set identification via quantile restrictions in short panels this paper studies the identifying power of conditional quantile restrictions in short panels with flxed efiects in contrast to classical flxed efiects models with conditional mean restrictions conditional quantile restrictions are not preserved by taking difierences in the regression equation over time this paper shows however that a conditional quantile restriction in conjunction with a weak conditional independence restriction provides bounds on quantiles of difierences in time varying unobservables across periods these bounds carry observable implications for model parameters which generally result in set identiflcation the analysis of these bounds includes conditions for point identiflcation of the parameter vector as well as weaker conditions that result in identiflcation of individual parameter components

stochastic resonance in a linear system an exact solution multistable systems can exhibit stochastic resonance which is characterized by the amplification of small periodic signals by additive noise here we consider a nonmultistable linear system with a multiplicative noise forced by an external periodic signal the noise is the sum of a colored noise of mean value zero and a noise with a definite sign we show that the system exhibits stochastic resonance through the numerical study of an exact analytical expression for the mean value obtained by functional integral techniques this is proof of the effect for a very general kind of noise which can even have a definite sign

parameter induced stochastic resonance in an over damped linear system abstract stochastic resonance sr in an over damped linear system subjected to an excitation of bias signal modulated noise with multiplicative and additive noises is investigated we obtain the exact expressions of the first two moments and the signal to noise ratio snr of the output by using linear response theory the snr depends non monotonically on the intensity and the correlation time of multiplicative noise the correlation time of additive noise the intensity of the cross noise between multiplicative and additive noise as well as the external field frequency the conventional sr the sr in a broad sense and the bona fide sr are found in the system the influences of the asymmetries of multiplicative and additive noise the correlation rate of the cross noise the intensity of additive noise the amplitude of signal and the bias on the snr are analyzed moreover we pointed out that sr can be realized by tuning the system parameter with fixed noise i e parameter induced stochastic resonance psr exists

instrumental variable quantile regression a robust inference approach abstract in this paper we develop robust inference procedures for an instrumental variables model defined by y d u where d u is strictly increasing in u and u is a uniform variable that may depend on d but is independent of a set of instrumental variables z the proposed inferential procedures are computationally convenient in typical applications and can be carried out using software available for ordinary quantile regression our inferential procedure arises naturally from an estimation algorithm and has the important feature of being robust to weak and partial identification and remains valid even in cases where identification fails completely the use of the proposed procedures is illustrated through two empirical examples

semiparametric analysis of random effects linear models from binary panel data andersen 1970 considered the problem of inference on random effects linear models from binary response panel data he showed that inference is possible if the disturbances for each panel member are known to be white noise with the logistic distribution and if the observed explanatory variables vary over time a conditional maximum likelihood estimator consistently estimates the model parameters up to scale the present paper shows that inference remains possible if the disturbances for each panel member are known only to be time stationary with unbounded support and if the explanatory variables vary enough over time a conditional version of the maximum score estimator manski 1975 1985 consistently estimates the model parameters up to scale

harmonic oscillator with multiplicative noise nonmonotonic dependence on the strength and the rate of dichotomous noise the output signal of a undamped linear oscillator with a random frequency subject to a periodic force shows nonmonotonic dependence on the strength and the rate of color noise stochastic resonance the effect is absent for white noise

estimating and testing a quantile regression model with interactive effects this paper proposes a quantile regression estimator for a panel data model with interactive effects potentially correlated with the independent variables we provide conditions under which the slope parameter estimator is asymptotically gaussian monte carlo studies are carried out to investigate the finite sample performance of the proposed method in comparison with other candidate meth ods we discuss an approach to testing the model specification against a competing fixed effects specification the paper presents an empirical application of the method to study the effect of class size and class composition on educational attainment the findings show that i a change in the gender composition of a class impacts differently low and high performing students ii while smaller classes are beneficial for low performers larger classes are beneficial for high performers iii reductions in class size do not seem to impact mean and median student performance iv the fixed effects specification is rejected in favor of the interactive effects specification

identification and estimation of triangular simultaneous equations models without additivity this paper uses control variables to identify and estimate models with nonseparable multidimensional disturbances triangular simultaneous equations models are considered with instruments and disturbances that are independent and a reduced form that is strictly monotonic in a scalar disturbance here it is shown that the conditional cumulative distribution function of the endogenous variable given the instruments is a control variable also for any control variable identification results are given for quantile average and policy effects bounds are given when a common support assumption is not satisfied estimators of identified objects and bounds are provided and a demand analysis empirical example is given

effect of asymmetry on stochastic resonance and stochastic resonance induced by multiplicative noise and by mean field coupling in the paper we investigate the effect of asymmetry of the potential on stochastic resonance sr for a model with an asymmetric bistable potential and driven by additive noise the signal to noise ratio snr for a model with a monostable potential and driven by additive and multiplicative noises and the snr for a mean field coupled model with infinite globally coupling oscillators driven by additive noises it is shown that for the first model the asymmetry of the potential can weaken the phenomenon of sr for the second and third models a sr induced by multiplicative noise and a different one caused by mean field coupling are found

nonparametric estimation and inference on conditional quantile processes this paper presents estimation methods and asymptotic theory for the analysis of a nonparametrically specified conditional quantile process two estimators based on local linear regressions are proposed the first estimator applies simple inequality constraints while the second uses rearrangement to maintain quantile monotonicity the bandwidth parameter is allowed to vary across quantiles to adapt to data sparsity for inference the paper first establishes a uniform bahadur representation and then shows that the two estimators converge weakly to the same limiting gaussian process as an empirical illustration the paper considers a dataset from project star and delivers two new findings

