regression based specification tests for the multinomial logit model abstract diagnostic tests for omitted variables or functional misspecification in the multinomial logit mnl model can be performed conveniently by testing the significance of auxiliary regressions of residuals on included and excluded variables in particular lagrange multiplier lm and hausman mcfadden hm tests of the independence from irrelevant alternatives iia property of the mnl model can be carried out by the regression method using suitably defined excluded variables using this method it is straightforward to test jointly against several sources of misspecification and to diagnose the structure of deviations from the mnl model

a method for globally minimizing concave functions over convex sets a method is described for globally minimizing concave functions over convex sets whose defining constraints may be nonlinear the algorithm generates linear programs whose solutions minimize the convex envelope of the original function over successively tighter polytopes enclosing the feasible region the algorithm does not involve cuts of the feasible region requires only simplex pivot operations and univariate search computations to be performed allows the objective function to be lower semicontinuous and nonseparable and is guaranteed to converge to the global solution computational aspects of the algorithm are discussed

a hausman test for the presence of market microstructure noise in high frequency data abstract we develop tests that help assess whether a high frequency data sample can be treated as reasonably free of market microstructure noise at a given sampling frequency for the purpose of implementing high frequency volatility and other estimators the tests are based on the hausman principle of comparing two estimators one that is efficient but not robust to the deviation being tested and one that is robust but not as efficient we investigate the asymptotic properties of the test statistic in a general nonparametric setting and compare it with several alternatives that are also developed in the paper empirically we find that improvements in stock market liquidity over the past decade have increased the frequency at which simple uncorrected volatility estimators can be safely employed

distributional specification tests against semiparametric alternatives abstract score or lagrange multiplier versions of a hausman test for distributional specification are presented for limited dependent variable models these tests are based on the first derivatives of semiparametric criterion functions associated with robust estimation of such models and only require the computation of the maximum likelihood estimator various regression forms of the statistic are also presented monte carlo results for the tobit model indicate that such tests may be efficacious in detecting situations in which the maximum likelihood estimator is seriously biased due to incorrect distributional specification

hausman tests for autocorrelation in the presence of lagged dependent variables some further results abstract further results on the augmented regression test are given a generalized augmented regression test a lagrange multiplier test and a new specification error test based upon locally equivalent alternatives are discussed in the context of testing for general order autocorrelation when there are several lagged values of the dependent variable in the regressors

linear precoding via conic optimization for fixed mimo receivers in this paper the problem of designing linear precoders for fixed multiple input multiple output mimo receivers is considered two different design criteria are considered in the first the transmitted power is minimized subject to signal to interference plus noise ratio sinr constraints in the second the worst case sinr is maximized subject to a power constraint it is shown that both problems can be solved using standard conic optimization packages in addition conditions are developed for the optimal precoder for both of these problems and two simple fixed point iterations are proposed to find the solutions that satisfy these conditions the relation to the well known uplink downlink duality in the context of joint transmit beamforming and power control is also explored the proposed precoder design is general and as a special case it solves the transmit rank one beamforming problem simulation results in a multiuser system show that the resulting precoders can significantly outperform existing linear precoders

choice of scale for cubic smoothing spline models in medical applications the determination of the functional form of the relationship between an outcome variable and one or more continuous covariates is an important aspect of the modelling of medical data for correct interpretation of the data it is essential that the functional form be specified at least approximately correctly i show that for given model complexity logarithmic transformation of a covariate can greatly improve the fit of one of the most useful and convenient non parametric regression models the cubic smoothing spline a mathematical rationale for the idea is given i propose a diagnostic for deciding initially whether a log transformation is needed the method is illustrated using several medical data sets no special software other than that used for fitting the spline models is needed

specification pre test estimator abstract this paper is related to the specification test introduced by j hausman a specification test is often used as a first step in an estimation procedure in the case of the linear model the biases and the mean square errors of the classical and of the specification pre test estimators are computed and compared it is shown that the latter is not uniformly better than the former and conversely in the case of a four variables model regions in the parameters space where the specification pre test estimator outdoes the classical one are numerically settled

