the integrated square error of the estimator is computed which is of interest by itself e g for the analysis of a cross validated bandwidth selector

minimum distance regression model checking with berkson measurement errors lack of fit testing of a regression model with berkson measurement error has not been discussed in the literature to date to fill this void we propose a class of tests based on minimized integrated square distances between a nonparametric regression function estimator and the parametric model being fitted we prove asymptotic normality of these test statistics under the null hypothesis and that of the corresponding minimum distance estimators under minimal conditions on the model being fitted we also prove consistency of the proposed tests against a class of fixed alternatives and obtain their asymptotic power against a class of local alternatives orthogonal to the null hypothesis these latter results are new even when there is no measurement error a simulation that is included shows very desirable finite sample behavior of the proposed inference procedures

consistent specification tests for semiparametric nonparametric models based on series estimation methods abstract this paper considers the problem of consistent model specification tests using series estimation methods the null models we consider in this paper all contain some nonparametric components a leading case we consider is to test for an additive partially linear model the null distribution of the test statistic is derived using a central limit theorem for hilbert valued random arrays the test statistic is shown to be able to detect local alternatives that approach the null models at the order of op n 1 2 we show that the wild bootstrap method can be used to approximate the null distribution of the test statistic a small monte carlo simulation is reported to examine the finite sample performance of the proposed test we also show that the proposed test can be easily modified to obtain series based consistent tests for other semiparametric nonparametric models

a consistent test of functional form via nonparametric estimation techniques abstract this paper presents a consistent test of functional form of nonlinear regression models the test combines the methodology of the conditional moment test and nonparametric estimation techniques using degenerate and nondegenerate u statistic theories the test statistic is shown to be asymptotically distributed standard normal under the null hypothesis that the parametric model is correct while diverging to infinity at a rate arbitrarily close to n the sample size if the parametric model is misspecified therefore the test is consistent against all deviations from the parametric model the test is robust to heteroskedasticity a version of the test can be constructed which will have asymptotic power equal to 1 against any local alternatives approaching the null at rates slower than the parametric rate 1 n a simulation study reveals that the test has good finite sample properties

translocation time of periodically forced polymer chains in this work we study the presence of both a minimum and clear oscillations in the frequency dependence of the translocation time of a polymer described as a unidimensional rouse chain driven by a spatially localized oscillating linear potential the observed oscillations of the mean translocation time arise from the synchronization between the very mean translocation time and the period of the external force we have checked the robustness of the frequency value for the minimum translocation time by changing the damping parameter finding a very simple relationship between this frequency and the correspondent translocation time the translocation time as a function of the polymer length has been also evaluated finding a precise l 2 scaling furthermore the role played by the thermal fluctuations described as a gaussian uncorrelated noise has been also investigated and the analogies with the resonant activation phenomenon are commented

model checking for parametric single index models a dimension reduction model adaptive approach summary local smoothing testing based on multivariate non parametric regression estimation is one of the main model checking methodologies in the literature however the relevant tests suffer from the typical curse of dimensionality resulting in slow rates of convergence to their limits under the null hypothesis and less deviation from the null hypothesis under alternative hypotheses this problem prevents tests from maintaining the level of significance well and makes tests less sensitive to alternative hypotheses in the paper a model adaptation concept in lack of fit testing is introduced and a dimension reduction model adaptive test procedure is proposed for parametric single index models the test behaves like a local smoothing test as if the model were univariate it is consistent against any global alternative hypothesis and can detect local alternative hypotheses distinct from the null hypothesis at a fast rate that existing local smoothing tests can achieve only when the model is univariate simulations are conducted to examine the performance of our methodology an analysis of real data is shown for illustration the method can be readily extended to global smoothing methodology and other testing problems

structural tests in additive regression we consider the component analysis problem for a regression model with an additive structure the problem is to test whether some of the additive components are of polynomial structure e g linear without specifying the structure of the remaining components a particular case is the problem of selecting the significant covariates the method that we present is based on the wavelet transform using the haar basis which allows for applications under mild conditions on the design and smoothness of the regression function the results demonstrate that each component of the model can be tested with the rate corresponding to the case if all of the remaining components were known the proposed procedure is also computationally straightforward simulation results and a real data example about female labor supply demonstrate the test s good performance

testing and imposing slutsky symmetry in nonparametric demand systems maximization of utility implies that consumer demand systems have a slutsky matrix which is everywhere symmetric however previous non and semi parametric approaches to the estimation of consumer demand systems do not give estimators that are restricted to satisfy this condition nor do they offer powerful tests of this restriction we use nonparametric modeling to test and impose slutsky symmetry in a system of expenditure share equations over prices and expenditure in this context slutsky symmetry is a set of nonlinear cross equation restrictions on levels and derivatives of consumer demand equations the key insight is that due to the differing convergence rates of levels and derivatives and due to the fact that the symmetry restrictions are linear in derivatives both the test and the symmetry restricted estimator behave asymptotically as if these restrictions were locally linear we establish large and finite sample properties of our methods and show that our test has advantages over the only other comparable test all methods we propose are implemented with canadian micro data we find that our nonparametric analysis yields statistically significantly and qualitatively different results from traditional parametric estimators and tests

specification testing in nonparametric instrumental variable estimation abstract in nonparametric instrumental variable estimation the function being estimated is the solution to an integral equation a solution may not exist if for example the instrument is not valid this paper discusses the problem of testing the null hypothesis that a solution exists against the alternative that there is no solution we give necessary and sufficient conditions for existence of a solution and show that uniformly consistent testing of an unrestricted null hypothesis is not possible uniformly consistent testing is possible however if the null hypothesis is restricted by assuming that any solution to the integral equation is smooth many functions of interest in applied econometrics including demand functions and engel curves are expected to be smooth the paper presents a statistic for testing the null hypothesis that a smooth solution exists the test is consistent uniformly over a large class of probability distributions of the observable random variables for which the integral equation has no smooth solution the finite sample performance of the test is illustrated through monte carlo experiments

an equality test across nonparametric regressions abstract we propose a procedure for testing the equality of several nonparametric multivariate regressions we allow the regressors designs and the number of observations to differ across subsamples the division into subsamples is defined through a variable c which can be either fixed or random for a random c our procedure is a general test of significance for qualitative variables in a nonparametric regression for a fixed c our procedure provides a nonparametric analysis of covariance which is valid for cross section or panel data in both cases the test is a one sided normal test and is consistent against all alternatives

bandwidth selection in nonparametric kernel testing we propose a sound approach to bandwidth selection in nonparametric kernel testing the main idea is to find an edgeworth expansion of the asymptotic distribution of the test concerned due to the involvement of a kernel bandwidth in the leading term of the edgeworth expansion we are able to establish closed form expressions to explicitly represent the leading terms of both the size and power functions and then determine how the bandwidth should be chosen according to certain requirements for both the size and power functions for example when a significance level is given we can choose the bandwidth such that the power function is maximized while the size function is controlled by the significance level both asymptotic theory and methodology are established in addition we develop an easy implementation procedure for the practical realization of the established methodology and illustrate this on two simulated examples and a real data example

testing conditional moment restrictions let x z be a pair of observable random vectors we construct a new smoothed empirical likelihood based test for the hypothesis e g z x 0 w p 1 where g is a vector of known functions and an unknown finite dimensional parameter we show that the test statistic is asymptotically normal under the null hypothesis and derive its asymptotic distribution under a sequence of local alternatives furthermore the test is shown to possess an optimality property in large samples simulation evidence suggests that it also behaves well in small samples