inference on counterfactual distributions counterfactual distributions are important ingredients for policy analysis and decomposition analysis in empirical economics in this article we develop modeling and inference tools for counterfactual distributions based on regression methods the counterfactual scenarios that we consider consist of ceteris paribus changes in either the distribution of covariates related to the outcome of interest or the conditional distribution of the outcome given covariates for either of these scenarios we derive joint functional central limit theorems and bootstrap validity results for regression based estimators of the status quo and counterfactual outcome distributions these results allow us to construct simultaneous confidence sets for function valued effects of the counterfactual changes including the effects on the entire distribution and quantile functions of the outcome as well as on related functionals these confidence sets can be used to test functional hypotheses such as no effect positive effect or stochastic dominance our theory applies to general counterfactual changes and covers the main regression methods including classical quantile duration and distribution regressions we illustrate the results with an empirical application to wage decompositions using data for the united states as a part of developing the main results we introduce distribution regression as a comprehensive and flexible tool for modeling and estimating the entire conditional distribution we show that distribution regression encompasses the cox duration regression and represents a useful alternative to quantile regression we establish functional central limit theorems and bootstrap validity results for the empirical distribution regression process and various related functionals publication abstract

universal character of stochastic resonance and a constructive role of white noise it is shown that in spite of claims put forward in the literature the stochastic resonance sr appears even in linear systems both overdamped and inertial driven by gaussian white noise and even after averaging the asymptotics over the initial phase of the input signal this supports recent suggestions that sr is a universal effect present in every stochastic process modulated by external signals it is also shown that the noise may sustain the output signal which otherwise would vanish exponentially in the course of time

the mechanism of stochastic resonance it is shown that a dynamical system subject to both periodic forcing and random perturbation may show a resonance peak in the power spectrum which is absent when either the forcing or the perturbation is absent

double stochastic resonance in an insect ecosystem with time delays abstract considering additive and multiplicative periodic signals we have investigated the stochastic resonance sr phenomenon in an insect ecosystem with two different types of time delays at which the insect dynamics is assumed to be disturbed by both additive and multiplicative noises the signal to noise ratio snr is calculated when two different weak periodic signals are added to the insect system it is shown from the snr that the additive and multiplicative periodic signals can cause the sr and double sr in the case of time delays in the stochastic and deterministic force respectively a detailed study of the snr depending on various noise characteristics is performed and the main results about sr are compared for two different cases of time delays

on mean convergence of hermite fejer and hermite interpolation for erdos weights abstract we investigate convergence of hermite fejer and hermite interpolation polynomials in l p 0 for erdos weights

probability distributions for multicomponent systems with multiplicative noise abstract linear systems with many degrees of freedom containing multiplicative and additive noise are considered the steady state probability distribution for equations of this kind is examined with multiplicative white noise and certain symmetry conditions it is shown that the probability distribution of a single component has power law tails with the exponent independent of the strength of additive noise but dependent on the strength of the multiplicative noise the classification of these systems into two regimes appears to be possible in the same manner as with just one degree of freedom a physical system that of a turbulent fluid undergoing a chemical reaction is predicted to show a transition from exponential to power law tails as the reaction rate is increased a variety of systems are studied numerically a replication algorithm is used to obtain the lyapunov exponents for high moments which would be inaccessible by more conventional approaches

dynamic numerical models of stock market price from microscopic determinism to macroscopic randomness abstract a variant of threshold dynamics is introduced to model the behaviors of a large assembly of dealers in a stock market although the microscopic evolution dynamics is deterministic the collective behaviors such as market prices show seemingly stochastic fluctuations the statistical properties of market price change can be well approximated by a simple discrete langevin type equation with random amplification the macroscopic stochastic equation is solved both numerically and analytically showing that the market price change generally follow power law distributions in the steady state the reason for the appearance of rapid decay in the distribution tails are discussed

quantile regression with measurement error regression quantiles can be substantially biased when the covariates are measured with error in this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error the method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels an iterative em type estimation algorithm to obtain the solutions to such joint estimation equations is provided the finite sample performance of the proposed method is investigated in a simulation study and compared to the standard regression calibration approach finally we apply our methodology to part of the national collaborative perinatal project growth data a longitudinal study with an unusual measurement error structure

stochastic resonance in a linear fractional langevin equation the fractional langevin equation is derived from the generalized langevin equation driven by the additive fractional gaussian noise we investigate the stochastic resonance sr phenomenon in the underdamped linear fractional langevin equation under the external periodic force and multiplicative symmetric dichotomous noise applying the shapiro loginov formula and the laplace transform technique we obtain the exact expressions of the amplitude and signal to noise ratio snr of the system by studying the impacts of the driving frequency and the noise parameters we find the non monotonic behaviors of the output amplitude and snr the results indicate that the bona fide sr conventional sr and the wide sense of sr phenomena occur in the proposed linear fractional system

quantile regression under misspecification with an application to the u s wage structure quantile regression qr fits a linear model for conditional quantiles just as ordinary least squares ols fits a linear model for conditional means an attractive feature of ols is that it gives the minimum mean squared error linear approximation to the conditional expectation function even when the linear model is misspecified empirical research using quantile regression with discrete covariates suggests that qr may have a similar property but the exact nature of the linear approximation has remained elusive in this paper we show that qr minimizes a weighted mean squared error loss function for specification error the weighting function is an average density of the dependent variable near the true conditional quantile the weighted least squares interpretation of qr is used to derive an omitted variables bias formula and a partial quantile regression concept similar to the relationship between partial regression and ols we also present asymptotic theory for the qr process under misspecification of the conditional quantile function the approximation properties of qr are illustrated using wage data from the u s census these results point to major changes in inequality from 1990 to 2000

