semi nonparametric estimation of binary response models with an application to natural resource valuation abstract a semi nonparametric binary response model is estimated to obtain the willingness to pay for the environmental quality improvement when the willingness to pay is restricted to be nonnegative the model estimation is straightforward by using conventional maximum likelihood software because the semi nonparametric model is the generalization of the parametric model the generality can be assessed by a likelihood ratio test by using an existing referendum survey data set it is shown that the semi nonparametric estimation provides a significant improvement over the parametric estimation the willingness to pay is sensitive to the distribution assumption

modeling long memory in stock market volatility inspired by the idea that regime switching may give rise to persistence that is observationally equivalent to a unit root we derive a regime switching process that exhibits long memory the feature of the process that generates long memory is a heavy tailed duration distribution using this process for volatility we obtain a regime switching stochastic volatility rssv model that we fit to daily s p returns from 1928 through 1995 by means of the efficient method of moments estimation emm method forecasts of rssv volatility given past returns can be generated by reprojection as we illustrate the rssv model is accepted according to the emm chi squared statistic using this statistic we also evaluate several other models that have been proposed in the literature and some modifications to them we find that models that exhibit long memory in volatility and heavy tails conditionally as does the rssv model fit the data whereas models without these characteristics do not we also find weak evidence that suggests the presence of an additional short memory component of volatility over and above the long memory component

seminonparametric bayesian estimation of the asymptotically ideal production model abstract recently it has been shown that seminonparametric methods can be used to produced high quality approximations to a firm s technology unlike the local approximations provided by the conventional class of flexible functional forms seminonparametric methods generate global spans within large classes of functions however that approach usually spans a much larger space than the neoclassical function space relevant to most production modeling an exception is the asymptotically ideal model aim generated from the muntz szatz series expansion since every basis function in that expansion is within the neoclassical function space a straightforward method exists for imposing neoclassical regularity when all factors are substitutes since the relevant constraints are inequality restrictions we implement the approach using bayesian methods to avoid the problems of sampling distribution truncation that would occur from sampling theoretic methods we further discuss the relevant extensions that would permit complementary factors nonconstant returns to scale and technological change

the computational cutoff rate of channels having memory corresp a random coding bound is derived for block coded transmission over channels characterized by a deterministic finite memory part followed by the addition of white gaussian noise intersymbol interference channels are a special case according m this bound a product measure cutoff rate can be defined quite naturally for such a class of channels

semiparametric estimation of censored selection models with a nonparametric selection mechanism abstract in this paper estimation of the coefficients in a single index selectivity bias model is considered under the assumption that the selection correction function depends on the conditional mean of some observable selection variable the estimation method follows a familiar two step strategy the first step uses a nonparametric regression estimator for the selection variable while the second step uses a weighted instrumental variables estimator for the coefficients in the equation of interest the paper gives conditions under which the proposed estimator is root n consistent and asymptotically normal the proposed method is applied to data on labor supply

smooth inference for survival functions with arbitrarily censored data we propose a procedure for estimating the survival function of a time to event random variable under arbitrary patterns of censoring the method is predicated on the mild assumption that the distribution of the random variable and hence the survival function has a density that lies in a class of smooth densities whose elements can be represented by an infinite hermite series truncation of the series yields a parametric expression that can well approximate any plausible survival density and hence survival function provided the degree of truncation is suitably chosen the representation admits a convenient expression for the likelihood for the parameters in the approximation under arbitrary censoring truncation that is straightforward to compute and maximize a test statistic for comparing two survival functions which is based on an integrated weighted difference of estimates of each under this representation is proposed via simulation studies and application to a number of data sets we demonstrate that the approach yields reliable inferences and can result in gains in efficiency over traditional nonparametric methods

estimation of nonparametric models with simultaneity we introduce methods for estimating nonparametric nonadditive models with simultaneity the methods are developed by directly connecting the elements of the structural system to be estimated with features of the density of the observable variables such as ratios of derivatives or averages of products of derivatives of this density the estimators are therefore easily computed functionals of a nonparametric estimator of the density of the observable variables we consider in detail a model where to each structural equation there corresponds an exclusive regressor and a model with one equation of interest and one instrument that is included in a second equation for both models we provide new characterizations of observational equivalence on a set in terms of the density of the observable variables and derivatives of the structural functions based on those characterizations we develop two estimation methods in the first method the estimators of the structural derivatives are calculated by a simple matrix inversion and matrix multiplication analogous to a standard least squares estimator but with the elements of the matrices being averages of products of derivatives of nonparametric density estimators in the second method the estimators of the structural derivatives are calculated in two steps in a first step values of the instrument are found at which the density of the observable variables satisfies some properties in the second step the estimators are calculated directly from the values of derivatives of the density of the observable variables evaluated at the found values of the instrument we show that both pointwise estimators are consistent and asymptotically normal

concentration of quantum states from quantum functional and transportation cost inequalities quantum functional inequalities e g the logarithmic sobolev and poincare inequalities have found widespread application in the study of the behavior of primitive quantum markov semigroups the classical counterparts of these inequalities are related to each other via a so called transportation cost inequality of order 2 tc2 the latter inequality relies on the notion of a metric on the set of probability distributions called the wasserstein distance of order 2 tc2 in turn implies a transportation cost inequality of order 1 tc1 in this paper we introduce quantum generalizations of the inequalities tc1 and tc2 making use of appropriate quantum versions of the wasserstein distances one recently defined by carlen and maas and the other defined by us we establish that these inequalities are related to each other and to the quantum modified logarithmic sobolev and poincare inequalities as in the classical case we also show that these inequalities imply certain concentration type results

