testing single index restrictions with a focus on average derivatives abstract this paper considers a situation where the violation of a single index restriction is a concern only to the extent that it causes bias to the estimates of the average derivatives we propose a method to construct tests that concentrate their asymptotic powers upon only such interesting alternatives the test has a 1 2 limiting distribution under the null hypothesis and even accommodates the case where the parameter estimates have a convergence rate slower than n as in the case of maximum score estimation the testing procedure can be easily modified when the main interest lies in average increment effects of binary covariates multivariate average derivatives or linear restrictions other than those of average derivatives results from monte carlo experiments show that the asymptotic theory is a reasonable approximation of the finite sample distributions and an application of our methods to female labor market participation illustrates the importance of this non omnibus approach

simulated latent variable estimation of models with ordered categorical data this paper addresses the problem that occurs when estimating a linear regression model in which some of the explanatory variables are represented by categorical indicators we replace the categorical variable with a simulated latent variable slv drawn from a truncated multivariate normal distribution using the gibbs sampler consistent and efficient parameter estimates are derived using generalized two stage least squares where the instrument set contains a second slv drawn from the same distribution consistent estimates of the parameter covariance matrix are derived using a two stage procedure we illustrate this methodology using a monte carlo simulation and also investigate the consequences of incorrect distributional assumptions the simulated latent variable procedure is shown to be superior to conventional methods involving dummy variables or conditional means

inverse m matrices ii this is an update of the 1981 survey by the first author in the meantime a considerable amount has been learned about the very special structure of the important class of inverse m matrices developments since the earlier survey are emphasized but we have tried to be somewhat complete and some results have not previously been published some proofs are given where appropriate and references are given for others after some elementary preliminaries results are grouped by certain natural categories

maximum score estimation of disequilibrium models and the role of anticipatory price setting abstract in this paper a general model of disequilibrium quantity determination under median regression assumptions is combined with weak assumptions on the price adjustment to yield a consistent maximum score estimator in contrast to sapra 1986 the supply and demand parameters are separately identifiable and the estimator employs all of the observed data the price adjustment assumptions can be motivated by the anticipatory pricing model of green and laffont 1981 in contrast to the green laffont model however our assumptions only require that long run walrasian prices have positive probability as an illustrative application maximum score estimates of a model of the us commercial loan market are computed and compared to maximum likelihood estimates monte carlo results are also reported

misspecified heteroskedasticity in the panel probit model a small sample comparison of gmm and sml estimators abstract this paper compares generalized method of moments gmm and simulated maximum likelihood sml approaches to the estimation of the panel probit model both techniques circumvent multiple integration of joint density functions without the need to restrict the error term variance covariance matrix of the latent normal regression model particular attention is paid to a three stage gmm estimator based on nonparametric estimation of the optimal instruments for given conditional moment functions monte carlo experiments are carried out which focus on the small sample consequences of misspecification of the error term variance covariance matrix the correctly specified experiment reveals the asymptotic efficiency advantages of sml the gmm estimators outperform sml in the presence of misspecification in terms of multiplicative heteroskedasticity this holds in particular for the three stage gmm estimator allowing for heteroskedasticity over time increases the robustness with respect to misspecification in terms of multiplicative heteroskedasticity an application to the product innovation activities of german manufacturing firms is presented

subvector inference when the true parameter vector may be near or at the boundary abstract extremum estimators are not asymptotically normally distributed when the estimator satisfies the restrictions on the parameter space such as the non negativity of a variance parameter and the true parameter vector is near or at the boundary this possible lack of asymptotic normality makes it difficult to construct tests for testing subvector hypotheses that control asymptotic size in a uniform sense and have good local asymptotic power irrespective of whether the true parameter vector is at near or far from the boundary we propose a novel estimator that is asymptotically normally distributed even when the true parameter vector is near or at the boundary and the objective function is not defined outside the parameter space the proposed estimator allows the implementation of a new test based on the conditional likelihood ratio statistic that is easy to implement controls asymptotic size and has good local asymptotic power properties furthermore we show that the test enjoys certain asymptotic optimality properties when the parameter of interest is scalar in an application of the random coefficients logit model berry levinsohn and pakes 1995 to the european car market we find that for most parameters the new test leads to tighter confidence intervals than the two sided t test commonly used in practice

efficient elliptic curve scalar multiplication algorithms resistant to power analysis this paper presents four algorithms for securing elliptic curve scalar multiplication against power analysis the highest weight binary form hbf of scalars and randomization are applied to resist power analysis by using a special method to recode the scalars the proposed algorithms do not suffer from simple power analysis spa with the randomization of the secret scalar or base point three of the four algorithms are secure against differential power analysis dpa refined power analysis rpa and zero value point attacks zpa the countermeasures are also immune to the doubling attack fast shamir s method is used in order to improve the efficiency of parallel scalar multiplication compared with previous countermeasures the new countermeasures achieve higher security and do not impact overall performance

bagging binary and quantile predictors for time series abstract bootstrap aggregating or bagging introduced by breiman 1996a bagging predictors machine learning 24 123 140 has been proved to be effective to improve on unstable forecast theoretical and empirical works using classification regression trees variable selection in linear and non linear regression have shown that bagging can generate substantial prediction gain however most of the existing literature on bagging has been limited to the cross sectional circumstances with symmetric cost functions in this paper we extend the application of bagging to time series settings with asymmetric cost functions particularly for predicting signs and quantiles we use quantile predictions to construct a binary predictor and the majority voted bagging binary prediction we show that bagging may improve the binary prediction in small sample but it does not improve in large sample various bagging forecast combination weights are used such as equal weighted and bayesian model averaging bma weighted combinations for demonstration we present results from monte carlo experiments and from empirical applications using monthly s p500 and nasdaq stock index returns

cross sectional aggregation of non linear models abstract this paper considers the problem of cross sectional aggregation when the underlying micro behavioural relations are characterized by general non linear specifications it focuses on forecasting the aggregates and shows how an optimal aggregate model can be derived by minimizing the mean squared prediction errors conditional on the aggregate information the paper also derives model selection criteria for distinguishing between aggregate and disaggregate models when the primary object of the analysis is forecasting the aggregates and establishes the consistency of the model selection criteria in large samples in the case of standard non linear micro relations with additive errors it also provides suitable small sample corrections for more general non linear specifications we consider bootstrap techniques to correct for small sample bias of the proposed model selection criteria some of the ideas in the paper are illustrated using log linear micro relations often employed in applied research the paper also contains an empirical application where log linear production functions are estimated for the uk economy disaggregated by eight industrial sectors and at the aggregate level over the period 1954 1995