asymptotics for panel quantile regression models with individual effects this paper studies panel quantile regression models with individual fixed effects we formally establish sufficient conditions for consistency and asymptotic normality of the quantile regression estimator when the number of individuals n and the number of time periods t jointly go to infinity the estimator is shown to be consistent under similar conditions to those found in the nonlinear panel data literature nevertheless due to the non smoothness of the objective function we had to impose a more restrictive condition on t to prove asymptotic normality than that usually found in the literature the finite sample performance of the estimator is evaluated by monte carlo simulations

nonparametric identification in panels using quantiles abstract this paper considers identification and estimation of ceteris paribus effects of continuous regressors in nonseparable panel models with time homogeneity the effects of interest are derivatives of the average and quantile structural functions of the model we find that these derivatives are identified with two time periods for stayers i e for individuals with the same regressor values in two time periods we show that the identification results carry over to models that allow location and scale time effects we propose nonparametric series methods and a weighted bootstrap scheme to estimate and make inference on the identified effects the bootstrap proposed allows inference for function valued parameters such as quantile effects uniformly over a region of quantile indices and or regressor values an empirical application to engel curve estimation with panel data illustrates the results

stochastic resonance in linear systems subject to multiplicative and additive noise exact expressions have been found for the first two moments and the correlation function for an overdamped linear system subject to an external periodic field as well as to multiplicative and additive noise stochastic resonance is absent for gaussian white noise however when the multiplicative noise has the form of an asymmetric dichotomous noise the signal to noise ratio snr becomes a nonmonotonic function of the correlation time and the asymmetry of noise moreover the snr turns out to be a nonmonotonic function of the frequency of the external field as well as strongly depending on the strength of the cross correlation between multiplicative and additive noise

another look at the instrumental variable estimation of error components models this article develops a framework for efficient iv estimators of random effects models with information in levels which can accommodate predetermined variables our formulation clarifies the relationship between the existing estimators and the role of transformations in panel data models we characterize the valid transformations for relevant models and show that optimal estimators are invariant to the transformation used to remove individual effects we present an alternative transformation for models with predetermined instruments which preserves the orthogonality among the errors finally we consider models with predetermined variables that have constant correlation with the effects and illustrate their importance with simulations

a product quadrature algorithm by hermite interpolation this paper is concerned with numerical integration of 1 1f x k x dx by product integration rules based on hermite interpolation special attention is given to the kernel k x ei x with a view to providing high precision rules for oscillatory integrals convergence results and error estimates are obtained in the case where the points of integration are zeros of pn w x or of 1 x2 pn 2 w x where pn w x n 0 1 2 are the orthonormal polynomials associated with a generalized jacobi weight w further examples are given that test the performance of the algorithm for oscillatory weight functions

stochastic resonance in a mono stable system with multiplicative and additive noise the stochastic resonance in a biased mono stable system subject to multiplicative and additive noise is investigated based on the adiabatic approximation theory the analytic expression of the signal to noise ratio snr is obtained it is shown that the snr is a non monotonic function of the intensities of the multiplicative and additive noise as well as the parameters of the mono stable system

a quantile correlated random coefficients panel data model abstract we propose a generalization of the linear quantile regression model to accommodate possibilities afforded by panel data specifically we extend the correlated random coefficients representation of linear quantile regression e g koenker 2005 section 2 6 we show that panel data allows the econometrician to i introduce dependence between the regressors and the random coefficients and ii weaken the assumption of comonotonicity across them i e to enrich the structure of allowable dependence between different coefficients we adopt a fixed effects approach leaving any dependence between the regressors and the random coefficients unmodeled we motivate different notions of quantile partial effects in our model and study their identification for the case of discretely valued covariates we present analog estimators and characterize their large sample properties when the number of time periods t exceeds the number of random coefficients p identification is regular and our estimates are n consistent when t p our identification results make special use of the subpopulation of stayers units whose regressor values change little over time in a way which builds on the approach of graham and powell 2012 in this just identified case we study asymptotic sequences which allow the frequency of stayers in the population to shrink with the sample size one purpose of these discrete bandwidth asymptotics is to approximate settings where covariates are continuously valued and as such there is only an infinitesimal fraction of exact stayers while keeping the convenience of an analysis based on discrete covariates when the mass of stayers shrinks with n identification is irregular and our estimates converge at a slower than n rate but continue to have limiting normal distributions we apply our methods to study the effects of collective bargaining coverage on earnings using the national longitudinal survey of youth 1979 nlsy79 consistent with prior work e g chamberlain 1982 vella and verbeek 1998 we find that using panel data to control for unobserved worker heterogeneity results in sharply lower estimates of union wage premia we estimate a median union wage premium of about 9 percent but with in a more novel finding substantial heterogeneity across workers the 0 1 quantile of union effects is insignificantly different from zero whereas the 0 9 quantile effect is of over 30 percent our empirical analysis further suggests that on net unions have an equalizing effect on the distribution of wages