semiparametric qualitative response model estimation with unknown heteroscedasticity or instrumental variables this paper provides estimators of discrete choice models including binary ordered and multinomial response choice models the estimators closely resemble ordinary and two stage least squares the distribution of the model s latent variable error is unknown and may be related to the regressors e g the model could have errors that are heteroscedastic or correlated with regressors the estimator does not require numerical searches even for multinomial choice for ordered and binary choice models the estimator is root n consistent and asymptotically normal a consistent estimator of the conditional error distribution is also provided

sieve m inference on irregular parameters abstract this paper presents sieve inferences on possibly irregular i e slower than root n estimable functionals of semi nonparametric models with i i d data we provide a simple consistent variance estimator of the plug in sieve m estimator of a possibly irregular functional and the asymptotic standard normality of the sieve t statistic we show that for hypothesis testing of irregular functionals the sieve likelihood ratio statistic is asymptotically chi square distributed these results are useful in inference on structural parameters that may have singular semiparametric efficiency bounds a simulation study and an empirical application of heckman and singer 1984 duration model are presented

regressions with berkson errors in covariates a nonparametric approach this paper establishes that so called instrumental variables enable the identification and the estimation of a fully nonparametric regression model with berkson type measurement error in the regressors an estimator is proposed and proven to be consistent its practical performance and feasibility are investigated via monte carlo simulations as well as through an epidemiological application investigating the effect of particulate air pollution on respiratory health these examples illustrate that berkson errors can clearly not be neglected in nonlinear regression models and that the proposed method represents an effective remedy

testing nonnested euler conditions with quadrature based methods of approximation abstract singleton 1985 proposed a test of the validity of an euler equation specification which incorporates information on a nonnested set of euler equations from an alternative economic model the information about the alternative model is introduced into the testing framework by constructing a sequence of local alternatives to the euler equations of the maintained model which are in the direction of the euler equations of the alternative model singleton s test is easy to apply but is designed to have power against an alternative that in many cases does not have a straightforward economic interpretation in this paper we reexamine the construction of local alternatives to euler equations we compare singleton s approach with a method based on perturbing the data generation process dgp of the maintained model in the direction of the dgp of the alternative model the second approach leads to tests which are always of interest but require the complete specification of the dgp in this paper we derive a test for discriminating between two nonnested sets of euler conditions which have been estimated us ing generalized method of moments gmm the test is based on the encompassing principle of cox 1961 and mizon and richard 1986 and uses tauchen s 1986 quadrature based nethods for approximating the expectation of nonlinear functions of stationary random variables singleton s empirical examples are also reconsidered

optimum soft decision demodulation for isi channels two different methods of soft decision demodulation for channels with finite intersymbol interference isi in the presence of additive white gaussian noise awgn are analyzed in both schemes the cutoff rate r sub 0 of the discrete channel created by the demodulator output quantization is chosen as the design criterion expressions for the optimal thresholds of the quantizer associated with the demodulation of binary signals are derived results for the channel with memory equal to one symbol duration are presented as a special case the 1 d channel with soft decision demodulation is analyzed closed form solutions show that a 4 b quantizer improves performance substantially in this case

distribution free maximum likelihood estimator of the binary choice model1 is a given function of the exogenous variables z and unknown parameters 9 representing the systematic component of the utility difference and f is the distribution function of the random component of the utility difference this paper describes a method of estimating the parameters 9 without assuming any functional form for the distribution function f and proves that this estimator is consistent f is also consistently estimated the method uses maximum likelihood estimation in which the likelihood is maximized not only over the parameter 9 but also over a space which contains all distribution functions

constrained bayes estimation with applications abstract bayesian techniques are widely used in these days for simultaneous estimation of several parameters in compound decision problems often however the main objective is to produce an ensemble of parameter estimates whose histogram is in some sense close to the histogram of population parameters this is for example the situation in subgroup analysis where the problem is not only to estimate the different components of a parameter vector but also to identify the parameters that are above and the others that are below a certain specified cutoff point we have proposed in this paper bayes estimates in a very general context that meet this need these estimates are obtained by matching the first two moments of the histogram of the estimates and the posterior expectations of the first two moments of the histogram of the parameters and minimizing subject to these conditions the posterior expectation of the euclidean distance between the estimates and the parameters several applications of the m

nonparametric likelihood ratio model selection tests between parametric likelihood and moment condition models we propose a nonparametric likelihood ratio testing procedure for choosing between a parametric likelihood model and a moment condition model when both models could be misspecified our procedure is based on comparing the kullback leibler information criterion klic between the parametric model and moment condition model we construct the klic for the parametric model using the difference between the parametric log likelihood and a sieve nonparametric estimate of population entropy and obtain the klic for the moment model using the empirical likelihood statistic we also consider multiple 2 model comparison tests when all the competing models could be misspecified and some models are parametric while others are moment based we evaluate the performance of our tests in a monte carlo study and apply the tests to an example from industrial organization

inference in semiparametric conditional moment models with partial identification abstract this paper develops inference methods for conditional moment models in which the unknown parameter is possibly partially identified and may contain infinite dimensional components for a conjectured restriction on the parameter we consider testing the hypothesis that the restriction is satisfied by at least one element of the identified set we propose using the sieve minimum of a kolmogorov smirnov type statistic as the test statistic derive its asymptotic distribution and provide consistent bootstrap critical values in this way a broad family of restrictions can be consistently tested making the proposed procedure applicable to testing the model specification and constructing confidence set for any given component or some feature of the parameter our methods are robust to partial identification and allow for the moment functions to be nonsmooth as an illustration we apply the proposed inference methods to study the quantile instrumental variable engel curves for gasoline in brazil a monte carlo study demonstrates finite sample performance