fast and flexible elliptic curve point arithmetic over prime fields we present an innovative methodology for accelerating the elliptic curve point formulae over prime fields this flexible technique uses the substitution of multiplication with squaring and other cheaper operations by exploiting the fact that field squaring is generally less costly than multiplication applying this substitution to the traditional formulae we obtain faster point operations in unprotected sequential implementations we show the significant impact our methodology has in protecting against simple side channel attacks ssca we modify the ecc point formulae to achieve a faster atomic structure when applying atomicity side channel protection in contrast to previous atomic operations that assumed squarings are undistinguishable from multiplications our new atomic structure offers true ssca protection because it includes squaring in its formulation we also extend our implementation to parallel architectures such as simd single instruction multiple data with the introduction of a new coordinate system and with the flexibility of our methodology we present to our knowledge the fastest formulae for simd based schemes that are capable of executing 3 and 4 operations simultaneously finally a new parallel ssca protected scheme is proposed for multiprocessor parallel architectures by applying the atomic structure presented in this work our parallel and atomic operations are shown to be significantly faster than previous implementations

simulation and the asymptotics of optimization estimators on demontre un theoreme de limite centrale general pour des estimateurs definis par minimisation de la longueur d une fonction de critere aleatoire a valeurs vectorielles aucune hypothese de regularite suffisante n est imposee sur la fonction de critere pour que les resultats puissent etre appliques a une classe d estimlateurs de simulation assez large des analyses completes de deux estimateurs de simulation l un introduit par pakes l autre par mcfadden illustrent l application des theoremes generaux

likelihood and other approaches to prediction in dynamic models abstract in this paper we consider the problem of generating multi period predictions from two simple dynamic models an autoregressive model and a geometric random walk the autoregressive model constitutes a useful paradigm for many of the practical problems of prediction because it possesses a number of features that differentiate it sharply from the standard linear regression model the geometric random walk model is widely used in macroeconomics and finance and is fundamentally non normal the ideal situation for the prediction problem would be to know the true density of the future observations unfortunately that density depends on parameters that are unknown and must be estimated we analyze six prediction functions approximations of the true density that attempt to circumvent this problem we contrast the theoretical properties of the likelihood prediction function proposed by cooley and parke 1986 with certainty equivalence prediction functions and mean squared error prediction functions the results of a monte carlo study illustrate the relative performance of the alternative prediction functions for conditional predictions and for the analysis of policy interventions the results confirm the importance of accounting for parameter uncertainty and approximating the true shape of the future density

a stopping rule for the computation of generalized method of moments estimators to obtain consistency and asymptotic normality a generalized method of moments gmm estimator typically is defined to be an approximate global minimizer of a gmm criterion function to compute such an estimator however can be problematic because of the difficulty of global optimization in consequence practitioners usually ignore the problem and take the gmm estimator to be the result of a local optimization algorithm this yields an estimator that is not necessarily consistent and asymptotically normal the use of a local optimization algorithm also can run into the problem of instability due to flats or ridges in the criterion function which makes it difficult to know when to stop the algorithm to alleviate these problems of global and local optimization we propose a stopping rule sr procedure for computing gmm estimators the sr procedure eliminates the need for global search with high probability and it provides an explicit sr for problems of stability that may arise with local optimization problems

a computationally practical simulation estimator for panel data in this paper i develop a practical extension of mcfadden s method of simulated moments estimator for limited dependent variable models to the panel data case the method is based on a factorization of the msm first order condition into transition probabilities along with the development of a new highly accurate method for similating these transition probabilities a series of monte carlo tests show that this msm estimator performs quite well relative to quadrature based ml estimators even when large numbers of quadrature points are employed the estimator also performs well relative to simulated ml even when a highly accurate method is used to simulate the choice probabilities in terms of computational speed complex panel data models involving random effects and arma errors may be estimated via msm in times similar to those necessary for estimation of simple random effects models via ml quadrature

numerical analysis of one dimensional motion of magma without mass forces abstract in this paper we present numerical treatment for a compaction driven darcian flow viscoelastic rock magma model this problem is a strongly coupled system of one quasi linear parabolic equation and one integro differential equation for the density and the porosity of the flow the numerical discretization uses cell centered finite difference method combined with semi implicit and implicit time stepping implicit explicit schemes as well as implicit explicit iterative algorithms have been developed to solve the corresponding discrete problems some properties positivity boundness conservation of the numerical solutions are investigated convergence study of the iteration processes is also presented the efficiency and the accuracy of the proposed methods are illustrated numerically by test examples with near real data

estimation of a censored regression panel data model using conditional moment restrictions efficiently honore has introduced a semiparametric estimator for the censored regression panel data model with fixed individual effects newey has shown how to obtain efficient estimators under a given conditional moment restriction we apply newey s approach to obtain a two step gmm estimator which is more efficient than the honore estimator we compare this estimator to the honore estimator and to parametric estimators including chamberlain s quasi fixed effects estimator in a monte carlo experiment we also extend the honore estimator and the two step gmm estimator to the case of a balanced or unbalanced panel of more than two waves we apply the estimators to an empirical example concerning earnings of married females using data from the dutch socio economic panel

efficient high dimensional importance sampling the paper describes a simple generic and yet highly accurate efficient importance sampling eis monte carlo mc procedure for the evaluation of high dimensional numerical integrals eis is based upon a sequence of auxiliary weighted regressions which actually are linear under appropriate conditions it can be used to evaluate likelihood functions and byproducts thereof such as ml estimators for models which depend upon unobservable variables a dynamic stochastic volatility model and a logit panel data model with unobserved heterogeneity random effects in both dimensions are used to provide illustrations of eis high numerical accuracy even under small number of mc draws mc simulations are used to characterize the finite sample numerical and statistical properties of eis based ml estimators

a hybrid estimator in nonlinear and generalised linear mixed effects models summary a hybrid method that combines laplace s approximation and monte carlo simulations to evaluate integrals in the likelihood function is proposed for estimation of the parameters in nonlinear mixed effects models that assume a normal parametric family for the random effects simulations show that these parametric estimates of fixed effects are close to the nonparametric estimates even though the mixing distribution is far from the assumed normal parametric family an asymptotic theory of this hybrid method for parametric estimation without requiring the true mixing distribution to belong to the assumed parametric family is developed to explain these results this hybrid method and its asymptotic theory are also extended to generalised linear mixed effects models

the double base number system and its application to elliptic curve cryptography we describe an algorithm for point multiplication on generic elliptic curves based on a representation of the scalar as a sum of mixed powers of 2 and 3 the sparseness of this so called double base number system combined with some efficient point tripling formulae lead to efficient point multiplication algorithms for curves defined over both prime and binary fields side channel resistance is provided thanks to side channel atomicity

