robust gmm tests for structural breaks we propose a class of new robust generalized method of moments gmm tests for endogenous structural breaks the tests are based on supremum average and exponential functionals derived from robust gmm estimators with bounded influence function we study the theoretical local robustness properties of the new tests and show that they imply a uniformly bounded asymptotic sensitivity of size and power under general local deviations from a reference model we then analyze the finite sample performance of the new robust tests via monte carlo simulations and compare it with that of classical gmm tests for structural breaks in large samples we find that the performance of classical asymptotic gmm tests can be quite unstable under slight departures from some given reference distribution in particular the loss in power can be substantial in some models robust asymptotic tests for structural breaks yield important power improvements both in exactly identified and overidentified model settings in small samples bootstrapped versions of the classical and the robust gmm tests provide accurate and stable empirical levels also for quite small sample sizes however bootstrapped robust gmm tests are found to provide again a higher finite sample efficiency

cooling schedules for optimal annealing a monte carlo optimization technique called simulated annealing is a descent algorithm modified by random ascent moves in order to escape local minima which are not global minima the level of randomization is determined by a control parameter t called temperature which tends to zero according to a deterministic cooling schedule we give a simple necessary and sufficient condition on the cooling schedule for the algorithm state to converge in probability to the set of globally minimum cost states in the special case that the cooling schedule has parametric form tt c log1 t the condition for convergence is that c be greater than or equal to the depth suitably defined of the deepest local minimum which is not a global minimum state

testing dependence among serially correlated multicategory variables the contingency table literature on tests for dependence among discrete multicategory variables is extensive standard tests assume however that draws are independent and only limited results exist on the effect of serial dependency a problem that is important in areas such as economics finance medical trials and meteorology this article proposes new tests of independence based on canonical correlations from dynamically augmented reduced rank regressions the tests allow for an arbitrary number of categories as well as multiway tables of arbitrary dimension and are robust in the presence of serial dependencies that take the form of finite order markov processes for three way or higher order tables we propose new tests of joint and marginal independence monte carlo experiments show that the proposed tests have good finite sample properties an empirical application to microeconomic survey data on firms forecasts of changes to their production and prices demonstrates the importance of correcting fo

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

1 norm based regularization scheme for system identification of structures with discontinuous system parameters this paper presents a new class of regularization functions and the associated regularization scheme for structural system identification in particular 1 norm regularization functions are investigated to overcome the smearing effect of 2 norm regularization functions for the identification of discontinuous system parameters of structures the truncated singular value decomposition is employed to filter out noise polluted solution components and to impose the 1 norm regularization function on si the bilinear fitting method is proposed for selecting an optimal truncation number of the truncated singular value decomposition the validity of the proposed method is demonstrated through the identification of an inclusion in a square plate and damaged members in a two span truss copyright 2006 john wiley sons ltd

confidence sets for the date of a single break in linear time series regressions this paper considers the problem of constructing confidence sets for the date of a single break in a linear time series regression we establish analytically and by small sample simulation that the current standard method in econometrics for constructing such confidence intervals has a coverage rate far below nominal levels when breaks are of moderate magnitude given that breaks of moderate magnitude are a theoretically and empirically relevant phenomenon we proceed to develop an appropriate alternative we suggest constructing confidence sets by inverting a sequence of tests each of the tests maintains a specific break date under the null hypothesis and rejects when a break occurs elsewhere by inverting a certain variant of a locally best invariant test we ensure that the asymptotic critical value does not depend on the maintained break date a valid confidence set can hence be obtained by assessing which of the sequence of test statistics exceeds a single number

convergence in probability of compressed annealing we consider combinatorial optimization problems for which the formation of a neighborhood structure of feasible solutions is impeded by a set of constraints neighborhoods are recovered by relaxing the complicating constraints into the objective function within a penalty term we examine a heuristic called compressed annealing that integrates a variable penalty multiplier approach within the framework of simulated annealing we refer to the value of the penalty multiplier as pressure we analyze the behavior of compressed annealing by exploring the interaction between temperature which controls the ability of compressed annealing to climb hills and pressure which controls the height of the hills we develop a necessary and sufficient condition on the joint cooling and compression schedules for compressed annealing to converge in probability to the set of global minima our work generalizes the results of hajek 1988 in the sense that when there are no relaxed constraints our results reduce to his