efficient estimation using panel data in an important recent paper hausman and taylor 1981 hereafter ht considered the instrumental variable estimation of a regression model using panel data when the individual effects may be correlated with a subset of the explanatory variables they provided a simple consistent estimator and an efficient estimator more recently amemiya and macurdy 1986 hereafter am have suggested an alternative estimator which is more efficient than the ht estimator under certain conditions and given stronger assumptions than ht made however the relationship between the ht and am papers is less clear than it might be in part because of notational differences between the two papers in this paper we clarify the relationship between the ht and am estimators and we show that the difference between these estimators lies in the treatment of the time varying explanatory variables which are uncorrelated with the effects ht use each such variable as two instruments means and deviations from means while am use such variables as t 1 instruments as deviations from means and also separately for each of the t available time periods this enables us to make clear the conditions under which the am estimator is more efficient than the ht estimator we also present each estimator in a form which allows it to be calculated using standard instrumental variables two stage least squares software following the am path one step further we then define a third bms estimator which under yet stronger assumptions is more efficient than the am estimator both ht and am use as instruments the deviations from means of the time varying variables which are correlated with the effects a more efficient estimator may be obtained by using separately the t 1 linearly independent values of these deviations from individual means consistency requires that these be legitimate instruments and whether this is so depends on why these time varying variables are correlated with the effects for example if such correlation arises solely because of a time invariant component which is removed in taking deviations from individual means these instruments are legitimate

fuzzy sets of rules for system identification the synthesis of fuzzy systems involves the identification of a structure and its specialization by means of parameter optimization in doing this symbolic approaches which encode the structure information in the form of high level rules allow further manipulation of the system to minimize its complexity and possibly its implementation cost while all parametric methodologies often achieve better approximation performance in this paper we rely on the concept of a fuzzy set of rules to tackle the rule induction problem at an intermediate level an online adaptive algorithm is developed which almost surely learns the extent to which inclusion of a rule in the rule set significantly contributes to the reproduction of the target behavior then the resulting fuzzy set of rules can be defuzzified to give a conventional rule set with similar behavior comparisons with high level and low level methodologies show that this approach retains the most positive features of both

some aspects of testing non nested hypotheses abstract this paper examines some aspects of testing non nested hypotheses an inequality between the cox and atkinson statistics is noted and the necessary and sufficient condition for the atkinson test to be consistent is derived a new test procedure is also outlined the rest of the paper illustrates the various test statistics their properties and relationships for competing linear regression models

analysis and design of fuzzy control systems using dynamic fuzzy global models a method for analysis and design of a fuzzy controller is developed for a class of fuzzy discrete time systems the method is based on a dynamical fuzzy state space model it is shown that the closed loop fuzzy system can be represented by a dynamical interval system and thus the methods of stability analysis in uncertain linear system theory can be used to analyse the global stability of the closed loop fuzzy system some examples are given to illustrate the application of the method

specification testing in panel data with instrumental variables i show that specification tests for correlated fixed effects developed by hausman and taylor extend in an analogous way to panel data sets with endogenous regressors given panel data different sets of instrumental variables can be used to construct the test for a simple class of models the test in many cases is asymptotically more efficient if an incomplete set of instruments is used however in small samples one may do better using the complete set of instruments monte carlo results demonstrate the likely gains for different assumptions about the degree of between and within variance in the data

on the performance of tests by wu and by hausman for detecting the ordinary least squares bias problem abstract we first consider the performance of the wu 1973 hausman 1978 w h specification error test as a test for the existence of ordinary least squares ols bias we discuss power properties of the test under alternative null hypotheses one of which has not previously been considered we next consider how the w h test performs as an indicator of the extent rather than the existence of an ols bias problem since this usage of the test seems common in applied studies finally monte carlo methods are used to evaluate wu s two step estimation procedure involving the w h test as a pretest

power in econometric applications this paper is concerned with the use of power properties of tests in econometric applications inverse power functions are defined these functions are designed to yield summary measures of power that facilitate the interpretation of test results in practice simple approximations are introduced for the inverse power functions of wald likelihood ratio lagrange multiplier and hausman tests these approximations readily convey the general qualitative features of the power of a test examples are provided to illustrate their usefulness in interpreting test results a common problem faced in applied econometrics is that of interpreting the results of a hypothesis test when the test fails to reject the null hypothesis most practitioners realize that just because a test fails to reject a hypothesis one cannot claim to accept it nevertheless it is common for this to be ignored since the practitioner is often in a position where he would like the outcome of the test to provide useful inferences whether or not the test rejects the purpose of this paper is to introduce inverse power ip summary measures that enable the practitioner to avoid such errors and make valid inferences when a test fails to reject the null hypothesis these summary measures are widely applicable easy to use especially in the common case of a test concerning a single restriction and simple to compute when a test rejects the null hypothesis the implication is that the data are inconsistent with each parameter point in the null in the sense that the probabil ity of type i error for each point is small viz a or less correspondingly when a test fails to reject the null hypothesis an analogous statement is needed regarding the error probabilities for points in the alternative hypothesis it is not the case that all points in the alternative are inconsistent with the data in the sense that their probability of type ii error is small a or less it is possible however to determine the region s in the alternative parameter space that is inconsistent with the data in this sense the ip function introduced below evaluated at