efficient semiparametric estimation of quantile treatment effects this paper develops estimators for quantile treatment effects under the identifying restriction that selection to treatment is based on observable characteristics identification is achieved without requiring computation of the conditional quantiles of the potential outcomes instead the identification results for the marginal quantiles lead to an estimation procedure for the quantile treatment effect parameters that has two steps nonparametric estimation of the propensity score and computation of the difference between the solutions of two separate minimization problems root n consistency asymptotic normality and achievement of the semiparametric efficiency bound are shown for that estimator a consistent estimation procedure for the variance is also presented finally the method developed here is applied to evaluation of a job training program and to a monte carlo exercise results from the empirical application indicate that the method works relatively well even for a data set with limited overlap between treated and controls in the support of covariates the monte carlo study shows that for a relatively small sample size the method produces estimates with good precision and low bias especially for middle quantiles

on the unbiased asymptotic normality of quantile regression with fixed effects abstract nonlinear panel data models with fixed individual effects provide an important set of tools for describing microeconometric data in a large class of such models including probit proportional hazard and quantile regression to name just a few it is impossible to difference out the individual effects and inference is usually justified in a large n large t asymptotic framework however there is a considerable gap in the type of assumptions that are currently imposed in models with smooth score functions such as probit and proportional hazard and quantile regression in the present paper we show that this gap can be bridged and establish unbiased asymptotic normality for fixed effects quantile regression panels under conditions on n t that are very close to what is typically assumed in standard nonlinear panels our results considerably improve upon existing theory and show that quantile regression is applicable to the same type of panel data in terms of n t as other commonly used nonlinear panel data models thorough numerical experiments confirm our theoretical findings

stochastic multiresonance and correlation time controlled stability for a harmonic oscillator with fluctuating frequency the long time behavior of the first two moments and the correlation function for the output signal of a harmonic oscillator with fluctuating frequency subjected to an external periodic force and an additive thermal noise is considered analytically the colored fluctuations of the oscillator frequency are modeled as a three level markovian noise using the shapiro loginov formula the exact expressions of several stochastic resonance sr characteristics such as the spectral amplification the variance of the output signal the signal to noise ratio and the sr gain have been calculated the nonmonotonic dependence of the sr characteristics versus the noise parameters as well as versus the input signal frequency and also the conditions for the appearance of energetic instability are analyzed in particular the multiresonancelike behavior of the variance and the signal to noise ratio as functions of the noise correlation time are observed and the connection between the occurrence of energetic instability and the phenomenon of stochastic multiresonance is established some unexpected effects such as the hypersensitive response of the spectral amplification to small variations of the noise amplitude encountered in the case of a large flatness of the colored noise are also discussed

multivariate regression models for panel data abstract the paper examines the relationship between heterogeneity bias and strict exogeneity in a distributed lag regression of y on x the relationship is very strong when x is continuous weaker when x is discrete and non existent as the order of the distributed lag becomes infinite the individual specific random variables introduce nonlinearity and heteroskedasticity so the paper provides an appropriate framework for the estimation of multivariate linear predictors restrictions are imposed using a minimum distance estimator it is generally more efficient than the conventional estimators such as quasi maximum likelihood there are computationally simple generalizations of two and three stage least squares that achieve this efficiency gain some of these ideas are illustrated using the sample of young men in the national longitudinal survey the paper reports regressions on the leads and lags of variables measuring union coverage smsa and region the results indicate that the leads and lags could have been generated just by a random intercept this gives some support for analysis of covariance type estimates these estimates indicate a substantial heterogeneity bias in the union smsa and region coefficients

stochastic resonance in a trapping overdamped monostable system the response of a trapping overdamped monostable system to a harmonic perturbation is analyzed in the context of stochastic resonance phenomenon we consider the dynamics of a brownian particle moving in a piecewise linear potential with a white gaussian noise source based on linear response theory and laplace transform technique analytical expressions of signal to noise ratio snr and signal power amplification spa are obtained we find that the snr is a nonmonotonic function of the noise intensity while the spa is monotonic theoretical results are compared with numerical simulations

irregular identification support conditions and inverse weight estimation in weighted moment condition models we show a subtle link between identification and estimability that limits the practical usefulness of estimators based on these models in particular if it is necessary for point identification that the weights take arbitrarily large values then the parameter of interest though point identified cannot be estimated at the regular parametric rate and is said to be irregularly identified this rate depends on relative tail conditions and can be as slow in some examples as n 1 4 this nonstandard rate of convergence can lead to numerical instability and or large standard errors we examine two weighted model examples i the binary response model under mean restriction introduced by lewbel 1997 and further generalized to cover endogeneity and selection where the estimator in this class of models is weighted by the density of a special regressor and ii the treatment effect model under exogenous selection rosenbaum and rubin 1983 where the resulting estimator of the average treatment effect is one that is weighted by a variant of the propensity score without strong relative support conditions these models similar to well known identified at infinity models lead to estimators that converge at slower than parametric rate since essentially to ensure point identification one requires some variables to take values on sets with arbitrarily small probabilities or thin sets for the two models above we derive some rates of convergence and propose that one conducts inference using rate adaptive procedures that are analogous to andrews and schafgans 1998 for the sample selection model

frequency analysis of tick quotes on foreign currency markets and the double threshold agent model abstract power spectrum densities for the number of tick quotes per minute market activity on three currency markets usd jpy eur usd and jpy eur are analyzed for periods from january 2000 to december 2000 we find some peaks on the power spectrum densities at a few minutes we develop the double threshold agent model and confirm that the corresponding periodicity can be observed for the activity of this model even though market participants perceive common weaker periodic information than threshold for decision making of them this model is numerically performed and theoretically investigated by utilizing the mean field approximation we propose a hypothesis that the periodicities found on the power spectrum densities can be observed due to nonlinearity and diversity of market participants