estimation and inference in unstable nonlinear least squares models there is compelling evidence that many macroeconomic and financial variables are not generated by linear models this evidence is based on testing linearity against either smooth nonlinearity or piece wise linearity but there is no framework that encompasses both this paper provides an econometric framework that allows for both breaks and smooth nonlinearity in between breaks we estimate the unknown break dates simultaneously with other parameters via nonlinear least squares using new central limit results for nonlinear processes we provide inference methods on break dates and parameter estimates and several instability tests we illustrate our methods via simulated and empirical smooth transition models with breaks

tests for parameter instability and structural change with unknown change point this paper considers tests for parameter instability and structural change with unknown change point the results apply to a wide class of parametric models that are suitable for estimation by generalized method of moments procedures the asymptotic distributions of the test statistics considered here are nonstandard because the change point parameter only appears under the alternative hypothesis and not under the null the tests considered here are shown to have nontrivial asymptotic local power against all alternatives for which the parameters are nonconstant the tests are found to perform quite well in a monte carlo experiment reported elsewhere copyright 1993 by the econometric society

nonstationary markov chains and convergence of the annealing algorithm we study the asymptotic behavior as timet of certain nonstationary markov chains and prove the convergence of the annealing algorithm in monte carlo simulations we find that in the limitt a nonstationary markov chain may exhibit phase transitions nonstationary markov chains in general and the annealing algorithm in particular lead to biased estimators for the expectation values of the process we compute the leading terms in the bias and the variance of the sample means estimator we find that the annealing algorithm converges if the temperaturet t goes to zero no faster thanc log t t0 ast with a computable constantc andt0 the initial time the bias and the variance of the sample means estimator in the annealing algorithm go to zero likeo t 1 e for some 0 e 1 with e 0 only in very special circumstances our results concerning the convergence of the annealing algorithm and the rate of convergence to zero of the bias and the variance of the sample means estimator provide a rigorous procedure for choosing the optimal annealing schedule this optimal choice reflects the competition between two physical effects a the adiabatic effect whereby if the temperature is loweredtoo abruptly the system may end up not in a ground state but in a nearby metastable state and b the super cooling effect whereby if the temperature is loweredtoo slowly the system will indeed approach the ground state s but may do so extremely slowly

the hausman test and weak instruments we consider the following problem there is a structural equation of interest that contains an explanatory variable that theory predicts is endogenous there are one or more instrumental variables that credibly are exogenous with regard to this structural equation but which have limited explanatory power for the endogenous variable further there is one or more potentially strong instruments which has much more explanatory power but which may not be exogenous hausman 1978 provided a test for the exogeneity of the second instrument when none of the instruments are weak here we focus on how the standard hausman test does in the presence of weak instruments using the staiger stock asymptotics it is natural to conjecture that the standard version of the hausman test would be invalid in the weak instrument case which we confirm however we provide a version of the hausman test that is valid even in the presence of weak iv and illustrate how to implement the test in the presence of heteroskedasticity we show that the situation we analyze occurs in several important economic examples our monte carlo experiments show that our procedure works relatively well in finite samples we should note that our test is not consistent although we believe that it is impossible to construct a consistent test with weak instruments

simulated annealing via sobolev inequalities we use sobolev inequalities to study the simulated annealing algorithm this approach takes advantage of the local time reversibility of the process and yields the optimal freezing schedule as well as quantitative information about the rate at which the process is tending to its ground state

stochastic relaxation gibbs distributions and the bayesian restoration of images we make an analogy between images and statistical mechanics systems pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice like physical system the assignment of an energy function in the physical system determines its gibbs distribution because of the gibbs distribution markov random field mrf equivalence this assignment also determines an mrf image model the energy function is a more convenient and natural mechanism for embodying picture attributes than are the local characteristics of the mrf for a range of degradation mechanisms including blurring nonlinear deformations and multiplicative or additive noise the posterior distribution is an mrf with a structure akin to the image model by the analogy the posterior distribution defines another imaginary physical system gradual temperature reduction in the physical system isolates low energy states annealing or what is the same thing the most probable states under the gibbs distribution the analogous operation under the posterior distribution yields the maximum a posteriori map estimate of the image given the degraded observations the result is a highly parallel relaxation algorithm for map estimation we establish convergence properties of the algorithm and we experiment with some simple pictures for which good restorations are obtained at low signal to noise ratios