nonparametric specification testing via the trinity of tests tests are developed for inference on a parameter vector whose dimension grows slowly with sample size the statistics are based on the lagrange multiplier wald and pseudo likelihood ratio principles admit standard normal asymptotic distributions under the null and are straightforward to compute they are shown to be consistent and possessing non trivial power against local alternatives the settings considered include multiple linear regression panel data models with fixed effects and spatial autoregressions when a nonparametric regression function is estimated by series we use our statistics to propose specification tests and in semiparametric adaptive estimation we provide a test for correct error distribution specification these tests are nonparametric but handled in practice with parametric techniques a monte carlo study suggests that our tests perform well in finite samples two empirical examples use them to test for correct shape of an electricity distribution cost function and linearity and equality of engel curves

heteroscedastic nonlinear regression several parameter estimation methods for dealing with heteroscedasticity in nonlinear regression are described these include variations on ordinary weighted iteratively reweighted extended and generalized least squares some of these variations are new and one of them in particular modified extended iteratively reweighted least squares meirls allows parameters of an assumed heteroscedastic variance model to be estimated with an adjustment for bias due to estimation of the regression parameters the context of the discussion is primarily that of pharmacokinetic type data although an example is given involving chemical reaction data using simulated data from 21 heteroscedastic pharmacokinetic type models some of the methods are compared in terms of mean absolute error and 95 confidence interval coverage from these comparisons meirls and the variations on generalized least squares emerge as the methods of choice

instrumental variable treatment of nonclassical measurement error models while the literature on nonclassical measurement error traditionally relies on the availability of an auxiliary data set containing correctly measured observations we establish that the availability of instruments enables the identification of a large class of nonclassical nonlinear errors in variables models with continuously distributed variables our main identifying assumption is that conditional on the value of the true regressors some measure of location of the distribution of the measurement error e g its mean mode or median is equal to zero the proposed approach relies on the eigenvalue eigenfunction decomposition of an integral operator associated with specific joint probability densities the main identifying assumption is used to index the eigenfunctions so that the decomposition is unique we propose a convenient sieve based estimator derive its asymptotic properties and investigate its finite sample behavior through monte carlo simulations

linear mixed models with flexible distributions of random effects for longitudinal data normality of random effects is a routine assumption for the linear mixed model but it may be unrealistic obscuring important features of among individual variation we relax this assumption by approximating the random effects density by the seminonparameteric snp representation of gallant and nychka 1987 econometrics 55 363 390 which includes normality as a special case and provides flexibility in capturing a broad range of nonnormal behavior controlled by a user chosen tuning parameter an advantage is that the marginal likelihood may be expressed in closed form so inference may be carried out using standard optimization techniques we demonstrate that standard information criteria may be used to choose the tuning parameter and detect departures from normality and we illustrate the approach via simulation and using longitudinal data from the framingham study

efficient estimation of average treatment effects using the estimated propensity score we are interested in estimating the average effect of a binary treatment on a scalar outcome if assignment to the treatment is exogenous or unconfounded that is independent of the potential outcomes given covariates biases associated with simple treatmentcontrol average comparisons can be removed by adjusting for differences in the covariates rosenbaum and rubin 1983 show that adjusting solely for differences between treated and control units in the propensity score removes all biases associated with differences in covariates although adjusting for differences in the propensity score removes all the bias this can come at the expense of efficiency as shown by hahn 1998 heckman ichimura and todd 1998 and robins mark and newey 1992 we show that weighting by the inverse of a nonparametric estimate of the propensity score rather than the true propensity score leads to an efficient estimate of the average treatment effect we provide intuition for this result by showing that this estimator can be interpreted as an empirical likelihood estimator that efficiently incorporates the information about the propensity score

nonparametric analysis of a duration model with stochastic unobserved heterogeneity abstract this paper develops nonparametric identification and estimation results for a single spell hazard model where the unobserved heterogeneity is specified as a levy subordinator the identification approach solves a nonlinear volterra integral equation of the first kind with an unknown kernel function both the kernel of the integral operator which models the distribution of the unobserved heterogeneity and the functions that enter it are identified given regularity conditions and minimal variation in the observed covariates the paper proposes a shape constrained nonparametric two step sieve minimum distance estimator rates of convergence are derived and monte carlo experiments show the finite sample performance of the estimator

semiparametric robust estimation of truncated and censored regression models many estimation methods of truncated and censored regression models such as the maximum likelihood and symmetrically censored least squares scls are sensitive to outliers and data contamination as we document therefore we propose a semiparametric general trimmed estimator gte of truncated and censored regression which is highly robust but relatively imprecise to improve its performance we also propose data adaptive and one step trimmed estimators we derive the robust and asymptotic properties of all proposed estimators and show that the one step estimators e g one step scls are as robust as gte and are asymptotically equivalent to the original estimator e g scls the finite sample properties of existing and proposed estimators are studied by means of monte carlo simulations

estimation of some partially specified nonlinear models abstract this paper presents a procedure for analyzing a partially specified nonlinear regression model in which the nuisance parameter is an unrestricted function of a subset of regressors the procedure does not require parameteric modeling of the nuisance parameter but assumes that the model can be transformed into a partially specified linear equation by inverting some nonlinear functions the model parameters are estimated by applying robinson s 1988a procedure and the estimator is show to be n consistent and asymptotically normal one attraction of the estimator is that is to computationally simple requiring no more than least squares regressions a simulation study indicates that the estimator has practical values

estimation of the binary response model using a mixture of distributions estimator mod abstract in this paper we develop a semiparametric sieve estimator which is termed a mixture of distributions estimator mod to estimate a binary response model when the distribution of the errors is unknown the estimator of the distribution function is composed of a mixture of smooth distributions where the number of mixtures increases with the sample size the model is semiparametric because it is assumed that a parametric index type restriction holds optimal rates of convergence are established for the distribution function under the l 2 norm and conditions are derived under which estimates of the parametric component are asymptotically normal an appealing feature about mod is that it is possible to restrict the estimator of the distribution function a priori to be smooth nonnegative nondecreasing and to integrate to one this has important practical and theoretical implications