robustness of supersensitivity to small signals in nonlinear dynamical systems nonlinear dynamical systems possessing an invariant subspace can display interesting dynamical behavior such as on off intermittency and bubbling this rapid communication shows that a class of such systems has amazing features of 1 supersensitivity to small input signals and 2 robustness of the supersensitivity in the presence of noise these features make the systems very promising as small signal detectors

correlated noise in a logistic growth model the logistic differential equation is used to analyze cancer cell population in the presence of a correlated gaussian white noise we study the steady state properties of tumor cell growth and discuss the effects of the correlated noise it is found that the degree of correlation of the noise can cause tumor cell extinction

semiparametric quantile regression estimation in dynamic models with partially varying coefficients national science foundation of china 71131008 70871003 70971113 university of north carolina at charlotte chinese ministry of education minjiang scholarship from fujian province china xiamen university boston college

average and quantile effects in nonseparable panel models nonseparable panel models are important in a variety of economic settings including discrete choice this paper gives identification and estimation results for nonseparable models under time homogeneity conditions that are like time is randomly assigned or time is an instrument partial identification results for average and quantile effects are given for discrete regressors under static or dynamic conditions in fully nonparametric and in semiparametric models with time effects it is shown that the usual linear fixed effects estimator is not a consistent estimator of the identified average effect and a consistent estimator is given a simple estimator of identified quantile treatment effects is given providing a solution to the important problem of estimating quantile treatment effects from panel data bounds for overall effects in static and dynamic models are given the dynamic bounds provide a partial identification solution to the important problem of estimating the effect of state dependence in the presence of unobserved heterogeneity the impact of t the number of time periods is shown by deriving shrinkage rates for the identified set as t grows we also consider semiparametric discrete choice models and find that semiparametric panel bounds can be much tighter than nonparametric bounds computationally convenient methods for semiparametric models are presented we propose a novel inference method that applies in panel data and other settings and show that it produces uniformly valid confidence regions in large samples we give empirical illustrations

jackknife and analytical bias reduction for nonlinear panel models fixed effects estimators of panel models can be severely biased because of the well known incidental parameters problem we show that this bias can be reduced by using a panel jackknife or an analytical bias correction motivated by large t we give bias corrections for averages over the fixed effects as well as model parameters we find large bias reductions from using these approaches in examples we consider asymptotics where t grows with n as an approximation to the properties of the estimators in econometric applications we show that if t grows at the same rate as n the fixed effects estimator is asymptotically biased so that asymptotic confidence intervals are incorrect but that they are correct for the panel jackknife we show t growing faster than n 1 3 suffices for correctness of the analytic correction a property we also conjecture for the jackknife

influence of delayed time and colored noise in a bistable system subject to asymmetric dichotomous noise and colored noise the influence of delayed time and colored noise in a bistable system subject to asymmetric dichotomous noise and colored noise is studied applying small delay time approximation under the adiabatic limit condition the expression of the signal to noise ratio snr of the system is obtained it is found that the snr varies non monotonously with the delayed time and the correlation of the colored noise moreover the snr exhibits sr behavior when it is plotted as a function of the intensity and asymmetry of the dichotomous noise and as a function of the strength of the colored noise

stochastic resonance in a stochastic bistable system subject to additive white noise and dichotomous noise abstract the stochastic resonance sr in a stochastic stable system driven by a static force and a periodic square wave signal as well as by additive white noise and dichotomous noise is considered from the point of view of the signal to noise ratio snr it is found that the snr exhibits sr behavior when it is plotted as a function of the noise strength of the white noise and dichotomous noise as well as when plotted as a function of the static force moreover the influence of the strength of the stochastic potential force and the correlation rate of the dichotomous noise is investigated

asymptotic efficiency in estimation with conditional moment restrictions abstract in this paper bounds on asymptotic efficiency are derived for a class of non parametric models the data are independent and identically distributed according to some unknown distribution f there is a given function of the data and a parameter the restrictions are that a conditional expectation of this function is zero at some point in the parameter space this point is to be estimated if f is assumed to be a multinomial distribution with known finite support then the problem becomes parametric and the bound can be obtained from the information matrix this bound turns out to depend only upon certain conditional moments and not upon the support of the distribution since a general f can be approximated by a multinomial distribution the multinomial bound applies to the general case

quantile regression methods for recursive structural equation models abstract two classes of quantile regression estimation methods for the recursive structural equation models of chesher 2003 identification in nonseparable models econometrica 71 1405 1441 are investigated a class of weighted average derivative estimators based directly on the identification strategy of chesher is contrasted with a new control variate estimation method the latter imposes stronger restrictions achieving an asymptotic efficiency bound with respect to the former class an application of the methods to the study of the effect of class size on the performance of dutch primary school students shows that i reductions in class size are beneficial for good students in language and for weaker students in mathematics ii larger classes appear beneficial for weaker language students and iii the impact of class size on both mean and median performance is negligible