testing for autocorrelation in the presence of lagged dependent variables a specification error approach abstract hausman s specification error testing procedure is used to develop serial correlation tests in lagged dependent variable models properties of the tests are discussed and comparisons are made with existing tests a reparameterization of the model using the error structure under the alternative hypothesis results in an augmented regression equation standard tests of exclusionary restrictions on coefficients of the augmented regressors can be regarded as serial correlation tests monte carlo experiments are used to examine small sample properties several of the newtests are shown to perform well in comparison with existing tests

robust inference with gmm estimators the local robustness properties of generalized method of moments gmm estimators and of a broad class of gmm based tests are investigated in a unified framework gmm statistics are shown to have bounded influence if and only if the function defining the orthogonality restrictions imposed on the underlying model is bounded since in many applications this function is unbounded it is useful to have procedures that modify the starting orthogonality conditions in order to obtain a robust version of a gmm estimator or test we show how this can be obtained when a reference model for the data distribution can be assumed we develop a flexible algorithm for constructing a robust gmm rgmm estimator leading to stable gmm test statistics the amount of robustness can be controlled by an appropriate tuning constant we relate by an explicit formula the choice of this constant to the maximal admissible bias on the level or and the power of a gmm test and the amount of contamination that one can reasonably assume given some information on the data finally we illustrate the rgmm methodology with some simulations of an application to rgmm testing for conditional heteroscedasticity in a simple linear autoregressive model in this example we find a significant instability of the size and the power of a classical gmm testing procedure under a non normal conditional error distribution on the other side the rgmm testing procedures can control the size and the power of the test under non standard conditions while maintaining a satisfactory power under an approximatively normal conditional error distribution

limited information estimators and exogeneity tests for simultaneous probit models a two step maximum likelihood procedure is proposed for estimating simultaneous probit models and is compared to alternative limited information estimators conditions under which each estimator attains the cramer rao lower bound are obtained simple tests for exogeneity based on the new two step estimator are proposed and are shown to be asymptotically equivalent to one another and to have the same local asymptotic power as classical tests based on the limited information maximum likelihood estimator finite sample comparisons between the new and alternative estimators are presented based on some monte carlo evidence the performance of the proposed tests for exogeneity is also assessed

truncated chebyshev series approximation of fuzzy systems for control and nonlinear system identification the lack of mathematical models that pertains fuzzy control systems imposes a serious drawback regarding some important tasks such as stability analysis and system identification for that purpose the availability of analytical expressions for the fuzzy systems is very important this paper provides a systematic practical way of approximating fuzzy systems by chebyshev polynomials which depend on a finite number of parameters the proposed methodology is illustrated with two examples a a control problem concerning a tubular reactor which depending on the operating conditions may exhibit multiple steady states and b the problem of identifying a continuous stirred tank reactor which at certain values of structural parameters exhibits stable or unstable steady states or limit cycles

an approach to fuzzy control of nonlinear systems stability and design issues presents a design methodology for stabilization of a class of nonlinear systems first the authors represent a nonlinear plant with a takagi sugeno fuzzy model then a model based fuzzy controller design utilizing the concept of the so called parallel distributed compensation is employed the main idea of the controller design is to derive each control rule so as to compensate each rule of a fuzzy system the design procedure is conceptually simple and natural moreover the stability analysis and control design problems can be reduced to linear matrix inequality lmi problems therefore they can be solved efficiently in practice by convex programming techniques for lmis the design methodology is illustrated by application to the problem of balancing and swing up of an inverted pendulum on a cart

asymptotic relative efficiency of the classical test statistics under misspecification abstract asymptotic properties of the three classical test statistics the likelihood ratio lagrange multiplier efficient score and wald statistics are studied in the case of two types of misspecification the first misspecification is that the alternative used to construct the test is different from the true nonnull model in the second case the alternative hypothesis is correct but some of the nuisance parameters satisfy restrictions which are not taken into account in the test pitman s local alternatives and the concept of asymptotic relative efficiency are used to derive the asymptotic distributions of the test statistics and to examine the effects of the misspecifications the general results of the paper are illustrated by an example on the dynamic specification of a regression model