dynamic system change detection using a modification of the transfer entropy recent work has validated the use of transfer entropy for exploring coupling in structural dynamics transfer entropy is a measure of information flow between markov processes using transitional probabilities one can get a quantification of the degree of influence one process has on the dynamics of another previous research included a time lag to assess information exchanges at various time scales without resulting in tremendously burdensome computations however it is more informative to view the transfer entropy using two separate time lags to assess transitional probabilities between both processes this work introduces a second time delay into a linearized version of the transfer entropy to produce a transfer entropy surface this surface allows for a fuller assessment of dynamical coupling between measurements analytical and low order correlation based measures of the transfer entropy were formed and compared to common modal analysis methods to assess their ability to detect linear dynamical changes in structures investigations were conducted on a computational linear oscillator and a simple experimental system the transfer entropy is a much better indicator of dynamical changes than the modal techniques for the structural systems studied the plane of greatest sensitivity in the time delayed transfer entropy surface is along the diagonal and not along one coordinate as was utilized in previous work

optimal tests when a nuisance parameter is present only under the alternative this paper derives asymptotically optimal tests for testing problems in which a nuisance parameter exists under the alternative hypothesis but not under the null for example the results apply to tests of structural change with unknown changepoint the testing problem considered is nonstandard and the classical asymptotic optimality results for the lagrange multiplier wald and likelihood ratio do not apply a weighted average power criterion is used here to generate optimal tests this criterion is similar to that used by a wald 1943 to obtain the classical asymptotic optimality properties of wald tests in regular testing problems copyright 1994 by the econometric society

does modeling a structural break improve forecast accuracy mean square forecast error loss implies a bias variance trade off that suggests that structural breaks of small magnitude should be ignored in this paper we provide a test to determine whether modeling a structural break improves forecast accuracy the test is near optimal even when the date of a local to zero break is not consistently estimable the results extend to forecast combinations that weight the post break sample and the full sample forecasts by our test statistic in a large number of macroeconomic time series we find that structural breaks that are relevant for forecasting occur much less frequently than existing tests indicate

inference and prediction in a multiple structural break model this paper develops a new bayesian approach to structural break modeling the focuses of the approach are the modeling of in sample structural breaks and forecasting time series allowing out of sample breaks the model has several desirable features first the number of regimes is not fixed but is treated as a random variable second the model adopts a hierarchical prior for regime coefficients which allows for the coefficients of one regime to contain information about coefficients of other regimes third the regime coefficients can be integrated analytically in the posterior density as a consequence the posterior simulator is fast and reliable an application to us real gdp quarterly growth rates links groups of regimes to specific historical periods and provides forecasts of future growth rates

testing for inequality constraints in singular models by trimming or winsorizing the variance matrix there are many applications in which a statistic follows at least asymptotically a normal distribution with a singular or nearly singular variance matrix a classic example occurs in linear regre

mosum tests for parameter constancy summary in this paper tests for structural change based on moving sums mosums of recursive and least squares residuals are investigated we obtain and tabulate the asymptotic critical values of the mosum test with recursive residuals and show that the asymptotic critical values of the mosum test with least squares residuals can easily be obtained from already existing tables for the moving estimates test we also show that these mosum tests are consistent and have nontrivial local power against a general class of alternatives our simulations further indicate that the proposed mosum tests can complement other tests when there is a single structural change and have power advantage when there are certain double structural changes

end of sample instability tests this paper considers tests for structural instability of short duration such as at the end of the sample the key feature of the testing problem is that the number m of observations in the period of potential change is relatively small possibly as small as one the well known f test of chow 1960 for this problem only applies in a linear regression model with normally distributed iid errors and strictly exogenous regressors even when the total number of observations n m is large we generalize the f test to cover regression models with much more general error processes regressors that are not strictly exogenous and estimation by instrumental variables as well as least squares in addition we extend the f test to nonlinear models estimated by generalized method of moments and maximum likelihood asymptotic critical values that are valid as n with m fixed are provided using a subsampling like method the results apply quite generally to processes that are strictly stationary and ergodic under the null hypothesis of no structural instability