a computationally efficient quadrature procedure for the one factor multinomial probit model a problem of estimation that has long confronted many economists is the difficulty of estimating the parameters of equations with limited dependent variables on cross section time series i e panel data while there are widely available packaged computer programs for estimating either a cross section probit and tobit models or b simple permanent transitory random effects panel models with continuous dependent variables there are no available computationally feasible methods of combining these two models this is because the likelihood function that arises in such a combined model contains multivariate normal integrals whose evaluation is quite difficult if not impossible with conventional approximation methods there is a widespread feeling among those working in the area that one possible method of evaluation the use of quadrature techniques is in principle possible but is in practice computationally too burdensome to consider e g albright et al 2 p 13 hausman and wise 6 p 12 in this note we point out that this is true only of standard quadrature techniques such as trapezoidal integration or its improved variants gaussian quadrature on the other hand is extremely efficient and is well within the bounds of computational feasibility on modern computers in what follows we state the nature of the integrals that need to be evaluated provide a brief exposition of gaussian quadrature and provide a numerical illustration of its use in

eigenvalue localization and neville elimination abstract neville elimination is an elimination procedure alternative to gaussian elimination and very adequate when dealing with some special classes of matrices in this paper we present pivoting strategies such that the radii of the gersgorin circles of the schur complements through neville elimination with these pivoting strategies reduce their length and we consider classes of matrices important in many applications we include illustrative examples comparing the results with those obtained with gaussian elimination and showing that our hypotheses are necessary

the method of simulated scores for the estimation of ldv models the method of simulated scores mss is presented for estimating limited dependent variables models ldv with flexible correlation structure in the unobservables we propose simulators that are continuous in the unknown parameter vectors and hence standard optimization methods can be used to compute the mss estimators that employ these simulators the first continuous method relies on a recursive conditioning of the multivariate normal density through a cholesky triangularization of its variance covariance matrix the second method combines results about the conditionals of the multivariate normal distribution with gibbs resampling techniques we establish consistency and asymptotic normality of the mss estimators and derive suitable rates at which the number of simulations must rise if biased simulators are used

uniformly semiparametric efficient estimation of treatment effects with a continuous treatment this article studies identification estimation and inference of general unconditional treatment effects models with continuous treatment under the ignorability assumption we show identification of the parameters of interest the dose response functions under the assumption that selection to treatment is based on observables we propose a semiparametric two step estimator and consider estimation of the dose response functions through moment restriction models with generalized residual functions that are possibly nonsmooth this general formulation includes average and quantile treatment effects as special cases the asymptotic properties of the estimator are derived namely uniform consistency weak convergence and semiparametric efficiency we also develop statistical inference procedures and establish the validity of a bootstrap approach to implement these methods in practice monte carlo simulations show that the proposed methods have good finite sample properties finally we apply the proposed

mmc techniques for limited dependent variables models implementation by the branch and bound algorithm we propose a finite sample approach to some of the most common limited dependent variables models the method rests on the maximized monte carlo mmc test technique proposed by dufour 1998 monte carlo tests with nuisance parameters a general approach to finite sample inference and nonstandard asymptotics journal of econometrics this issue we provide a general way for implementing tests and confidence regions we show that the decision rule associated with a mmc test may be written as a mixed integer programming problem the branch and bound algorithm yields a global maximum in finite time an appropriate choice of the statistic yields a consistent test while fulfilling the level constraint for any sample size the technique is illustrated with numerical data for the logit model c 2005 elsevier b v all rights reserved

signed digit representations of minimal hamming weight the authors give an online algorithm for computing a canonical signed digit representation of minimal hamming weight for any integer n using combinatorial techniques the probability distributions pr k sub r h where k sub r is taken to be a random variable on the uniform probability space of k digit integers is computed also using a markov chain analysis it is shown that e k sub r approximately r 1 k r 1 as k to infinity

the fitting of complex parametric models consider parametric models that are too complicated to allow calculation of a likelihood but from which observations can be simulated we examine parameter estimators that are linear functions of a possibly large set of candidate features a combination of simulations based on a fractional design and sets of discriminant analyses is then used to find an optimal estimator of the vector parameter and its covariance matrix the procedure is an alternative to the approximate bayesian computation scheme copyright 2012 oxford university press

a smooth likelihood simulator for dynamic disequilibrium models abstract this article considers the simulation of likelihood functions for dynamic disequilibrium models without sample separation information a recursive simulation algorithm is proposed the recursive simulation algorithm is computationally tractable for a class of dynamic disequilibrium models and serially correlated disturbance models the simulated likelihood functions are smooth in parameters monte carlo studies are provided to demonstrate the computational efficiency of this approach and investigate finite sample properties of simulated likelihood estimators and likelihood ratio test statistics regime classification rules based on simulated likelihood are introduced finite sample results of the simulated likelihood approach are encouraging

total positivity and neville elimination abstract neville elimination is described in terms of schur complements of matrices and used to improve some well known characterizations of totally positive and strictly totally positive matrices

elliptic curve cryptosystems we discuss analogs based on elliptic curves over finite fields of public key cryptosystems which use the multiplicative group of a finite field these elliptic curve cryptosystems may be more secure because the analog of the discrete logarithm problem on elliptic curves is likely to be harder than the classical discrete logarithm problem especially over gf 2 we discuss the question of primitive points on an elliptic curve modulo p and give a theorem on nonsmoothness of the order of the cyclic subgroup generated by a global point

m matrices whose inverses are totally positive abstract we fully characterize the class of totally positive matrices whose inverses are m matrices improving upon the characterization given by markham we also characterize the set of m matrices whose inverses are totally positive and show that this set is formed by the tridiagonal m matrices

universal donsker classes and metric entropy when mathfrak f is a universal donsker class then for independent indetically distributed i i d observation mathbf x 1 ldots mathbf x n with an unknown law p for any mathfrak f i in mathfrak f i 1 ldots m quad n 1 2 left mathfrak f 1 left mathbf x 1 right ldots mathfrak f i left mathbf x n right right 1 leq i leq m is asymptotically normal with mean vector n 1 2 left int mathfrak f i left mathbf x n right d mathbf p left x right right 1 leq i leq m and covariance matrix int mathfrak f i mathfrak f j d mathbf p int mathfrak f id mathbf p int mathfrak f jd mathbf p uniformly for mathfrak f i in mathfrak f then for certain statistics formed frome the mathfrak f i left mathbf x k right even where mathfrak f i may be chosen depending on the mathbf x k there will be asymptotic distribution as n rightarrow infty for example for mathbf x 2 statistics where f i are indicators of disjoint intervals depending suitably on mathbf x 1 ldots mathbf x n whose union is the real line mathbf x 2 quadratic forms have limiting distributions roy 1956 and watson 1958 which may however not be mathbf x 2 distributions and may depend on p chernoff and lehmann 1954 universal donsker classes of sets are up to mild measurability conditions just classes satisfying the vapnik cervonenkis comdinatorial conditions defined later in this section donsker the vapnik cervonenkis combinatorial conditions defined later in this section durst and dudley 1981 and dudley 1984 chapter 11 the use of such classes allows a variety of extensions of the roy watson results to general multidimensional sample spaces pollard 1979 and moore and subblebine 1981 vapnik and cervonenkis 1974 indicated application of their families of sets to classification pattern recognition problems more recently the classes have been applied to tree structured classifiacation breiman friedman olshen and stone 1984 chapter 12