on the complexity of joint subcarrier and power allocation for multi user ofdma systems consider a multi user orthogonal frequency division multiple access ofdma system where multiple users share multiple discrete subcarriers but at most one user is allowed to transmit power on each subcarrier to adapt fast traffic and channel fluctuations and improve the spectrum efficiency the system should have the ability to dynamically allocate subcarriers and power resources to users assuming perfect channel knowledge two formulations for the joint subcarrier and power allocation problem are considered in this paper the first is to minimize the total transmission power subject to the quality of service constraints and the ofdma constraint and the second is to maximize some system utility function subject to the total transmission power constraint per user and the ofdma constraint in spite of the existence of various heuristics approaches little is known about the computational complexity status of the above problem this paper aims at filling this theoretical gap i e characterizing the complexity of the joint subcarrier and power allocation problem for the multi user ofdma system it is shown in this paper that both formulations of the joint subcarrier and power allocation problem are strongly np hard several subclasses of the problem which can be solved efficiently in polynomial time are also identified these complexity results suggest that there are not polynomial time algorithms that are able to solve the general joint subcarrier and power allocation problem to global optimality unless p np and determining an approximately optimal subcarrier and power allocation strategy is more realistic in practice

on the complexity of approximating a kkt point of quadratic programming we present a potential reduction algorithm to approximate a karush kuhn tucker kkt point of general quadratic programming qp we show that the algorithm is a fully polynomial time approximation scheme and its running time dependency on accuracy 0 1 is o l log l log log l compared to the previously best known result o l 2 furthermore the limit of the kkt point satisfies the second order necessary optimality condition of being a local minimizer 1998 the mathematical programming society inc published by elsevier science b v

statistical inference in non nested econometric models the purpose of this paper is to discuss some procedures that are available for testing non nested or separate hypotheses in the statistics and econometrics literature since many of these techniques may also be exploited in other disciplines it is hoped that an elaboration of the principal theoretical findings may make them more readily accessible to researchers in other disciplines several simple examples are used to illustrate the concepts of nested and non nested hypotheses and within the latter category global and partial non nested hypotheses two alternative methods of testing non nested hypotheses are discussed and contrasted the first of these is cox s modification of the likelihood ratio statistic and the second is atkinson s comprehensive model approach a major emphasis is placed on the role of the cox principle of hypothesis testing which enables a broad range of hypotheses to be tested within the same framework the problem associated with the application of the comprehensive model approach to composite non nested hypotheses is also highlighted roy s union intersection principle is presented as a viable method of dealing with this problem simulation results concerning the finite sample properties of various tests are discussed together with an analysis of some attempts to correct the poor size of the cox and related tests

a diagnostic test for the mixing distribution in a generalised linear mixed model summary we introduce a diagnostic test for the mixing distribution in a generalised linear mixed model the test is based on the difference between the marginal maximum likelihood and conditional maximum likelihood estimators of a subset of the fixed effects in the model we derive the asymptotic variance of this difference and propose a test statistic that has a limiting chi squared distribution under the null hypothesis that the mixing distribution is correctly specified this strategy uses an idea presented by hausman 1978 who considered analogous tests for the linear mixed model an important advantage of the methods outlined here is that the resulting diagnostic test is easily implemented in commercial software we illustrate the method by applying it to data from a clinical trial investigating the effect of hormonal contraceptives in women

bayesian specification analysis and estimation of simultaneous equation models using monte carlo methods bayesian procedures for specification analysis or diagnostic checking of modeling assumptions for structural equations of econometric models are developed and applied using monte carlo numerical methods checks on the validity of identifying restrictions exogeneity assumptions and other specifying assumptions are performed using posterior distributions for discrepancy vectors and functions representing departures from specifying assumptions several mappings or functions of reduced form coefficients are defined and their posterior distributions are computed a restricted reduced form approach is used to compute posterior distributions for structural parameters these procedures are applied in analyses of two econometric models

a unified approach to estimation and orthogonality tests in linear single equation econometric models abstract maximum likelihood estimation is considered for a generalisation of the model of anderson and rubin 1949 in which the exogenous variables in the structural equation may not be included in the reduced form equations classical and specification tests are derived for orthogonality hypotheses a necessary and sufficient condition for their equivalence is presented the classical tests are compared using bahadur s asymptotic relative efficiency criterion it is shown that a generalisation of the durbin wu hausman t 2 statistics is asymptotically bahadur efficient