on the problem of modelling for parameter identification in distributed structures structures are often characterized by parameters such as mass and stiffness that are spatially distributed parameter identification of distributed structures is subject to many of the difficulties involved in the modeling problem and the choice of the model can greatly affect the results of the parameter identification process analogously to control spillover in the control of distributed parameter systems identification spillover is shown to exist as well and its effect is to degrade the parameter estimates moreover as in modeling by the rayleigh ritz method it is shown that for a rayleigh ritz type identification algorithm an inclusion principle exists in the identification of distributed parameter systems as well so that the identified natural frequencies approach the actual natural frequencies monotonically from above

on the convergence rate of annealing processes for the class of inhomogeneous markov processes arising from simulated annealing it is shown that lim t to infty p x t i exp u i t t exists and is positive for each state i where t t is the temperature and u i is the energy level at i assuming that min u i 0 the method used is to consider the forward equations associated with such processes

tests for white noise against alternatives with both seasonal and nonseasonal serial correlation summary this paper considers tests for seasonal and nonseasonal serial correlation in time series and in the errors of regression models the problem of testing for white noise against

a genetic algorithm for the multiple choice integer program we present a genetic algorithm for the multiple choice integer program that finds an optimal solution with probability one though it is typically used as a heuristic general constraints are relaxed by a nonlinear penalty function for which the corresponding dual problem has weak and strong duality the relaxed problem is attacked by a genetic algorithm with solution representation special to the multiple choice structure nontraditional reproduction crossover and mutation operations are employed extensive computational tests for dual degenerate problem instances show that suboptimal solutions can be obtained with the genetic algorithm within running times that are shorter than those of the osl optimization routine

testing constancy in varying coefficient models abstract this article proposes a coefficients constancy test in semi varying coefficient models that only needs to estimate the restricted coefficients under the null hypothesis the test statistic resembles the union intersection test after ordering the data according to the varying coefficients explanatory variable this statistic depends on a trimming parameter that can be chosen by a data driven calibration method we propose a bootstrap test is justified under fairly general regularity conditions under more restrictive assumptions the critical values can be tabulated and trimming is unnecessary the finite sample performance is studied by means of monte carlo experiments and a real data application for modeling education returns

convergence of an annealing algorithm the annealing algorithm is a stochastic optimization method which has attracted attention because of its success with certain difficult problems including np hard combinatorial problems such as the travelling salesman steiner trees and others there is an appealing physical analogy for its operation but a more formal model seems desirable in this paper we present such a model and prove that the algorithm converges with probability arbitrarily close to 1 we also show that there are cases where convergence takes exponentially long that is it is no better than a deterministic method we study how the convergence rate is affected by the form of the problem finally we describe a version of the algorithm that terminates in polynomial time and allows a good deal of practical confidence in the solution

optimal weighted average power similar tests for the covariance structure in the linear regression model this paper suggests a procedure for the construction of optimal weighted average power similar tests for the error covariance matrix of a gaussian linear regression model when the alternative model belongs to the exponential family the paper uses a saddlepoint approximation to construct simple test statistics for a large class of problems and overcomes the computational burden of evaluating the complicated integrals arising in the derivation of optimal weighted average power tests extensions to panel data models are considered applications are given to tests for error autocorrelation in the linear regression model and in a panel data framework

parameter identification in distributed systems this paper describes a method for the identification of the parameters entering into the equations of motion of distributed systems because the motion of distributed systems is described in terms of partial differential equations these parameters are in general continous functions of the spatial variables for vibrating systems these parameters ordinarily represent the mass stiffness and damping distributions in this paper these distributions are expanded in terms of finite series of known functions of the spatial variables multiplied by undetermined coefficients it is assumed that the nature of the equations of motion is known and that a limited number of eigenvalues and eigenfunctions is identified in advance use is then made of the least squares method in conjunction with the eigenfunctions orthogonality to compute the undetermined coefficients thus identifying the system distributed parameters a method for the identification of the eigensolution is also presented the procedure for the identification of the eigensolution and of the system parameters is demonstrated via a numerical example