structural estimation of pairwise stable networks with nonnegative externality abstract this paper develops a framework to structurally estimate pairwise stable networks with nonnegative externality we characterize pairwise stable equilibria as a fixed point of a certain mapping and show that the set of pairwise stable equilibria with nonnegative externality is a complete lattice we extend the characterization to an econometric framework for structural estimation based on the moment inequality model we apply our methodology to friendship networks of students in the united states using data from add health we find that the preference toward racial homophily is overestimated if we do not control for the preference toward clustering

high performance architecture of elliptic curve scalar multiplication a high performance architecture of elliptic curve scalar multiplication based on the montgomery ladder method over finite field gf 2m is proposed a pseudo pipelined word serial finite field multiplier with word size w suitable for the scalar multiplication is also developed implemented in hardware this system performs a scalar multiplication in approximately 6lceilm wrceil m 1 clock cycles and the gate delay in the critical path is equal to tand lceillog2 w k rceiltxor where tand and txor are delays due to two input and and xor gates respectively and 1 les k lt w is used to shorten the critical path

field inversion and point halving revisited we present a careful analysis of elliptic curve point multiplication methods that use the point halving technique of knudsen and schroeppel and compare these methods to traditional algorithms that use point doubling the performance advantage of halving methods is clearest in the case of point multiplication kp where p is not known in advance and smaller field inversion to multiplication ratios generally favor halving although halving essentially operates on affine coordinate representations we adapt an algorithm of knuth to allow efficient use of projective coordinates with halving based windowing methods for point multiplication

on ranking and generalization bounds the problem of ranking is to predict or to guess the ordering between objects on the basis of their observed features in this paper we consider ranking estimators that minimize the empirical convex risk we prove generalization bounds for the excess risk of such estimators with rates that are faster than 1 n we apply our results to commonly used ranking algorithms for instance boosting or support vector machines moreover we study the performance of considered estimators on real data sets

a numerically stable quadrature procedure for the one factor random component discrete choice model abstract the gaussian quadrature formula had been popularized by butler and moffitt 1982 econometrika 50 761 764 for the estimation of the error component probit panel model borjas and sueyoshi 1994 journal of econometrics 64 164 182 pointed out some numerical and statistical difficulties of applying it to models with group effects with a moderate or large number of individuals in a group the likelihood function of the model evaluated by the gaussian quadrature formula can be numerically unstable and at worst impossible to evaluate statistical inference may also be inaccurate we point out that some of these difficulties can be overcome with a carefully designed algorithm and the proper selection of the number of quadrature points however with a very large number of individuals in a group the gaussian quadrature formulation of integral may have large numerical approximation errors

semiparametric estimation of a censored regression model with endogeneity abstract censoring and endogeneity are common in empirical applications however the existing semiparametric estimation methods for the censored regression model with endogeneity under an independence restriction are associated with some drawbacks in this paper we propose a new semiparametric estimator that overcomes these drawbacks we derive conditional quantile moment conditions for all the conditional quantiles and propose a moment based estimator in particular we construct two types of moment conditions and develop a computationally attractive estimator we show that our estimator is consistent and asymptotic normal a monte carlo study indicates that our estimator performs well in finite samples and compares favorably with existing methods

randomized mixed radix scalar multiplication a set of congruence relations is a mathbb z covering if each integer belongs to at least one congruence class from that set in this paper we first show that most existing scalar multiplication algorithms can be formulated in terms of covering systems of congruences then using a special form of covering systems called exact n covers we present a novel uniformly randomized scalar multiplication algorithm with built in protections against most passive side channel attacks our algorithm randomizes the addition chain using a mixed radix representation of the scalar its reduced overhead and purposeful robustness could make it a sound replacement to several conventional countermeasures in particular it is significantly faster than coron s scalar blinding technique for elliptic curves when the choice of a particular finite field tailored for speed compels to double the size of the scalar hence the cost of the scalar multiplication

higher order properties of approximate estimators many modern estimation methods in econometrics approximate an objective function for instance through simulation or discretization these approximations typically affect both bias and variance of the resulting estimator we first provide a higher order expansion of such approximate estimators that takes into account the errors due to the use of approximations we show how a newton raphson adjustment can reduce the impact of approximations then we use our expansions to develop inferential tools that take into account approximation errors we propose adjustments of the approximate estimator that remove its first order bias and adjust its standard errors these corrections apply to a class of approximate estimators that includes all known simulation based procedures a monte carlo simulation on the mixed logit model shows that our proposed adjustments can yield significant improvements at a low computational cost

endogeneity in stochastic frontier models abstract stochastic frontier models are typically estimated by maximum likelihood mle or corrected ordinary least squares the consistency of either estimator depends on exogeneity of the explanatory variables inputs in the production frontier setting we will investigate the case that one or more of the inputs is endogenous in the simultaneous equation sense of endogeneity that is we worry that there is correlation between the inputs and statistical noise or inefficiency in a standard regression setting simultaneity is handled by a number of procedures that are numerically or asymptotically equivalent these include 2sls using the residual from the reduced form equations for the endogenous variables as a control function and mle of the system that contains the equation of interest plus the unrestricted reduced form equations for the endogenous variables liml we will consider modifications of these standard procedures for the stochastic frontier setting the paper is mostly a survey and combination of existing results from the stochastic frontier literature and the classic simultaneous equations literature but it also contains some new results

maximum likelihood estimation of limited and discrete dependent variable models with nested random effects abstract gauss hermite quadrature is often used to evaluate and maximize the likelihood for random component probit models unfortunately the estimates are biased for large cluster sizes and or intraclass correlations we show that adaptive quadrature largely overcomes these problems we then extend the adaptive quadrature approach to general random coefficient models with limited and discrete dependent variables the models can include several nested random effects intercepts and coefficients representing unobserved heterogeneity at different levels of a hierarchical dataset the required multivariate integrals are evaluated efficiently using spherical quadrature rules simulations show that adaptive quadrature performs well in a wide range of situations

pipelined computation of scalar multiplication in elliptic curve cryptosystems extended version in the current work we propose a pipelining scheme for implementing elliptic curve cryptosystems ecc the scalar multiplication is the dominant operation in ecc it is computed by a series of point additions and doublings the pipelining scheme is based on a key observation to start the subsequent operation one need not wait until the current one exits the next operation can begin while a part of the current operation is still being processed to our knowledge this is the first attempt to compute the scalar multiplication in such a pipelined manner also the proposed scheme can be made resistant to sidechannel attacks sca our scheme compares favorably with all sca resistant sequential and parallel methods