necessary optimality conditions and exact penalization for non lipschitz nonlinear programs when the objective function is not locally lipschitz constraint qualifications are no longer sufficient for karush kuhn tucker kkt conditions to hold at a local minimizer let alone ensuring an exact penalization in this paper we extend quasi normality and relaxed constant positive linear dependence condition to allow the non lipschitzness of the objective function and show that they are sufficient for kkt conditions to be necessary for optimality moreover we derive exact penalization results for the following two special cases when the non lipschitz term in the objective function is the sum of a composite function of a separable lower semi continuous function with a continuous function and an indicator function of a closed subset we show that a local minimizer of our problem is also a local minimizer of an exact penalization problem under a local error bound condition for a restricted constraint region and a suitable assumption on the outer separable function when the non lipschitz term is the sum of a continuous function and an indicator function of a closed subset we also show that our problem admits an exact penalization under an extended quasi normality involving the coderivative of the continuous function

robust properties of likelihood ratio tests summary the usual asymptotic chi squared distribution for the likelihood ratio test statistic is based on the assumptions that the data come from the parametric model under consideration and that the parameter satisfies the null hypothesis in this paper we examine the distribution of the likelihood ratio statistic when the data do not come from the parametric model but when the nearest member of the parametric family still satisfies the null hypothesis in general the likelihood ratio statistic no longer follows an asymptotic chi squared distribution and an alternative statistic based on the unionintersection approach is proposed

joint power and admission control via linear programming deflation we consider the joint power and admission control problem for a wireless network consisting of multiple interfering links the goal is to support a maximum number of links at their specified signal to interference plus noise ratio sinr targets while using a minimum total transmission power in this work we first reformulate this np hard problem as a sparse l0 minimization problem and then relax it to a linear program furthermore we derive two easy to check necessary conditions for all links in the network to be simultaneously supported at their target sinr levels and use them to iteratively remove strong interfering links deflation an upper bound on the maximum number of supported links is also given numerical simulations show that the proposed approach compares favorably with the existing approaches in terms of the number of supported links the total transmission power and the execution time

building a model based fuzzy controller a new form of fuzzy model based controller is introduced the new controller is based on a conventional predictive controller design but utilises a fuzzy relational model the controller is described and compared with existing fuzzy controllers practical advice is given on generating suitable relational models for control purposes and an appropriate identification algorithm is recommended the controller is tested on a liquid level process and its performance compared to another fuzzy model based controller which has been previously reported

a smoothing sqp framework for a class of composite l_q lq minimization over polyhedron the composite l q 0 q 1 minimization problem over a general polyhedron has received various applications in machine learning wireless communications image restoration signal reconstruction etc this paper aims to provide a theoretical study on this problem first we derive the karush kuhn tucker kkt optimality conditions for local minimizers of the problem second we propose a smoothing sequential quadratic programming framework for solving this problem the framework requires a approximate solution of a convex quadratic program at each iteration finally we analyze the worst case iteration complexity of the framework for returning an epsilon kkt point i e a feasible point that satisfies a perturbed version of the derived kkt optimality conditions to the best of our knowledge the proposed framework is the first one with a worst case iteration complexity guarantee for solving composite l q minimization over a general polyhedron

a simple parametric model selection test abstractwe propose a simple model selection test for choosing among two parametric likelihoods which can be applied in the most general setting without any assumptions on the relation between the candidate models and the true distribution that is both one or neither is allowed to be correctly specified or misspecified they may be nested nonnested strictly nonnested or overlapping unlike in previous testing approaches no pretesting is needed since in each case the same test statistic together with a standard normal critical value can be used the new procedure controls asymptotic size uniformly over a large class of data generating processes we demonstrate its finite sample properties in a monte carlo experiment and its practical relevance in an empirical application comparing keynesian versus new classical macroeconomic models supplementary materials for this article are available online

the mizon richard encompassing test for the cox and aalen additive hazards models summary the cox hazards model cox 1972 journal of the royal statistical society series b 34 187 220 for survival data is routinely used in many applied fields sometimes however with too little emphasis on the fit of the model a useful alternative to the cox model is the aalen additive hazards model aalen 1980 in lecture notes in statistics 2 1 25 that can easily accommodate time changing covariate effects it is of interest to decide which of the two models that are most appropriate to apply in a given application this is a nontrivial problem as these two classes of models are nonnested except only for special cases in this article we explore the mizon richard encompassing test for this particular problem it turns out that it corresponds to fitting of the aalen model to the martingale residuals obtained from the cox regression analysis we also consider a variant of this method which relates to the proportional excess model martinussen and scheike 2002 biometrika 89 283 298 large sample properties of the suggested methods under the two rival models are derived the finite sample properties of the proposed procedures are assessed through a simulation study the methods are further applied to the well known primary biliary cirrhosis data set