semi nonparametric cointegration testing this paper considers a semi nonparametric cointegration test the test uses the lm testing principle the score function needed for the lm test is estimated from the data using an expansion of the density around a student t distribution in this way we capture both the possible fat tailedness and the skewness of the innovation process using a monte carlo experiment we show that the semi nonparametric cointegration test has good size and power properties over a broad class of distributions for the innovation process we also investigate the effect of order selection of the underlying var on inference the complete methodology is illustrated using an interest rate example

the relative efficiency of method of moments estimators abstract the asymptotic relative efficiency of efficient method of moments when implemented with a seminonparametric auxiliary model is compared to that of conventional method of moments when implemented with polynomial moment functions because the expectations required by these estimators can be computed by simulation these two methods are commonly used to estimate the parameters of nonlinear latent variables models the comparison is for the models in the marron wand test suite a scale mixture of normals and the second largest order statistic of the lognormal distribution the latter models are representative of financial market data and auction data respectively which are the two most common applications of simulation estimators efficient method of moments dominates conventional method of moments over these models

on the asymptotic distribution theory of a class of consistent estimators of a distribution satisfying a uniform stochastic ordering constraint we identify the asymptotic behavior of the estimators proposed by rojo and samaniego and mukerjee of a distribution f assumed to be uniformly stochastically smaller than a known baseline distribution g we show that these estimators are sqrt n convergent to a limit distribution with mean squared error smaller than or equal to the mean squared error of the empirical survival function by examining the mean squared error of the limit distribution we are able to identify the optimal estimator within mukerjee s class under a variety of different assumptions on f and g similar theoretical results are developed for the two sample problem where f and g are both unknown the asymptotic distribution theory is applied to obtain confidence bands for the survival function bar f based on published data from an accelerated life testing experiment

bayesian inference in a sample selection model this paper develops methods of bayesian inference in a sample selection model the main feature of this model is that the outcome variable is only partially observed we first present a gibbs sampling algorithm for a model in which the selection and outcome errors are normally distributed the algorithm is then extended to analyze models that are characterized by nonnormality specifically we use a dirichlet process prior and model the distribution of the unobservables as a mixture of normal distributions with a random number of components the posterior distribution in this model can simultaneously detect the presence of selection effects and departures from normality our methods are illustrated using some simulated data and an abstract from the rand health insurance experiment

identification in a generalization of bivariate probit models with dummy endogenous regressors abstract this paper provides identification results for a class of models specified by a triangular system of two equations with binary endogenous variables the joint distribution of the latent error terms is specified through a parametric copula structure that satisfies a particular dependence ordering while the marginal distributions are allowed to be arbitrary but known this class of models is broad and includes bivariate probit models as a special case the paper demonstrates that having an exclusion restriction is necessary and sufficient for global identification in a model without common exogenous covariates where the excluded variable is allowed to be binary having an exclusion restriction is sufficient in models with common exogenous covariates that are present in both equations the paper then extends the identification analyses to a model where the marginal distributions of the error terms are unknown

on the decoding of convolutional codes on an unknown channel an algorithm is proposed for universal decoding of convolutional trellis codes employed over unknown channels on discrete memoryless channels and at rates below the channel s computational cutoff rate for a uniform input distribution the algorithm achieves an asymptotic complexity performance tradeoff similar to the tradeoff achieved by the viterbi 1979 algorithm but with the benefit that the algorithm s implementation does not require knowledge of the channel law the algorithm is also applicable to channels with memory and in particular to intersymbol interference isi channels to channels with nonlinear isi and even to general finite state channels

a maximum likelihood estimation method for random coefficient regression models summary a method for estimating the distribution of the parameters of a random coefficient regression model is proposed this distribution accounting for interindividual variability is assumed to lie in a wide class of probability distributions rather than in a given parametric class estimation is based on observations from a sample of individuals and likelihood is the estimation criterion experimental designs may be different among individuals allowing the method to apply to routinely collected data the problem has strong connections with the theory of optimum design of experiments conditions are given under which the problem has a unique solution which then corresponds to a discrete distribution a simple pharmacokinetic model involving two parameters is used as an example these parameters have a bimodal distribution as statistical specification moreover only one observation is available per individual thus the method applies even when the model is not identifiable

bounds on the symmetric binary cutoff rate for dispersive gaussian channels bounds on the symmetric binary cutoff rate for a pulse amplitude modulated pam signaling over dispersive gaussian channels are evaluated and discussed these easily calculable bounds can be used to estimate the reliable rate of information transmission and the error exponent behavior for binary two level pam schemes operating through a prefiltered additive white gaussian channel the memory of which is long enough to make the exact evaluation of the cutoff rate formidable the core of the bounding technique relies on a probabilistic interpretation of a fundamental theorem in matrix theory regarding the logarithm of the largest eigenvalue of a nonnegative primitive matrix commonly applied in large deviation problems these bounds are calculated for some examples and their respective tightness is considered further potential applications of the proposed bounding technique are pointed out

a regularization corrected score method for nonlinear regression models with covariate error present here a new general method for adjusting for covariate error our method consists of an approximate version of the stefanski nakamura corrected score approach using the method of regularization to obtain an approximate solution of the relevant integral equation we develop the theory in the setting of classical likelihood models this setting covers for example linear regression nonlinear regression logistic regression and poisson regression the method is extremely general in terms of the types of measurement error models covered and is a functional method in the sense of not involving assumptions on the distribution of the true covariate we discuss the theoretical properties of the method and present simulation results in the logistic regression setting univariate and multivariate for illustration we apply the method to data from the harvard nurses health study concerning the relationship between physical activity and breast cancer mortality in the period following a diagnosis of breast cancer