nonparametric estimation of distributional policy effects this paper proposes a fully nonparametric procedure to evaluate the effect of a counterfactual change in the distribution of some covariates on the unconditional distribution of an outcome variable of interest in contrast to other methods we do not restrict attention to the effect on the mean in particular our method can be used to conduct inference on the change of the distribution function as a whole its moments and quantiles inequality measures such as the lorenz curve or gini coefficient and to test for stochastic dominance the practical applicability of our procedure is illustrated via a simulation study and an empirical example

identification and estimation of nonlinear dynamic panel data models with unobserved covariates abstract this paper considers nonparametric identification of nonlinear dynamic models for panel data with unobserved covariates including such unobserved covariates may control for both the individual specific unobserved heterogeneity and the endogeneity of the explanatory variables without specifying the distribution of the initial condition with the unobserved variables we show that the models are nonparametrically identified from two periods of the dependent variable y i t and three periods of the covariate x i t the main identifying assumptions include high level injectivity restrictions and require that the evolution of the observed covariates depends on the unobserved covariates but not on the lagged dependent variable we also propose a sieve maximum likelihood estimator mle and focus on two classes of nonlinear dynamic panel data models i e dynamic discrete choice models and dynamic censored models we present the asymptotic properties of the sieve mle and investigate the finite sample properties of these sieve based estimators through a monte carlo study an intertemporal female labor force participation model is estimated as an empirical illustration using a sample from the panel study of income dynamics psid

a two stage estimator for probit models with structural group effects this paper outlines a two stage technique for estimation and inference in probit models with structural group effects the structural group specification belongs to a broader class of random components models in particular individuals in a given group share a common component in the specification of the conditional mean of a latent variable for a number of computational reasons existing random effects models are impractical for estimation and inference in this type of problem our two stage estimator provides an easily estimable alternative to the random effects specification in addition we conduct a monte carlo simulation comparing the performance of alternative es timators and find that the two stage estimator is superior both in terms of estimation and inference to traditional estimators

convenient estimators for the panel probit model the paper shows that several estimators for the panel probit model suggested in the literature belong to a common class of gmm estimators they are relatively easy to compute because they are based on conditional moment restrictions involving univariate moments of the binary dependent variable only applying nonparametric methods we discuss an estimator that is optimal in this class a monte carlo study shows that a particular variant of this estimator has good small sample properties and that the efficiency loss compared to maximum likelihood is small an application to the product innovation decisions of german firms reveals the expected efficiency gains

simulation based inference a survey with special reference to panel data models abstract in this article we study the recent developments of inference methods based on simulations in particular we discuss the simulated generalized method of moments the simulated maximum likelihood method and the simulated pseudo maximum likelihood methods the asymptotic properties of the estimators are described when the number of observations n goes to infinity and we distinguish the case where the number h of simulations per observation is fixed from the case where this number also goes to infinity in the former case the possible asymptotic bias is evaluated and in the latter case we carefully examine the consequences of the assumptions on the relative divergence rates of n and h we also show how these methods apply to various contexts in particular to the case of panel data models

estimation and inference with weak semi strong and strong identification this paper analyzes the properties of standard estimators tests and confidence sets cs s for parameters that are unidentified or weakly identified in some parts of the parameter space the paper also introduces methods to make the tests and cs s robust to such identification problems the results apply to a class of extremum estimators and corresponding tests and cs s that are based on criterion functions that satisfy certain asymptotic stochastic quadratic expansions and that depend on the parameter that determines the strength of identification this covers a class of models estimated using maximum likelihood ml least squares ls quantile generalized method of moments generalized empirical likelihood minimum distance and semi parametric estimators the consistency lack of consistency and asymptotic distributions of the estimators are established under a full range of drifting sequences of true distributions the asymptotic sizes in a uniform sense of standard and identification robust tests and cs s are established the results are applied to the arma 1 1 time series model estimated by ml and to the nonlinear regression model estimated by ls in companion papers the results are applied to a number of other models

first order side channel attacks on zhang s countermeasures abstract zhang s three countermeasures are known to be secure against certain first order side channel attacks such as differential power analysis and correlation power analysis this security comes from the countermeasures use of random points to blind the message and random integers to blind the secret scalar in this paper we propose first order side channel attack methods that can perfectly break these three countermeasures even though zhang s countermeasures use random points and random integers our attacks are made possible by the fact that intermediate values computed by these countermeasures are dependent on specific values that we can guess the experimental results verify that the proposed attack methods can successfully break existing countermeasures

consistent estimation of the fixed effects stochastic frontier model abstract in this paper we consider a fixed effects stochastic frontier model that is we have panel data fixed individual firm effects and the usual stochastic frontier analysis sfa composed error maximum likelihood estimation mle of this model has been considered by greene 2005a greene 2005b it is subject to the incidental parameters problem that is to possible inconsistency due to the number of parameters growing with the number of firms in the linear regression model with normal errors it is known that the mle of the regression coefficients is consistent and the inconsistency due to the incidental parameters problem applies only to the error variance greene s simulations suggest that the same is true in the fixed effects sfa model in this paper we take a somewhat different approach we consider mle based only on the joint density of the deviations from means in the linear regression model with normal errors this estimator is the same as the full mle for the regression coefficients but it yields a consistent estimator of the error variance for the sfa model the mle based on the deviations from means is not the same as the full mle and it has the advantage of not being subject to the incidental parameters problem the derivation of the joint density of the deviations from means is made possible by results in the statistical literature on the closed skew normal family of distributions these results may be of independent interest to researchers in this area simulations indicate that our within mle estimator performs quite well in finite samples we also present an empirical example

extended elliptic curve montgomery ladder algorithm over binary fields with resistance to simple power analysis in this paper we propose a scalar multiplication algorithm on elliptic curves over gf 2 m the proposed algorithm is an extended version of the montgomery ladder algorithm with the quaternary representation of the scalar in addition in order to improve performance we have developed new composite operation formulas and apply them to the proposed scalar multiplication algorithm the proposed composite formulas are 2p 1 2p 2 3p 1 p 2 and 4p 1 where p 1 and p 2 are points on an elliptic curve they can be computed using only the x coordinate of a point p x y in the affine coordinate system however the proposed scalar multiplication algorithm is vulnerable to simple power analysis attacks because different operations are performed depending on the bits of the scalar unlike the original montgomery ladder algorithm therefore we combine the concept of the side channel atomicity with the proposed composite operation formulas to prevent simple power analysis furthermore to optimize the computational cost we use the montgomery trick which can reduce the number of finite field inversion operations used in the affine coordinate system as the result the proposed scalar multiplication algorithm saves at least 26 of running time with small storage compared to the previous algorithms such as window based methods and comb based methods