stability analysis and design of fuzzy control systems abstract the stability analysis and the design technique of fuzzy control systems using fuzzy block diagrams are discussed first we show the concept of fuzzy blocks and consider the connection problems of fuzzy blocks diagrams we derive some theorems and corollaries with respect to two basic types of connections of fuzzy blocks in order to preserve some properties in a connection of fuzzy blocks continuous piecewise polynomial membership functions are defined secondly a sufficient condition which guarantees the stability of fuzzy systems is obtained in terms of lyapunov s direct method we give an important fact based on this condition thirdly we propose a new design technique of a fuzzy controller the fuzzy block diagrams and the stability analysis are applied to the design problems of a model based fuzzy controller

tests of non nested regression models small sample adjustments and monte carlo evidence abstract it is known that the small sample significance levels of cox type tests of non nested regression models can be much greater than the nominal level adjustments designed to overcome this problem are discussed and two tests are proposed monte carlo evidence on the performance of the tests derived in this paper the davidson mackinnon j test and the fisher mcaleer test is presented the f test applied to the comprehensive model is also included in the simulation experiments

the encompassing principle and its application to testing nonnested hypotheses the encompassing principle is used to develop a testing framework which unifies the literature on non nested testing allowing analysis of the relationship between alternative tests and in particular enabling asymptotic and finite sample equivalences and identities to be established easily when they exist as well as embracing nested hypothesis testing the concept of the implicit null hypothesis of a test is introduced to show that the effective acceptance region for some tests extends beyond the acceptance region corresponding to the null of interest and so such tests can be inconsistent against fixed alternatives closely related to the nominal null and alternative the analysis is illustrated by an application to two non nested linear regression models and it is shown that the conventional f test as well as all the one degree of freedom non nested tests has an encompassing interpretation and that the f test is a complete parametric encompassing test

testing nested or non nested hypotheses this paper presents a test procedure for nested or non nested hypotheses the test statistic is based on the difference between two estimators of the pseudo true value as defined for instance by sawa this statistic is similar to the usual wald statistic in the case of nested hypotheses and it can be replaced by an asymptotically equivalent one deduced from the score function

indirect adaptive fuzzy sliding mode control part i fuzzy switching in this paper by using the concept of sliding mode control design and lyapunov synthesis approach we propose an indirect adaptive fuzzy sliding mode control iafsmc scheme for a class of nonlinear systems in contrast to the existing sliding mode fuzzy control system designs where the sliding mode control law is directly substituted by a fuzzy controller in our approach both the equivalent control term and switching type control term in the sliding mode control law are approximated by fuzzy systems respectively an on line adaptive tuning algorithm for the consequent parameters in the fuzzy rules is also designed the sliding mode based design procedure ensures the stability and robustness of the proposed iafsmc we prove that the proposed adaptive scheme can achieve asymptotically stable tracking of a reference input with a guarantee of the bounded system signals the chattering phenomena in the sliding mode control is attenuated and the steady error is also alleviated simulation studies have shown that the presented adaptive design of fuzzy sliding mode controller performs very well in the presence of an unknown disturbance

testing slope homogeneity in large panels abstract this paper proposes a standardized version of swamy s test of slope homogeneity for panel data models where the cross section dimension n could be large relative to the time series dimension t the proposed test denoted by exploits the cross section dispersion of individual slopes weighted by their relative precision in the case of models with strictly exogenous regressors but with non normally distributed errors the test is shown to have a standard normal distribution as n t j such that n t 2 0 when the errors are normally distributed a mean variance bias adjusted version of the test is shown to be normally distributed irrespective of the relative expansion rates of n and t the test is also applied to stationary dynamic models and shown to be valid asymptotically so long as n t as n t j where 0 using monte carlo experiments it is shown that the test has the correct size and satisfactory power in panels with strictly exogenous regressors for various combinations of n and t similar results are also obtained for dynamic panels but only if the autoregressive coefficient is not too close to unity and so long as t n

on the formulation of empirical models in dynamic econometrics abstract available information is considered as partitioned into the sets past present and future observations other data of competing models and theory knowledge in each case specific concepts are relevant to empirical model formulation e g innovations for past data exogeneity for present encompassing for contending models etc and various properties of such concepts are established viz encompassing is transitive and asymmetric relationships between concepts are developed e g encompassing entails variance dominance in linear models and related to the notion of a progressive research strategy an empirical model illustrates the various criteria model selection by dynamic simulation tracking performance is critically evaluated