the nonlinear mixed effects model with a smooth random effects density summary the fixed parameters of the nonlinear mixed effects model and the density of the random effects are estimated jointly by maximum likelihood the density of the random effects is assumed to be smooth but is otherwise unrestricted the method uses a series expansion that follows from the smoothness assumption to represent the density and quadrature to compute the likelihood standard algorithms are used for optimization empirical bayes estimates of random coefficients are obtained by computing posterior modes the method is applied to data from pharmacokinetics and properties of the method are investigated by application to simulated data

semiparametric estimation of the type 3 tobit model abstract this paper considers estimation of the type 3 tobit model with only some weak restrictions imposed on the distribution of the error terms two least squares type estimation approaches are proposed under the condition that the error terms and regressors are independent consistent estimators for the asymptotic covariance matrices are proposed to facilitate large sample inference and a small monte carlo simulation is performed to investigate the finite sample behavior of the proposed estimators also the semiparametric efficiency bound is derived for the type 3 tobit model under the independence restriction

maximum entropy autoregressive conditional heteroskedasticity model abstract in many applications it has been found that the autoregressive conditional heteroskedasticity arch model under the conditional normal or student s t distributions are not general enough to account for the excess kurtosis in the data moreover asymmetry in the financial data is rarely modeled in a systematic way in this paper we suggest a general density function based on the maximum entropy me approach that takes account of asymmetry excess kurtosis and also of high peakedness the me principle is based on the efficient use of available information and as is well known many of the standard family of distributions can be derived from the me approach we demonstrate how we can extract information functional from the data in the form of moment functions we also propose a test procedure for selecting appropriate moment functions our procedure is illustrated with an application to the nyse stock returns the empirical results reveal that the me approach with a fewer moment functions leads to a model that captures the stylized facts quite effectively

semiparametric maximum likelihood for measurement error model regression this paper presents an em algorithm for semiparametric likelihood analysis of linear generalized linear and nonlinear regression models with measurement errors in explanatory variables a structural model is used in which probability distributions are specified for a the response and b the measurement error a distribution is also assumed for the true explanatory variable but is left unspecified and is estimated by nonparametric maximum likelihood for various types of extra information about the measurement error distribution the proposed algorithm makes use of available routines that would be appropriate for likelihood analysis of a and b if the true x were available simulations suggest that the semiparametric maximum likelihood estimator retains a high degree of efficiency relative to the structural maximum likelihood estimator based on correct distributional assumptions and can outperform maximum likelihood based on an incorrect distributional assumption the approach is illustrated on three examples with a variety of structures and types of extra information about the measurement error distribution

a random linear functional approach to efficiency bounds abstract this paper suggests a method for obtaining efficiency bounds in models containing either only infinite dimensional parameters or both finite and infinite dimensional parameters semiparametric models the method is based on a theory of random linear functionals applied to the gradient of the log likelihood functional and is illustrated by computing the lower bound for cox s regression model

sieve maximum likelihood estimation of the spatial autoregressive tobit model abstract this paper extends the ml estimation of a spatial autoregressive tobit model under normal disturbances in xu and lee 2015b journal of econometrics to distribution free estimation we examine the sieve mle of the model where the disturbances are i i d with an unknown distribution we show that the spatial autoregressive process with tobit censoring and related variables are spatial near epoch dependent ned a related contribution is that we develop some exponential inequalities for spatial ned random fields with these inequalities we establish the consistency of the estimator asymptotic distributions of structural parameters of the model are derived from a functional central limit theorem and projection simulations show that the sieve mle can improve the finite sample performance upon misspecified normal mles as an empirical application we examine the school district income surtax rates in iowa our results show that the spatial spillover effects are significant but they may be overestimated if disturbances are restricted to be normally distributed

robust rank estimation for transformation models with random effects summarysemiparametric transformation models with random effects are useful in analysing recurrent and clustered data with specified error and random effect distributions zeng lin 2007a proved that nonparametric maximum likelihood estimators are semiparametric efficient in this paper we consider a more general class of transformation models with random effects under which an unknown monotonic transformation of the response is linearly related to the covariates and the random effects with unspecified error and random effect distributions this includes many popular models we propose an estimator based on the maximum rank correlation which relies on symmetry of the random effect distribution and establish its consistency and asymptotic normality a random weighting resampling scheme is employed for inference the proposed method can be extended to censored and clustered data numerical studies demonstrate that the proposed method performs well in practical situations application of the method is illustrated with the framingham cholesterol data

a longitudinal measurement error model with a semicontinuous covariate covariate measurement error in regression is typically assumed to act in an additive or multiplicative manner on the true covariate value however such an assumption does not hold for the measurement error of sleep disordered breathing sdb in the wisconsin sleep cohort study wscs the true covariate is the severity of sdb and the observed surrogate is the number of breathing pauses per unit time of sleep which has a nonnegative semicontinuous distribution with a point mass at zero we propose a latent variable measurement error model for the error structure in this situation and implement it in a linear mixed model the estimation procedure is similar to regression calibration but involves a distributional assumption for the latent variable modeling and model fitting strategies are explored and illustrated through an example from the wscs

performance bounds and cutoff rates for data channels affected by correlated randomly time variant multipath fading the symmetric cutoff rate is derived for quadrature amplitude modulated qam block coded data transmissions over noisy links affected by time correlated multipath phenomena which introduce intersymbol interference isi in the received signal maximum likelihood ml decoding supported by perfect channel state information csi is assumed due to the presence of time correlated isi phenomena the resulting transmission link constitutes an example of a continuous state channel with memory