best nonlinear three stage least squares estimation of certain econometric models a method is presented for estimating nonlinear simultaneous equations and transformation models in the presence of disturbance distribution of unknown form it asymptotically achieves the lower variance bound for instrumental variables estimates the author avoids smoothed nonparametric estimation his instruments averaging over the unsmoothed empirical distribution of preliminary residuals he allows for stationary serial dependence in various settings estimates are proposed and large sample inference rules justified these being unaffected if the optimal instruments use only an arbitrarily small vanishing fraction of the residuals the author investigates theoretically the effect of such computational savings on the goodness of the normal approximation copyright 1991 by the econometric society

likelihood approximation by numerical integration on sparse grids the calculation of likelihood functions of many econometric models requires the evaluation of integrals without analytical solutions approaches for extending gaussian quadrature to multiple dimensions discussed in the literature are either very specific or suffer from exponentially rising computational costs in the number of dimensions we propose an extension that is very general and easily implemented and does not suffer from the curse of dimensionality monte carlo experiments for the mixed logit model indicate the superior performance of the proposed method over simulation techniques

two step estimation of panel data models with censored endogenous variables and selection bias this paper presents some two step estimators for a wide range of parametric panel data models with censored endogenous variables and sample selection bias our approach is to derive estimates of the unobserved heterogeneity responsible for the endogeneity selection bias to include as additional explanatory variables in the primary equation these are obtained through a decomposition of the reduced form residuals the panel nature of the data allows adjustment and testing for two forms of endogeneity and or sample selection bias furthermore it incorporates roles for dynamics and state dependence in the reduced form finally we provide an empirical illustration which features our procedure and highlights the ability to test several of the underlying assumptions

conditional moment models under semi strong identification we consider conditional moment models under semi strong identification identification strength is directly defined through the conditional moments that flatten as the sample size increases our new minimum distance estimator is consistent asymptotically normal robust to semi strong identification and does not rely on the choice of a user chosen parameter such as the number of instruments or some smoothing parameter heteroskedasticity robust inference is possible through wald testing without prior knowledge of the identification pattern simulations show that our estimator is competitive with alternative estimators based on many instruments being well centered with better coverage rates for confidence intervals

estimation of marginal effects in semiparametric selection models with binary outcomes this paper addresses the estimation of a semiparametric sample selection index model where both the selection rule and the outcome variable are binary since the marginal effects are often of primary interest and are difficult to recover in a semiparametric setting we focus on developing an estimator for the marginal effects this marginal effect estimator uses only observations where the selection probability is above a certain threshold a key innovation is that this high probability set is adaptive to the data we establish the large sample properties of the marginal effect estimator as well as those for an index estimator upon which it depends monte carlo studies show that these estimators perform well in finite samples

speeding the pollard and elliptic curve methods of factorization since 1974 several algorithms have been developed that attempt to factor a large number n by doing extensive computations module n and occasionally taking gcds with n these began with pollard s p 1 and monte carlo methods more recently williams published a p 1 method and lenstra discovered an elliptic curve method ecm we present ways to speed all of these one improvement uses two tables during the second phases of p 1 and ecm looking for a match polynomial preconditioning lets us search a fixed table of size n with n 2 o n multiplications a parametrization of elliptic curves lets step 1 of ecm compute the x coordinate of np from that of p in about 9 3 1og2 n multiplications for arbitrary p

estimation of quantity games in the presence of indivisibilities and heterogeneous firms abstract this paper presents a theoretical framework that allows estimation of game theoretic models of quantity competition including a non trivial class of differentiated product quantity games the simplest examples of quantity games are entry games where the strategy each firm i makes is discrete s i s i 0 1 do not enter enter i consider a general class of games where strategy sets are chains which includes the situation where they are a finite set of integers s i 0 1 m in addition i assume that profits of each firm can be written as a function of the firm s own strategy s i and a possibly parametric index of market output q s i s i 1 so that i s i s i i s i q s i s i 1 2 where 1 2 the main theoretical result in the paper establishes easily verifiable conditions under which the index of market output q s i s i 1 is uniquely determined within the set of nash equilibria of the game the model s parameters may then be estimated by comparing the predicted index of market output in a cross section of markets the paper provides both a generalization and an extension of the theoretical results developed by bresnahan and reiss 1991 empirical models of discrete games journal of econometrics 48 57 82 and berry 1992 estimation of a model of entry in the airline industry econometrica 60 4 887 917 which allowed estimation of the homogeneous products entry game and where the index of market output was the number of active firms q s i s i i 1 n s i with s i 0 1 i illustrate one member of the class of models that these results allow us to estimate by developing a model of discrete quantity competition using count data from the supermarket industry the discrete quantity game is the game theoretic analogue to ordered ldv models such as the ordered probit model

a method of simulated moments for estimation of discrete response models without numerical integration this paper proposes a simple modification of a conventional method of moments estimator for a discrete response model replacing response probabilities that require numerical integration with estimators obtained by monte carlo simulation this method of simulated moments msm does not require precise estimates of these probabilities for consistency and asymptotic normality relying instead on the law of large numbers operating across observations to control simulation error and hence can use simulations of practical size the method is useful for models such as high dimensional multinomial probit mnp where computation has restricted applications

simulation estimation of dynamic switching regression and dynamic disequilibrium models some monte carlo results abstract this article considers the estimation of dynamic exogenous switching regression models and dynamic endogenous switching models with autocorrelation in disturbances or latent lagged dependent variables likelihood functions of such models involve high dimensional integrals and a huge number of summations over unobserved regime paths simulated likelihood methods and simulated methods of moments are available these approaches simulate both continuous and discrete latent dependent variables by monte carlo experiments it is found that the performances of various approaches depend crucially on how discrete state variables are simulated the valuable approach is to simulate regime paths with regime probabilities based on the current and past sample information

new regular radix 8 scheme for elliptic curve scalar multiplication without pre computation the recent advances in mobile technologies have increased the demand for high performance parallel computing schemes in this paper we present a new algorithm for evaluating elliptic curve scalar multiplication that can be used on any abelian group we show that the properties of the proposed algorithm enhance parallelism at both the point arithmetic and the field arithmetic levels then we employ this algorithm in proposing a new hardware design for the implementation of an elliptic curve scalar multiplication on a prime extended twisted edwards curve incorporating eight parallel operations we further show that in comparison to the other simple side channel attack protected schemes over prime fields the proposed design of the extended twisted edwards curve is the fastest scalar multiplication scheme reported in the literature