resonant behavior of a fractional oscillator with fluctuating frequency the long time behavior of the first moment for the output signal of a fractional oscillator with fluctuating frequency subjected to an external periodic force is considered colored fluctuations of the oscillator eigen frequency are modeled as a dichotomous noise the viscoelastic type friction kernel with memory is assumed as a power law function of time using the shapiro loginov formula exact expressions for the response to an external periodic field and for the complex susceptibility are presented on the basis of the exact formulas it is demonstrated that interplay of colored noise and memory can generate a variety of cooperation effects such as multiresonances versus the driving frequency and the friction coefficient as well as stochastic resonance versus noise parameters the necessary and sufficient conditions for the cooperation effects are also discussed particularly two different critical memory exponents have been found which mark dynamical transitions in the behavior of the system

stochastic resonance in a periodically driven linear system with multiplicative and periodically modulated additive white noises abstract the stochastic resonance sr of periodically driven linear system with multiplicative white noise and periodically modulated additive white noise is studied exact expressions of output signal to noise ratio are obtained the sr characteristics features are shown in two different situations the driven frequency is equal to the modulative frequency 1 and the driven frequency is not equal to the modulative frequency 1 the result was as follows the exact expression of output signal to noise ratio snr exhibits sr in three aspects in the case of 1 1 the curve of snr versus noise strength q exists a resonance peak this is a standard form of sr 2 a resonance peak exists in the snr vs curve in the region of 0 3 a maximum appears in the curve of snr as a function of the characteristic of signal itself the signal frequency it is of interest to examine the snr in the case of 1 from the exact expression of snr we see for 1 that the snr vs q curve exhibits a minimum firstly then a maximum that is to say it exhibits a suppression firstly resonance later and is very different from the case of 1

numerical evaluation of cauchy principal value integrals with singular integrands convergence results are proved for sequences of interpolatory integration rules for cauchy principal value integrals of the form fk x f x x a dx 1 a 1 when f x is singular at a point a 7 i and the singularity is ignored or avoided

product integration based on hermite feje r interpolation abstract the use of generalized hermite fejer interpolation polynomials for product integration is studied algorithms for implementing this method are developed and convergence results are proved comparisons are made between this new approach and the classical approach based on lagrange interpolation polynomials it turns out that integration rules based on hermite fejer interpolation converge in more cases but in general more slowly than those based on lagrange interpolation when the latter converge

mean convergence of hermite fejer interpolation abstract weighted l p convergence of hermite fejer interpolation based on the zeros of generalized jacobi polynomials is investigated the main result of the paper gives necessary and sufficient conditions for such convergence for all continuous functions

local linear quantile regression abstract in this article we study nonparametric regression quantile estimation by kernel weighted local linear fitting two such estimators are considered one is based on localizing the characterization of a regression quantile as the minimizer of e pp y a x x where p is the appropriate check function the other follows by inverting a local linear conditional distribution estimator and involves two smoothing parameters rather than one our aim is to present fully operational versions of both approaches and to show that each works quite well although either might be used in practice we have a particular preference for the second our automatic smoothing parameter selection method is novel the main regression quantile smoothing parameters are chosen by rule of thumb adaptations of state of the art methods for smoothing parameter selection for regression mean estimation the techniques are illustrated by application to two datasets and compared in simulations

stochastic resonance in a fractional harmonic oscillator subject to random mass and signal modulated noise stochastic resonance in a fractional harmonic oscillator with random mass and signal modulated noise is investigated applying linear system theory and the characteristics of the noises the analysis expression of the mean output amplitude gain oag is obtained it is shown that the oag varies non monotonically with the increase of the intensity of the multiplicative dichotomous noise with the increase of the frequency of the driving force as well as with the increase of the system frequency in addition the oag is a non monotonic function of the system friction coefficient as a function of the viscous damping coefficient as a function of the fractional exponent

pointwise simultaneous convergence of extended lagrange interpolation with additional knots in numerical analysis it is important to construct interpolating polynomials approximating a given function and its derivatives simultaneously the authors define some new good interpolating matrices with many nodes close to the endpoints of the interval and also give error estimates

identification and estimation of average partial effects in irregular correlated random coefficient panel data models in this paper we study identification and estimation of a correlated random coefficients crc panel data model the outcome of interest varies linearly with a vector of endogenous regressors the coefficients on these regressors are heterogenous across units and may covary with them we consider the average partial effect ape of a small change in the regressor vector on the outcome cf chamberlain 1984 wooldridge 2005a chamberlain 1992 calculated the semiparametric efficiency bound for the ape in our model and proposed a n consistent estimator nonsingularity of the ape s information bound and hence the appropriateness of chamberlain s 1992 estimator requires i the time dimension of the panel t to strictly exceed the number of random coefficients p and ii strong conditions on the time series properties of the regressor vector we demonstrate irregular identification of the ape when t p and for more persistent regressor processes our approach exploits the different identifying content of the subpopulations of stayers or units whose regressor values change little across periods and movers or units whose regressor values change substantially across periods we propose a feasible estimator based on our identification result and characterize its large sample properties while irregularity precludes our estimator from attaining parametric rates of convergence its limiting distribution is normal and inference is straightforward to conduct standard software may be used to compute point estimates and standard errors we use our methods to estimate the average elasticity of calorie consumption with respect to total outlay for a sample of poor nicaraguan households

uniform convergence of derivatives of extended lagrange interpolation the authors construct some extended interpolation formulae to approximate the derivatives of a function in uniform norm they prove theorems on uniform convergence and give estimates of pointwise type and of simultaneous approximation