joint power and admission control non convex l_ q approximation and an effective polynomial time deflation approach in an interference limited network joint power and admission control jpac aims at supporting a maximum number of links at their specified signal to interference plus noise ratio sinr targets while using minimum total transmission power various convex approximation deflation approaches have been developed for the jpac problem in this paper we propose an effective polynomial time non convex approximation deflation approach for solving the problem the approach is based on the non convex ell q 0 approximation of an equivalent sparse ell 0 reformulation of the jpac problem we show that for any instance of the jpac problem there exists a bar q in 0 1 such that it can be exactly solved by solving its ell q approximation problem with any q in 0 bar q we also show that finding the global solution of the ell q approximation problem is np hard then we propose a potential reduction interior point algorithm which can return an epsilon kkt solution of the np hard ell q approximation problem in polynomial time the returned solution can be used to check the simultaneous supportability of all links in the network and to guide an iterative link removal procedure resulting in the polynomial time non convex approximation deflation approach for the jpac problem numerical simulations show that the proposed approach outperforms the existing convex approximation approaches in terms of the number of supported links and the total transmission power particularly exhibiting a quite good performance in selecting which subset of links to support

generalized method of moments specification testing abstract this paper analyzes the asymptotic power properties of specification tests which are based on a finite set of moment conditions it shows that any such test may fail against general misspecification that causes estimator inconsistency the mutual asymptotic equivalence of maximal degree of freedom tests is shown and the form of optimal tests against specific forms of misspecification is derived applications to testing for exogeneity of a set of instrumental variables are presented

the statistical implications of preliminary specification error testing abstract this paper demonstrates that a preliminary test estimator based on a specification test of the type suggested by wu and hausman is dominated under a mean square error criterion by an instrumental variable estimator results are obtained by considering a numerical evaluation of the asymptotic risk function of the pre test estimator for various levels of the pre test and special circumstances in which the power function of the pre test becomes flat in the direction of the alternative hypothesis

the pseudo true score encompassing test for non nested hypotheses abstract well known encompassing tests are usually difficult to implement because it is difficult to compute the pseudo true value of the quasi maximum likelihood estimator in this paper we propose a more operational encompassing test that does not involve such pseudo true value and is asymptotically equivalent to the wald and score encompassing tests instead the proposed test relies on the pseudo true score which is relatively easy to evaluate this test is not restricted to testing conditional mean specifications and hence extends the applicability of the conditional mean encompassing test of wooldridge j econom 45 1990b 331 the proposed test is particularly useful when data characteristics can be fully incorporated into the likelihood function our simulations confirm that this test compares favorably with the j and ja tests in testing conditional mean specifications and outperforms the cox test in testing conditional variance specifications

exogeneity tests incomplete models weak identification and non gaussian distributions invariance and finite sample distributional theory abstract we study the distribution of durbin wu hausman dwh and revankar hartley rh tests for exogeneity from a finite sample viewpoint under the null and alternative hypotheses we consider linear structural models with possibly non gaussian errors where structural parameters may not be identified and where reduced forms can be incompletely specified or nonparametric on level control we characterize the null distributions of all the test statistics through conditioning and invariance arguments we show that these distributions do not involve nuisance parameters in particular this applies to several test statistics for which no finite sample distributional theory is yet available such as the standard statistic proposed by hausman 1978 the distributions of the test statistics may be non standard so corrections to usual asymptotic critical values are needed but the characterizations are sufficiently explicit to yield finite sample monte carlo tests of the exogeneity hypothesis the procedures so obtained are robust to weak identification missing instruments or misspecified reduced forms and can easily be adapted to allow for parametric non gaussian error distributions we give a general invariance result block triangular invariance for exogeneity test statistics this property yields a convenient exogeneity canonical form and a parsimonious reduction of the parameters on which power depends in the extreme case where no structural parameter is identified the distributions under the alternative hypothesis and the null hypothesis are identical so the power function is flat for all the exogeneity statistics however as soon as identification does not fail completely this phenomenon typically disappears we present simulation experiments which confirm the finite sample theory the theoretical results are illustrated with an empirical example the relation between trade and economic growth

an identification algorithm in fuzzy relational systems abstract the paper presents an effective identification method in fuzzy relational systems we propose an algorithm for constructing models on the basis of fuzzy and nonfuzzy data with the aid of fuzzy discretization and clustering techniques the usefulness of the method provided is demonstrated by means of two numerical examples also a possible way of generating a linguistic decision making algorithm is discussed