distribution free estimation of heteroskedastic binary response models using probit logit criterion functions abstract in this paper estimators for distribution free heteroskedastic binary response models are proposed the estimation procedures are based on relationships between distribution free models with a conditional median restriction and parametric models such as probit logit exhibiting multiplicative heteroskedasticity the first proposed estimator is based on the observational equivalence between the two models and is a semiparametric sieve estimator see e g gallant and nychka 1987 ai and chen 2003 and chen et al 2005 for the regression coefficients based on maximizing standard logit probit criterion functions such as nlls and mle this procedure has the advantage that choice probabilities and regression coefficients are estimated simultaneously the second proposed procedure is based on the equivalence between existing semiparametric estimators for the conditional median model manski 1975 manski 1985 horowitz 1992 and the standard parametric probit logit nlls estimator this estimator has the advantage of being implementable with standard software packages such as stata distribution theory is developed for both estimators and a monte carlo study indicates they both perform well in finite samples

nonparametric estimation of stage occupation probabilities in a multistage model with current status data multistage models are used to describe individuals or experimental units moving through a succession of stages corresponding to distinct states e g healthy diseased diseased with complications dead the resulting data can be considered to be a form of multivariate survival data containing information about the transition times and the stages occupied traditional survival analysis is the simplest example of a multistage model where individuals begin in an initial stage say alive and move irreversibly to a second stage death in this article we consider general multistage models with a directed tree structure progressive models in which individuals traverse through stages in a possibly non markovian manner we construct nonparametric estimators of stage occupation probabilities and marginal cumulative transition hazards empirical calculations of these quantities are not possible due to the lack of complete data we consider current status information which represents a more severe form of censoring than the commonly used right censoring asymptotic validity of our estimators can be justified using consistency results for nonparametric regression estimators finite sample behavior of our estimators is studied by simulation in which we show that our estimators based on these limited data compare well with those based on complete data we also apply our method to a real life data set arising from a cardiovascular diseases study in taiwan

semi nonparametric var forecasts for hedge funds during the recent crisis abstract the need to provide accurate value at risk var forecasting measures has triggered an important literature in econophysics although these accurate var models and methodologies are particularly demanded for hedge fund managers there exist few articles specifically devoted to implement new techniques in hedge fund returns var forecasting this article advances in these issues by comparing the performance of risk measures based on parametric distributions the normal student s t and skewed t semi nonparametric snp methodologies based on gram charlier gc series and the extreme value theory evt approach our results show that normal student s t and skewed t based methodologies fail to forecast hedge fund var whilst snp and evt approaches accurately success on it we extend these results to the multivariate framework by providing an explicit formula for the gc copula and its density that encompasses the gaussian copula and accounts for non linear dependences we show that the var obtained by the meta gc accurately captures portfolio risk and outperforms regulatory var estimates obtained through the meta gaussian and student s t distributions

item response theory with estimation of the latent population distribution using spline based densities the purpose of this paper is to introduce a new method for fitting item response theory models with the latent population distribution estimated from the data using splines a spline based density estimation system provides a flexible alternative to existing procedures that use a normal distribution or a different functional form for the population distribution a simulation study shows that the new procedure is feasible in practice and that when the latent distribution is not well approximated as normal two parameter logistic 2pl item parameter estimates and expected a posteriori scores eaps can be improved over what they would be with the normal model an example with real data compares the new method and the extant empirical histogram approach

semi nonparametric estimation of independently and identically repeated first price auctions via an integrated simulated moments method abstract in this paper we propose to estimate the value distribution of independently and identically repeated first price auctions directly via a semi nonparametric integrated simulated moments sieve approach given a candidate value distribution function in a sieve space we simulate bids according to the equilibrium bid function involved we take the difference of the empirical characteristic functions of the actual and simulated bids as the moment function the objective function is then the integral of the squared moment function over an interval minimizing this integral to the distribution functions in the sieve space involved and letting the sieve order increase to infinity with the sample size then yields a uniformly consistent semi nonparametric estimator of the actual value distribution also we propose an integrated moment test for the validity of the first price auction model and an data driven method for the choice of the sieve order finally we conduct a few numerical experiments to check the performance of our approach

semiparametric efficiency in gmm models with auxiliary data we study semiparametric efficiency bounds and efficient estimation of parameters defined through general moment restrictions with missing data identification relies on auxiliary data containing information about the distribution of the missing variables conditional on proxy variables that are observed in both the primary and the auxiliary database when such distribution is common to the two data sets the auxiliary sample can be independent of the primary sample or can be a subset of it for both cases we derive bounds when the probability of missing data given the proxy variables is unknown or known or belongs to a correctly specified parametric family we find that the conditional probability is not ancillary when the two samples are independent for all cases we discuss efficient semiparametric estimators an estimator based on a conditional expectation projection is shown to require milder regularity conditions than one based on inverse probability weighting

inference in nonparametric instrumental variables with partial identification this paper develops methods for hypothesis testing in a nonparametric instrumental variables setting within a partial identification framework we construct and derive the asymptotic distribution of a test statistic for the hypothesis that at least one element of the identified set satisfies a conjectured restriction the same test statistic can be employed under identification in which case the hypothesis is whether the true model satisfies the posited property an almost sure consistent bootstrap procedure is provided for obtaining critical values possible applications include testing for semiparametric specifications as well as building confidence regions for certain functionals on the identified set as an illustration we obtain confidence intervals for the level and slope of brazilian fuel engel curves a monte carlo study examines finite sample performance