statistical inference in the multinomial multiperiod probit model abstract statistical inference in multinomial multiperiod probit models has been hindered in the past by the high dimensional numerical integrations necessary to form the likelihood functions posterior distributions or moment conditions in these models we describe three alternative estimators implemented using simulation based approaches to inference that circumvent the integration problem posterior means computed using gibbs sampling and data augmentation gibbs simulated maximum likelihood sml estimation using the ghk probability simulator and method of simulated moment msm estimation using ghk we perform a set of monte carlo experiments to compare the sampling distributions of these estimators although all three estimators perform reasonably well some important differences emerge our most important finding is that holding simulation size fixed the relative and absolute performance of the classical methods especially sml gets worse when serial correlation in disturbances is strong in data sets with an ar 1 parameter of 0 50 the rmses for sml and msm based on ghk with 20 draws exceed those of gibbs by 9 and 0 respectively but when the ar 1 parameter is 0 80 the rmses for sml and msm based on 20 draws exceed those of gibbs by 79 and 37 respectively and the number of draws needed to reduce the rmses to within 10 of gibbs are 160 and 80 respectively also the sml estimates of serial correlation parameters exhibit significant downward bias thus while conventional wisdom suggests that 20 draws of ghk is enough to render the bias and noise induced by simulation negligible our results suggest that much larger simulation sizes are needed when serial correlation in disturbances is strong

efficient two step estimation via targeting abstract the standard description of two step extremum estimation amounts to plugging in a first step estimator of nuisance parameters to simplify the optimization problem and then deducing a user friendly but potentially inefficient estimator for the parameters of interest in this paper we consider a more general setting of two step estimation where we do not necessarily have nuisance parameters but rather awkward occurrences of the parameters of interest the efficiency problem associated with two step estimators in this context is more difficult than with standard nuisance parameters as even if the true unknown value of the parameters were plugged in to alleviate the awkward occurrences of the parameters the resulting second step estimator may not be efficient in addition standard approaches to restore efficiency for two step procedures may not work due to a consistency issue to alleviate this potential issue we propose a new computationally simple two step estimation procedure that relies on targeting and penalization to enforce consistency with the second step estimators maintaining asymptotic efficiency we compare this new method with existing iterative methods in the framework of copula models and asset pricing models simulation results illustrate that this new method performs better than existing iterative procedures and is nearly computationally equivalent

simulated moments estimation of markov models of asset prices this paper provides a simulated moments estimator sme of the parameters of dynamic models in which the state vector follows a time homogeneous markov process conditions are provided for both weak and strong consistency as well as asymptotic normality various tradeoffs among the regularity conditions underlying the large sample properties of the sme are discussed in the context of an asset pricing model this paper provides conditions for the consistency and asymptotic normality of a simulated moments estimator sme of the parameters of asset pricing models with time homogeneous markov representations of the stochastic forc ing process sme s for economic models have been proposed by mcfadden 1989 and pakes and pollard 1989 for i i d environments and by lee and ingram 1991 for a time series environment the sme for time series models examined in this paper is as follows the state vector yt that determines asset prices is assumed to follow a time homogeneous markov process whose transi tion function depends on an unknown parameter vector 3 0 asset prices and possibly other relevant data are observed as f yt 0 for some given function f of the underlying state and parameter vector in parallel a simulated state process y is generated analytically or numerically from the economic model and corresponding simulated observations f yj3 13 are taken for a given parameter choice f3 the parameter is chosen so as to match moments that is to minimize the distance between sample moments of the data f y 8030 and those of the simulated series f yt f3 in a sense to be made precise the proposed sme extends the generalized method of moments gmm estimator hansen 1982 to a large class of asset pricing models for which the moment restrictions of interest do not have analytic representations in terms of observable variables and the unknown parameter vector we provide conditions on the transition function of yt and the observation function f under which the sme of 030 is consistent and characterize the normalized asymptotic distribu tion of the estimator for two reasons neither the regularity conditions underly ing hansen s 1982 analysis of gmm estimators for time series models without

residual based procedures for prediction and estimation in a nonlinear simultaneous system this paper proposes the residual based stochastic predictor as an alternative procedure for obtaining forecasts with a static nonlinear econometric model this procedure modifies the usual monte carlo approach to stochastic simulations of the model in that calculated residuals over the sample period are used as proxies for disturbances instead of random draws from some assumed parametric distribution in compar ison with the monte carlo predictor the residual based should be less sensitive to distributional assumptions concerning disturbances in the system it is also less demanding computationally the large sample asymptotic moments of the residual based predictor are derived in this paper and compared with those of the monte carlo predictor both procedures are asymptotically unbiased in terms of asymptotic mean squared prediction error amspe the monte carlo is efficient relative to the residual based when the number of replications in the monte carlo simulations is large relative to sample size this order of relative efficiency is reversed however when replication and sample sizes are similar in any event the amount by which the amspe of either predictor exceeds the lower bound for amspe is small as a percentage of the lower bound amspe when sample and replication sizes are at least of moderate magnitude the paper also discusses the extension of the residual based anld monte carlo procedures to the estimation of higher order moments and cumulative distribution functions of endogenous variables in the system

short memory scalar multiplication for koblitz curves this paper presents a scalar multiplication method for koblitz curves koblitz curves are elliptic curves where the scalar multiplication can be computed in a much faster way than with other curves allowing designs and implementations without arithmetic coprocessor the new method is as fast as the fastest known techniques on koblitz curves but requires much less memory therefore it is of particular interest for environments with low resources our technique is well suited for both hardware and software implementations in hardware we show that a normal basis implementation reduces memory consumption by 85 percent compared to conventional methods but this still has exactly the same computational cost in software thanks to a mixed normal polynomial bases approach our technique allows memory savings up to 70 percent and depending on the instruction set of the cpu can be as fast as the fastest known scalar multiplication methods or can even beat them by a large margin therefore in software and in hardware our scalar multiplication technique offers high performance without sacrifice in view of memory

simulated maximum likelihood estimation of dynamic discrete choice statistical models some monte carlo results this article reports monte carlo results on the simulated maximum likelihood estimation of discrete dynamic panel models introduced by james heckman 1981a the simulated maximum likelihood method is numerically stable even for long panels regression models and polya and renewal models can be better estimated than markov models with a moderate number of simulation draws most of these complex models can be adequately estimated for panels with length up to 30 polya and renewal models can be accurately estimated for panels up to 50 periods estimates of markov models can be sensitive to misspecified initial states but polya renewal and habit persistence models may not

estimating count data models with endogenous switching sample selection and endogenous treatment effects count data regression models are extended to account for endogenous switching and its two most common incarnations viz endogenous dummy variables treatment effects and sample selection fully parametric and partially parametric versions of the extended model are discussed in the parametric version of the model a full information maximum likelihood fiml approach to estimation is introduced under the correct model specification the fiml estimator is efficient but computationally burdensome for the relatively robust partially parametric version of the model i develop a two stage method of moments tsm estimator the tsm estimator is a nonlinear least squares analog to the popular heckman 1976 1979 estimator and therefore avoids the computational requirements of fiml estimation a nonlinear weighted least squares nwls estimator is offered for the fully parametric case the nwls estimator is computationally efficient relative to fiml estimation and statistically efficient relative to the tsm estimator for illustrative purposes the tsm and nwls estimators are applied to the estimation of a regression model with household trip frequency as the dependent variable and a potentially endogenous dummy variable indicating vehicle ownership among the regressors