quantile regression with varying coefficients quantile regression provides a framework for modeling statistical quantities of interest other than the conditional mean the regression methodology is well developed for linear models but less so for nonparametric models we consider conditional quantiles with varying coefficients and propose a methodology for their estimation and assessment using polynomial splines the proposed estimators are easy to compute via standard quantile regression algorithms and a stepwise knot selection algorithm the proposed rao score type test that assesses the model against a linear model is also easy to implement we provide asymptotic results on the convergence of the estimators and the null distribution of the test statistic empirical results are also provided including an application of the methodology to forced expiratory volume fev data

on average derivative quantile regression for fixed 0 1 the quantile regression function gives the th quantile x in the conditional distribution of a response variable y given the value x x of a vector of covariates it can be used to measure the effect of covariates not only in the center of a population but also in the upper and lower tails a functional that summarizes key features of the quantile specific relationship between x and y is the vector of weighted expected values of the vector of partial derivatives of the quantile function x in a nonparametric setting can be regarded as a vector of quantile specific nonparametric regression coefficients in survival analysis models e g cox s proportional hazard model proportional odds rate model accelerated failure time model and in monotone transformation models used in regression analysis gives the direction of the parameter vector in the parametric part of the model can also be used to estimate the direction of the parameter vector in semiparametric single index models popular in econometrics we show that under suitable regularity conditions the estimate of obtained by using the locally polynomial quantile estimate of chaudhuri 1991a is n 1 2 consistent and asymptotically normal with asymptotic variance equal to the variance of the influence function of the functional we discuss how the estimate of can be used for model diagnostics and in the construction of a link function estimate in general single index models

a survey on mean convergence of interpolatory processes abstract a detailed account of what happened in the theory of mean convergence of lagrange and hermite fejer type interpolation in the last fifty five years is given starting from the classical result of erd s and turan hopefully all important developments are considered in some cases with an indication of the method of proof even some yet unpublished results are included a list of references helps to orientate those interested in the details

network and panel quantile effects via distribution regression abstract this paper provides a method to construct simultaneous confidence bands for quantile functions and quantile effects in nonlinear network and panel models with unobserved two way effects strictly exogenous covariates and possibly discrete outcome variables the method is based upon projection of simultaneous confidence bands for distribution functions constructed from fixed effects distribution regression estimators these fixed effects estimators are debiased to deal with the incidental parameter problem under asymptotic sequences where both dimensions of the data set grow at the same rate the confidence bands for the quantile functions and effects have correct joint coverage in large samples an empirical application to gravity models of trade illustrates the applicability of the methods to network data

stochastic resonance in linear system due to dichotomous noise modulated by bias signal the stochastic resonance in an over damped linear system due to dichotomous noise modulated by a bias signal is studied in detail by the theory of signal to noise ratio snr and the shapiro loginov formula the exact expressions of the first two moments and snr for the output to the asymmetric dichotomous noise input are obtained it is found that each curve of the snr versus the multiplicative noise intensity exhibits a mono peak and the conventional stochastic resonance appears which is absent for the case of noise and periodic signal introduced additively meanwhile the snr is a non monotonic function of the signal frequency or the correlation time of noise and the bona fide stochastic resonance sr and sr in a broad sense exist moreover the snr depends on the additive noise intensity cross correlation strength and asymmetry of multiplicative noise

stochastic resonance in linear system driven by multiplicative and additive noise abstract the stochastic resonance sr is studied in an overdamped linear system driven by multiplicative and additive noise when the additive noise is a linear combination of an asymmetric dichotomous noise and its square the exact expressions are obtained for the first two moments and the correlation function and the sr phenomenon appeared there are three different forms of sr the bona fide sr the conventional sr and sr in the broad sense moreover the asymmetry of multiplicative noise has different effect on signal to ratio snr for the first two different forms of sr and the effects of multiplicative noise and additive noise on snr are different

noise free stochastic resonance in simple chaotic systems abstract the phenomenon of stochastic resonance sr is reported in a completely noise free situation with the role of thermal noise being taken by low dimensional chaos a one dimensional piecewise linear map and a pair of coupled excitatory inhibitory neurons are the systems used for the investigation both systems show a transition from symmetry broken to symmetric chaos on varying a system parameter in the latter state the systems switch between the formerly disjoint attractors due to the inherent chaotic dynamics this switching rate is found to resonate with the frequency of an externally applied periodic perturbation either parametric or additive the existence of a resonance in the response of the system is characterized in terms of the residence time distributions the results are an unambiguous indicator of the presence of sr like behavior in these systems analytical investigations supporting the observations are also presented the results have implications in the area of information processing in biological systems

stochastic resonance driven by two different kinds of colored noise in a bistable system the phenomenon of stochastic resonance in a bistable nonlinear system is investigated when both the multiplicative noise and the coupling between additive and multiplicative noise are colored with different values of noise correlation time ensuremath tau 1 and ensuremath tau 2 combining the functional analysis and unified colored noise approximation the two different kinds of colored noise in the nonlinear system can be simplified the signal to noise ratio is calculated when a weakly periodic signal is added to the system it is found that there appears a transition between one peak and two peaks in the curve of the signal to noise ratio when either the noise correlation time ensuremath tau 1 and ensuremath tau 2 or the coupling strength ensuremath lambda between additive and multiplicative noise is increased the transition between one and two peaks depending on ensuremath tau 1 and ensuremath lambda is more complex than that depending on ensuremath tau 2