semiparametric bayesian inference in autoregressive panel data models this paper develops bayesian methods for inference in dynamic panel data models with individual effects and applies them to study longitudinal data on earnings from the panel study of income dynamics psid we study semiparametric versions of commonly used random effects autoregressive models in which the distribution of the disturbances is not restricted to fall in a parametric class to model the unknown distributions without resorting to strong parametric assumptions we draw upon recent advances in the theory and computation of nonparametric bayesian models using dirichlet process priors the overall approach can be viewed as an application of the general semiparametric bayesian approach of west muller and escobar 1994 which in turn makes use of results on bayesian density estimation by escobar 1994 and escobar and west 1995 most conventional panel data methods e g anderson and hsiao 1981 macurdy

two stage non gaussian qml estimation of garch models and testing the efficiency of the gaussian qmle abstract in generalized autoregressive conditional heteroskedastic garch models the standard identifiability assumption that the variance of the iid process is equal to 1 can be replaced by an alternative moment assumption we show that for estimating the original specification based on the standard identifiability assumption efficiency gains can be expected from using a quasi maximum likelihood qml estimator based on a non gaussian density and a reparameterization based on an alternative identifiability assumption a test allowing to determine whether a reparameterization is needed that is whether the more efficient qmle is obtained with a non gaussian density is proposed

estimating stochastic differential equations efficiently by minimum chi squared summary we propose a minimum chi squared estimator for the parameters of an ergodic system of stochastic differential equations with partially observed state we prove that the efficiency of the estimator approaches that of maximum likelihood as the number of moment functions entering the chi squared criterion increases and as the number of past observations entering each moment function increases the minimised criterion is asymptotically chi squared and can be used to test system adequacy when a fitted system is rejected inspecting studentised moments suggests how the fitted system might be modified to improve the fit the method and diagnostic tests are applied to daily observations on the u s dollar to deutschmark exchange rate from 1977 to 1992

on nonparametric maximum likelihood estimation with interval censoring and left truncation summary a graph theoretical approach is employed to describe the support set of the nonparametric maximum likelihood estimator for the cumulative distribution function given interval censored and left truncated data a necessary and sufficient condition for the existence of a nonparametric maximum likelihood estimator is then derived two previously analysed data sets are revisited

a new semiparametric spatial model for panel time series abstract this paper presents a semiparametric model for large dimension vector time series whose elements correspond to economic agents dependence between agents variables is characterized using a spatial model functions of agents economic distances provide restrictions that enable estimation of a vector autoregressive specification we present sufficient conditions for our model to generate stationary mixing series with finite higher order moments we estimate the model using a simple two step sieve least squares procedure where the sieve estimators are constructed to preserve shape restrictions on the functions of economic distance e g positive definiteness of a covariance function we provide rates of convergence for the sieve estimators t limiting distributions for the model s finite dimensional parameters and a bootstrap method for inference in an illustrative application we use this model to characterize how the comovement in output growth across us industrial sectors depends on the similarity of sectors technologies we also present a small monte carlo evaluation of our estimators

estimation of stochastic volatility models with diagnostics abstract efficient method of moments emm is used to fit the standard stochastic volatility model of various extensions to several daily financial time series emm matches to the score of the model determined by data analysis called the score generator discrepancies reveal characteristics of data that stochastic volatility models cannot approximate the two score generators employed here are semiparametric arch and nonlinear nonparametric with the first the standard model is rejected although some extensions are accepted with the second all versions are rejected the extensions required for an adequate fit are so elaborate that nonparametric specifications are probably more convenient

using conditional moments of asset payoffs to infer the volatility of intertemporal marginal rates of substitution previously hansen and jagannathan 1990a derived and computed mean standard deviation frontiers for intertemporal marginal rates of substitution imrs implied by asset market data these frontiers give the lower bounds on the standard deviations as a function of the mean in this paper we develop a strategy for utilizing conditioning information efficiently and hence improve on the standard deviation bounds computed by hansen and jagannathan we implement this strategy empirically by using the seminonparametric snp methodology suggested by gallant and tauchen 1989 to estimate the conditional distribution of a vector of monthly asset payoffs we use the fitted conditional distributions to calculate both conditional and unconditional standard deviation bounds for the imrs the unconditional bounds are as sharp as possible subject to robustness considerations we also use the fitted distributions to compute the moments of various candidate marginal rates of substitution suggested by economic theory and in particular the time nonseparable preferences of dunn and singleton 1986 and eichenbaum and hansen 1990 for these preferences our findings suggest that habit persistence will put the moments of the imrs inside the frontier at reasonable values of the curvature parameter at the same time we uncover evidence that the implied imrs fails to satisfy all of the restrictions inherent in the euler equation the findings help explain why euler equation estimation methods typically find evidence in favor of local durability instead of habit persistence for monthly data

semi nonparametric maximum likelihood estimation on developpe une approche basee sur une innovation due a phillips 1983 sur les approximations des fonctions de densite

semiparametric estimation of count regression models this paper develops a semiparametric estimation approach for mixed count regression models based on series expansion for the unknown density of the unobserved heterogeneity we use the generalized laguerre series expansion around a gamma baseline density to model unobserved heterogeneity in a poisson mixture model we establish the consistency of the estimator and present a computational strategy to implement the proposed estimation techniques in the standard count model as well as in truncated censored and zero inflated count regression models monte carlo evidence shows that the finite sample behavior of the estimator is quite good the paper applies the method to a model of individual shopping behavior