on line fuzzy identification using genetic algorithms the design and implementation of an on line fuzzy identification method using genetic algorithms gas is reported in this paper in the proposed algorithm the rule table of a fuzzy system is first divided into several independent and much smaller fuzzy systems which in turn are encoded into separate bit strings for genetic operations a novel ga updating architecture is then proposed to search the optimal rule base of these fuzzy systems at each sample interval the performance of this identification algorithm is evaluated by simulation of a non linear system moreover an experiment on simple behavior learning of a mobile robot is also reported the results indicate an improvement in design cycle and convergence to the optimal rule base within a relatively short period of time

implicit alternatives and the local power of test statistics the local power of test statistics is analyzed by extending the notion of pitman sequences to sequences of data generating processes dgp s that approach the null hypothesis without necessarily satisfying the alternative a space of probability densities is defined and endowed with the structure of an infinite dimensional hilbert manifold which permits a geometrical interpretation of hypothesis testing the three classical test statistics lr wald and lm are shown to tend asymptotically to the same random variable under all sequences of local dgp s the power of these statistics is seen to depend on the null the alternative and the sequence of dgp s in a simple and geometrically intuitive way moreover for any test statistic that is asymptotically chi squared under the null there exists an implicit alternative hypothesis against which that statistic will have highest power and which coincides with the explicit alternative for the classical test statistics

fuzzy on line identification of siso nonlinear systems abstract a new fuzzy on line identification algorithm for a single input single output continuous time nonlinear dynamic system is presented this method combines the conventional on line identification with fuzzy logic system the nonlinear system is approximated by a set of fuzzy rules that describe the local linear dynamic in each subspace formed by fuzzifying the input and output space the continuous time fuzzy input output model is identified on line by using the input and output measurements a fuzzy identification algorithm has been developed and a convergence analysis is carried out simulation studies have demonstrated that this fuzzy on line identifier can match the time varying nonlinear system within 5 accuracy

a convex optimization approach for minimizing the ratio of indefinite quadratic functions over an ellipsoid we consider the nonconvex problem rq of minimizing the ratio of two nonconvex quadratic functions over a possibly degenerate ellipsoid this formulation is motivated by the so called regularized total least squares problem rtls which is a special case of the problem s class we study we prove that under a certain mild assumption on the problem s data problem rq admits an exact semidefinite programming relaxation we then study a simple iterative procedure which is proven to converge superlinearly to a global solution of rq and show that the dependency of the number of iterations on the optimality tolerance varepsilon grows as o sqrt ln varepsilon 1

on a simultaneous equations pre test estimator this paper investigates the finite sample properties of a pre test estimator for the linear regression model when endogenous regressors render the ordinary least squares estimator inconsistent a wu hausman exogeneity test is applied to determine whether two stage least squares is appropriate both analytic and nonparametric approximations to the pre test risk and bias are discussed

modified lagrange multiplier tests for problems with one sided alternatives abstract a modified lagrange multiplier test statistic is proposed which takes explicit account of the one sided nature of the alternative in problems where the null hypothesis specifies that the true value of the parameter vector lies on the boundary of the parameter space computation of this statistic requires only the constrained maximum likelihood estimator conditions for the consistency of tests based on this statistic are examined and it is shown that the distribution of the statistic is not affected of nuisance parameters are allowed to lie on the boundary of the parameter space

a note on specification tests for the multinomial logit model abstract choice probabilities generated by mcfadden s random utility model are both multinomial logit and have the independence of irrelevant alternatives property various authors have suggested specification tests of this property this paper identifies the alternative against which these tests are constructed and obtains classical specification tests it is shown that both the null hypothesis and local asymptotic power of the classical tests is the same as for a proposed hausman test finally the discussion throws some light on the nature of the iia property and on the performance of the logit model in applications where one would expect the iia property to be violated but where the alternative set is fixed

approximate generalized extreme value models of discrete choice abstract estimation of generalized extreme value gev models of discrete choice is hampered by computational complexity and convergence problems however if the model deviates only modestly from standard multinomial logit a first order approximation may be useful this paper presents three tests each based on such an approximation for the null hypothesis of multinomial logit against any particular gev model as an alternative hypothesis one test applies the universal logit concept of mcfadden train and tye the second is the usual lagrange multiplier test and the third generalizes a regression based test developed by mcfadden 1987 all three begin with a logit estimation followed by one further computational step which also produces an approximate estimate of the gev model these estimates as well as the test statistics are asymptotically equivalent under the null hypothesis monte carlo data generated alternatively by logit and by three different gev models provide evidence on the small sample properties of both the test statistics and the approximate estimators these properties are found to be superior in important respects to maximum likelihood estimation of the gev model