efficiency bounds for semiparametric regression efficiency bounds for conditional moment restrictions with a nonparametric component are derived there is a given function of the data a random sample from a distribution f and a parameter the restriction is that a conditional expectation of this function is zero at some point in the parameter space the parameter has two parts a finite dimensional component and a general function evaluated at a subset of the conditioning variables an example is a regression function that is additive in parametric and nonparametric component as arises in sample selection models copyright 1992 by the econometric society

quantile regression in partially linear varying coefficient models semiparametric models are often considered for analyzing longitudinal data for a good balance between flexibility and parsimony in this paper we study a class of marginal partially linear quantile models with possibly varying coefficients the functional coefficients are estimated by basis function approximations the estimation procedure is easy to implement and it requires no specification of the error distributions the asymptotic properties of the proposed estimators are established for the varying coefficients as well as for the constant coefficients we develop rank score tests for hypotheses on the coefficients including the hypotheses on the constancy of a subset of the varying coefficients hypothesis testing of this type is theoretically challenging as the dimensions of the parameter spaces under both the null and the alternative hypotheses are growing with the sample size we assess the finite sample performance of the proposed method by monte carlo simulation studies and demonstrate its value by the analysis of an aids data set where the modeling of quantiles provides more comprehensive information than the usual least squares approach

aperiodic stochastic resonance stochastic resonance sr is a phenomenon wherein the response of a nonlinear system to a weak periodic input signal is optimized by the presence of a particular level of noise recently we presented a method and theory for characterizing sr type behavior in excitable systems with aperiodic i e broadband input signals phys rev e 52 r3321 1995 we coined the term aperiodic stochastic resonance asr to describe this general type of behavior in that earlier study we demonstrated asr in the fitzhugh nagumo neuronal model here we demonstrate asr in three additional systems a bistable well system an integrate and fire neuronal model and the hodgkin huxley hh neuronal model we present computational and theoretical results for each system in the context of the hh model we develop a general theory for asr in excitable membranes this work clearly shows that sr type behavior is not limited to systems with periodic inputs thus in general noise can serve to enhance the response of a nonlinear system to a weak input signal regardless of whether the signal is periodic or aperiodic textcopyright 1996 the american physical society

effect of correlated dichotomous noises on stochastic resonance in a linear system a linear system driven by correlated asymmetric dichotomous noises and periodic signal was investigated in the overdamped case the exact expressions of output signal amplitude and signal to noise ratio snr of the system were derived by means of numerical calculations we found that i at some fixed multiplicative noise intensities the output signal amplitude with frequency exhibits the structure of a weak peak even no peak as the dichotomous noise is asymmetric ii in the case of asymmetric dichotomous noise the signal frequency can cause non monotonous behavior of the output signal amplitude with respect to multiplicative noise intensity iii the curve of snr with frequency has a weak peak and a trough in the case of symmetric dichotomous noise but no peak with asymmetric iv whether the multiplicative noise is symmetric or asymmetric the noise can enhance response of the system v the snr increases with the correlation strength between the two noises decreasing in addition the plane of multiplicative noise intensity versus noise symmetric parameter was plotted

on off intermittency power spectrum and fractal properties of time series abstract some dynamical systems possess invariant submanifolds such that the dynamics restricted to the invariant submanifold is chaotic this situation arises in systems with a spatial symmetry or in the synchronization of chaotic oscillators the invariant submanifold could become unstable to perturbations in the transverse directions when a parameter of the system is changed through a critical blow out value this could result in an extreme form of temporally intermittent bursting called on off intermittency we propose a model that incorporates the universal features of systems that display on off intermittency we study this model both with and without additive noise and we derive scaling results for the power spectral density of the on off intermittent process and for the box counting dimension for the set of time intervals when the process takes on values above a given threshold we then present numerical simulations realizing these results

multiple cross correlation noise induced transition in a stochastic bistable system abstract based on the stochastic equivalent rules the fokker planck equation for a general one dimensional nonlinear system subjected to n component noises and cross correlation noises is derived and the greatest advantage of the method lies in its simplicity applying this method the effects of multiple sources of noise and the correlation forms of noises among them i e two multiplicative noises an additive noise and the correlation between the three noises on the steady state properties and the mean first passage time mfpt of a stochastic bistable system are discussed in details the results show rich transition phenomena such as the reentrance like noise induced phenomenon and the switch between the bimodal and the unimodal structure for different noise intensities moreover the effects of the cross correlation among the three noise sources on the mfpt are also discussed and the noise enhanced stability phenomenon and the resonant activation phenomenon are observed the numerical results are in basic agreement with the theoretical predictions

linear transmitter with correlated noises abstract a linear transmitter with correlated gaussian white additive and multiplicative noises and a periodic signal coupled either additively or multiplicatively is considered the correlations have a weak destructive effect in case of the additive signal and a strong constructive effect in case of the multiplicative one analytical results for the linear transmitter with the additive signal agree with those obtained previously by a different approach we also show how to analytically calculate certain expectation values involving exponentials of gaussian processes

on the uniform convergence of gaussian quadrature rules for cauchy principal value integrals in a previous paper the authors proposed a modified gaussian rule m wf t to compute the integral wf t in the cauchy principal value sense associated with the weightw and they proved the convergence in closed sets contained in the integration interval the main purpose of the present work is to prove uniform convergence of the sequence m wf t on the whole integration interval and to give estimates for the remainder term the same results are shown for particular subsequences of the gaussian rules m wf t for the evaluation of cauchy principal value integrals a result on the uniform convergence of the product rules is also discussed and an application to the numerical solution of singular integral equations is made