era s a new approach to small sample theory this article proposes a new approach to small sample theory that achieves a meaningful integration of earlier directions of research in this field the approach centers on the constructive technique of approximating distributions developed recently by the author in 10 this technique utilizes extended rational approximants era s which build on the strengths of alternative less flexible approximation methods such as those based on asymptotic expansions and which simultaneously blend information from diverse analytic numerical and experimental sources the first part of the article explores the general theory of approximation of continuous probability distributions by means of era s existence characterization error bound and uniqueness theorems for these approximants are given and a new proof is provided for the convergence result obtained earlier in 10 some further aspects of finding era s by modifications to multiple point pade approximants are presented and the new approach is applied to the noncircular serial correlation coefficient the results of this application demonstrate how era s provide systematic improvements over edgeworth and saddlepoint techniques these results taken with those of the earlier article 10 suggest that the approach offers considerable potential for empirical application in terms of its reliability convenience and generality

an elasticity can be estimated consistently without a priori knowledge of functional form we consider an open question in applied price theory without a priori knowledge of a firm s cost function or a consumer s indirect utility function is it possible to estimate price and substitution elasticities consistently by observing a demand system as the work of white 30 guilkey lovell and sickles 19 and others has shown ordinary flexible functional forms such as the translog cannot achieve this objective we find that if one is prepared to assume that elasticities of substitution cannot oscillate wildly over the region of interest then consistent estimation is possible using the fourier flexible form provided the number of fitted parameters increases as the number of observations increases this result obtains with any of the commonly used statistical methods as for example multivariate least squares maximum likelihood and three stage least squares it obtains if the number of fitted parameters is chosen adaptively by observing the data or chosen deterministically according to some fixed rule we approach the problem along the classical lines of estimability considerations as used in the study of less than full rank linear statistical models and thereby discover that the problem has a fascinating structure which we explore in detail

a discrete time model for daily s p500 returns and realized variations jumps and leverage effects abstract we develop an empirically highly accurate discrete time daily stochastic volatility model that explicitly distinguishes between the jump and continuous time components of price movements using nonparametric realized variation and bipower variation measures constructed from high frequency intraday data the model setup allows us to directly assess the structural inter dependencies among the shocks to returns and the two different volatility components the model estimates suggest that the leverage effect or asymmetry between returns and volatility works primarily through the continuous volatility component the excellent fit of the model makes it an ideal candidate for an easy to implement auxiliary model in the context of indirect estimation of empirically more realistic continuous time jump diffusion and levy driven stochastic volatility models effectively incorporating the interdaily dependencies inherent in the high frequency intraday data

a parametric approach to flexible nonlinear inference author s hamilton james d abstract this paper proposes a new framework for determining whether a given relationship is nonlinear what the nonlinearity looks like and whether it is adequately described by a particular parametric model the paper studies a regression or forecasting model of the form yt xt et where the functional form of is unknown we propose viewing itself as the outcome of a random process the paper introduces a new stationary random random field m that generalizes finite differenced brownian motion to a vector field and whose realizations could represent a broad class of possible forms for we view the parameters that characterize the relation between a given realization of m and the particular value of for a given sample as population parameters to be estimated by maximum likelihood or bayesian methods we show that the resulting inference about the functional relation also yields consistent estimates for a broad class of deterministic functions the paper further develops a new test of the null hypothesis of linearity based on the lagrange multiplier principle and small sample confidence intervals based on numerical bayesian methods an empirical application suggests that properly accounting for the nonlinearity of the inflation unemployment tradeoff may explain the previously reported uneven empirical success of the phillips curve

nonparametric errors in variables models with measurement errors on both sides of the equation abstract measurement errors are often correlated as in surveys where respondent s biases or tendencies to err affect multiple reported variables we extend schennach 2007 to identify moments of the conditional distribution of a true y given a true x when both are measured with error the measurement errors in y and x are correlated and the true unknown model of y given x has nonseparable model errors after showing nonparametric identification we provide a sieve generalized method of moments based estimator of the model and apply it to nonparametric engel curve estimation in our application measurement errors on the expenditures of a good y are by construction correlated with measurement errors in total expenditures x this problem which is present in many consumption data sets has been ignored in most demand applications we find that accounting for this problem casts doubt on hildenbrand s 1994 increasing dispersion assumption

large deviation probabilities for certain nonparametric maximum likelihood estimators let x d be a measurable space and p ti q e ee e t e t a family of probability measures given an appropriate estimator sequence for 0 we define a sequence of asymptotic maximum likelihood estimators for t and give bounds for its large deviation probabilities under conditions which are natural for the application to the estimation of mixing distributions this paper generalizes earlier results of pfanzagl to the following cases i estimator sequences restricted to a sieve ii estimator sequences using a given estimator sequence for a nuisance parameter iii convergence under the wrong model iv large deviation probabilities instead of consistency

robust empirical bayes analyses of event rates a collection of i similar items generates point event histories for example machines experience failures or operators make mistakes suppose the intervals between events are modeled as iid exponential i or the counts as poisson i t i for the ith item furthermore so as to represent between item variability each individual rate parameter i is presumed drawn from a fixed super population with density g being a vector parameter a parametric empirical bayes peb setup for g specified alternatively as log student t n or gamma we exhibit the results of numerical procedures for estimating superpopulation parameters ll and for describing pooled estimates of the individual rates i obtained via bayes s formula three data sets are analyzed and convenient explicit approximate formulas are furnished for i estimates in the student t case the individual estimates are seen to have a robust quality

novel analytical performance bounds for symbol by symbol decoding of digital data impaired by isi and awgn new analytical upper bounds are derived for the performance of symbol by symbol maximum likelihood ml abend fritchman type detectors for multi amplitude phase modulated digital sequences transmitted over time dispersive generally time variant linear channels introducing finite intersymbol interference isi and additive white gaussian noise awgn the proposed bounds are obtained starting from a novel form of the standard bhattacharyya bound and take explicitly into account the allowed decoding delay both deterministic and random multipath slow fading rayleigh transmission isi channels are considered simulation results obtained for some test channels of practical interest show the tightness of the derived analytical bounds