the limiting distribution of the maximum rank correlation estimator han s maximum rank correlation mrc estimator is shown to be 4 consistent and asymptotically normal the proof rests on a general method for determining the asymptotic distribution of a maximization estimator a simple u statistic decomposition and a uniform bound for degenerate u processes a consistent estimator of the asymptotic covariance matrix is provided along with a result giving the explicit form of this matrix for any model within the scope of the mrc estimator the latter result is applied to the binary choice model and it is found that the mrc estimator does not achieve the semiparametric efficiency bound

estimating the canonical disequilibrium model asymptotic theory and finite sample properties the purpose of this paper is to review the various available estimation techniques for the canonical disequilibrium model full information maximum likelihood fiml pseudo maximum likelihood methods and their simulated counterparts the use of the latter was advocated by laroque salanie 1989 when the model is analytically untractable we provide some new asymptotic results for all of these methods and compare their behaviour on finite samples using monte carlo simulations it appears that fiml performs only slightly better than the second order pml method which in turn performs only slightly better than its simulated counterpart given the ease of implementation of the latter technique these results strongly militate for its use

a general framework of side channel atomicity for elliptic curve scalar multiplication simple power attack spa is a type of side channel attack sca in the literature many spa resistant scalar multiplication algorithms have been proposed but most are inefficient and not interoperable with other coding methods to prevent spa chevallier mames et al proposed a technique called side channel atomicity for pure binary number systems using their method extra costs for preventing spa can be limited even though many researchers have extended this technique to other number systems their algorithms are for specific cases and few provide implementation results in this paper we generalize the atomicity technique to protect nearly all existing fast coding methods number systems our general framework provides security and flexibility while its efficiency is coupled to that of the coding methods moreover we utilize our framework to protect the known fastest scalar multiplications by exploring application on the glv method for gls curves proof of concept programs are written in the c language along with assembly for fast field operations and run on amd athlon x2 245 based hardware

low cost solutions for preventing simple side channel analysis side channel atomicity we introduce simple methods to convert a cryptographic algorithm into an algorithm protected against simple side channel attacks contrary to previously known solutions the proposed techniques are not at the expense of the execution time moreover they are generic and apply to virtually any algorithm in particular we present several novel exponentiation algorithms namely a protected square and multiply algorithm its right to left counterpart and several protected sliding window algorithms we also illustrate our methodology applied to point multiplication on elliptic curves all these algorithms share the common feature that the complexity is globally unchanged compared to the corresponding unprotected implementations

estimation and testing in the random effects probit model abstract this paper examines the finite sample properties of the random effects probit estimator in comparison to the standard probit estimator and the standard probit estimator with a corrected asymptotic covariance matrix the monte carlo experiment considers data generating processes consistent with longitudinal data and also data from sample surveys the probit estimator with corrected asymptotic covariance matrix works surprisingly well over a wide range of parametric configurations and is recommended as long as an estimate of the error correlation is not of high importance

fast multicomputation with asynchronous strategy we propose a new strategy to improve the performance of multicomputations such as computing xa yb in an additive group or a xby in a multiplicative group this new strategy is called asynchronous strategy in each iteration it tries to compute partial results by pairing nearby zero and nonzero bits the new strategy can be applied to any binary code of x and y to further reduce the computational cost however our experiments show that sparse signed digit code can achieve minimum computational cost the algorithms using the asynchronous strategy need only o h extra registers when they try to do computation with bits which are h positions apart

two step estimation of semiparametric censored regression models root n consistent estimators of the regression coecients in the linear censored regression model under conditional quantile restrictions on the error terms were pro posed by powell journal of econometrics 25 1984 303 325 32 1986a 143 155 while those estimators have desirable asymptotic properties under weak regularity conditions simulation studies have shown these estimators to exhibit a small sam ple bias in the opposite direction of the least squares bias for censored data this paper introduces two step estimators for these models which minimize convex objec tive functions and are designed to overcome thisnite sample bias the paper gives regularity conditions under which the proposed two step estimators are consistent and asymptotically normal a monte carlo study compares thenite sample behavior of the proposed methods with their one step counterparts 2001 elsevier science s a all rights reserved

quasi maximum likelihood estimation for conditional quantiles in this paper we derive the asymptotic distribution of a new class of quasi maximum likelihood estimators qmle based on a tick exponential family of densities we show that the tick exponential assumption is a necessary and sufficient condition for a qmle to be consistent for the parameters of a correctly specified model of a given conditional quantile hence the role of this family of densities in the conditional quantile estimation is analog to the role of the linear exponential family in the conditional mean estimation the tick exponential qmles are shown to be asymptotically normal with an asymptotic covariance matrix that has a novel form not seen in earlier work and which accounts for possible model misspecification for practical purposes we show that the maximization of the tick exponential quasi log likelihood can conveniently be carried out by using standard gradient based optimization techniques more importantly we provide a consistent estimator for the asymptotic covariance matrix based on the scores of the log likelihood which allows us to compute the conditional quantile confidence intervals

analyzing the galbraith lin scott point multiplication method for elliptic curves over binary fields galbraith lin and scott recently constructed efficiently computable endomorphisms for a large family of elliptic curves defined over ifq2 and showed in the case where q is a prime that the gallant lambert vanstone point multiplication method for these curves is significantly faster than point multiplication for general elliptic curves over prime fields in this paper we investigate the potential benefits of using galbraith lin scott elliptic curves in the case where q is a power of 2 the analysis differs from the q prime case because of several factors including the availability of the point halving strategy for elliptic curves over binary fields our analysis and implementations show that galbraith lin scott point multiplication method offers significant acceleration for curves over binary fields in both doubling and halving based approaches experimentally the acceleration surpasses that reported for prime fields for the platform in common a somewhat counterintuitive result given the relative costs of point addition and doubling in each case

using follow up data to avoid omitted variable bias an application to cardiovascular epidemiology omitted variable bias is discussed in the context of linear models it is shown that the effect of omitted variables can be controlled in linear models for metric dependent variables by using data from follow up studies two different models for analysing such data are proposed in the first model the omitted variables are assumed to be uncorrelated with the explanatory variables in the model and to be constant over time these assumptions lead to a special structure of the covariance matrix of the errors over time efficient estimation of the parameters in the linear model has to take this special covariance matrix of the errors into account by using appropriate generalized least squares or maximum likelihood methods in the second model the omitted variables are assumed to be time constant additionally they are allowed to be correlated with the explanatory variables that is these variables are omitted confounders in the usual epidemiological sense it is shown that even in this case the parameters of the linear model can be estimated consistently with ordinary least squares if a follow up study is available the differences between the parameter estimates under the first and the second model may be used to construct a hausman test for misspecification the models the estimation methods and the hausman test are illustrated by the example that explores the determinants of serum cholesterol in german adoloscents of both sexes