the role of eigenparameter gradients in the detection of perturbations in discrete linear systems abstract continuous mechanical systems can be characterized through analysis of discrete linear models such models can provide approximations for the eigenvalues and eigenvectors collectively eigenparameters although the eigenparameters do not qualify as measures for the state of the system in response to specific loading and boundary conditions they do reflect the identity of the system and this in itself has important applications in particular the eigenparameters can be used to study the sensitivity of a system to perturbations due perhaps to damage incurred by one or more discrete elements these studies can rationalize the choice and weighting of eigenparameters for system identification strategies damage detection algorithms and damage assessment methods to this end this paper develops a set of sensitivity coefficients based on gradients of the eigenparameters sensitivities are normalized with respect to that of the harmonic oscillator and generalized to include the mode vectors through the definition of a figure of merit analytical and numerical examples based on appropriate elements are used to illustrate the utility of the approach

testing for structural change in conditional models in the past decade we have seen the development of a new set of tests for structural change of unknown timing in regression models most notably the supf statistic of andrews 1993 econometrica 61 825 856 the expf and avef statistics of andrewsploberger 1994 econometrica 62 1383 1414 and the statistic of nyblom 1989 journal of american statistical association 84 223 230 the distribution theory used for these tests is primarily asymptotic and has been derived under the maintained assumption that the regressors are stationary this excludes structural change in the marginal distribution of the regressors as a result these tests technically cannot discriminate between structural change in the conditional and marginal distributions this paper attempts to remedy this de ciency by deriving the large sample distributions of the test statistics allowing for structural change in the marginal distribution of the regressors we nd that the asymptotic distributions of the supf expf avef and statistics are not invariant to structural change in the regressors to solve the size problem we introduce a xed regressor bootstrap which achieves the rst order asymptotic distribution and appears to possess reasonable size properties in small samples our bootstrap theory allows for arbitrary structural change in the regressors including structural shifts polynomial trends and exogenous stochastic trends it allows for lagged dependent variables and heteroskedastic error processes 2000 elsevier science s a all rights reserved

shrinkage estimation of common breaks in panel data models via adaptive group fused lasso abstract in this paper we consider estimation and inference of common breaks in panel data models via adaptive group fused lasso we consider two approaches penalized least squares pls for first differenced models without endogenous regressors and penalized gmm pgmm for first differenced models with endogeneity we show that with probability tending to one both methods can correctly determine the unknown number of breaks and estimate the common break dates consistently we establish the asymptotic distributions of the lasso estimators of the regression coefficients and their post lasso versions we also propose and validate a data driven method to determine the tuning parameter used in the lasso procedure monte carlo simulations demonstrate that both the pls and pgmm estimation methods work well in finite samples we apply our pgmm method to study the effect of foreign direct investment fdi on economic growth using a panel of 88 countries and regions from 1973 to 2012 and find multiple breaks in the model

testing for structural stability in the whole sample the paper examines a lagrange multiplier type test for the constancy of the parameter in general models with dependent data without imposing any artificial choice of the possible location of the break in order to prove the asymptotic behaviour of the test we extend a strong approximation result for partial sums of a sequence of random variables we also present a monte carlo experiment to examine the finite sample performance of the test and how it compares with tests which assume some knowledge of the possible location of the break

on the convergence and applications of generalized simulated annealing the convergence of the generalized simulated annealing with time inhomogeneous communication cost functions is discussed this study is based on the use of log sobolev inequalities and semigroup techniques in the spirit of a previous article by one of the authors we also propose a natural test set approach to study the global minima of the virtual energy the second part of the paper is devoted to the application of these results we propose two general markovian models of genetic algorithms and we give a simple proof of the convergence toward the global minima of the fitness function finally we introduce a stochastic algorithm that converges to the set of the global minima of a given mean cost optimization problem

predictive tests for structural change with unknown breakpoint abstract this paper considers predictive tests for structural change in models estimated via generalized method of moments our analysis extends earlier work by ghysels and hall 1990a by allowing for the instability to occur at an unknown point in the sample we analyse various statistics based on continuous mappings of the sequence of predictive test calculated for a set of possible breakpoints in the sample the limiting distribution of these statistics is derived under both the null hypothesis and local alternatives percentiles are reported for the distribution under the null a sideproduct of our analysis is that we can illuminate the power properties of the predictive test and also compare its properties to those of the wald lr and lm tests for parameter variation we study those power properties both via local asymptotic analysis and monte carlo