robust linear static panel data models using e contamination abstract the paper develops a general bayesian framework for robust linear static panel data models using e contamination a two step approach is employed to derive the conditional type ii maximum likelihood ml ii posterior distribution of the coefficients and individual effects the ml ii posterior means are weighted averages of the bayes estimator under a base prior and the data dependent empirical bayes estimator two stage and three stage hierarchy estimators are developed and their finite sample performance is investigated through a series of monte carlo experiments these include standard random effects as well as mundlak type chamberlain type and hausman taylor type models the simulation results underscore the relatively good performance of the three stage hierarchy estimator within a single theoretical framework our bayesian approach encompasses a variety of specifications while conventional methods require separate estimators for each case

omitted variables in multilevel models statistical methodology for handling omitted variables is presented in a multilevel modeling framework in many nonexperimental studies the analyst may not have access to all requisite variables and this omission may lead to biased estimates of model parameters by exploiting the hierarchical nature of multilevel data a battery of statistical tools are developed to test various forms of model misspecification as well as to obtain estimators that are robust to the presence of omitted variables the methodology allows for tests of omitted effects at single and multiple levels the paper also introduces intermediate level tests these are tests for omitted effects at a single level regardless of the presence of omitted effects at a higher level a simulation study shows not surprisingly that the omission of variables yields bias in both regression coefficients and variance components it also suggests that omitted effects at lower levels may cause more severe bias than at higher levels important factors resulting in bias were found to be the level of an omitted variable its effect size and sample size a real data study illustrates that an omitted variable at one level may yield biased estimators at any level and in this study one cannot obtain reliable estimates for school level variables when omitted child effects exist however robust estimators may provide unbiased estimates for effects of interest even when the efficient estimators fail and the one degree of freedom test helps one to understand where the problem is located it is argued that multilevel data typically contain rich information to deal with omitted variables offering yet another appealing reason for the use of multilevel models in the social sciences

testing a linear dynamic panel data model against nonlinear alternatives abstract the most popular econometric models in the panel data literature are the class of linear panel data models with unobserved individual and or time specific effects the consistency of parameter estimators and the validity of their economic interpretations as marginal effects depend crucially on the correct functional form specification of the linear panel data model in this paper a new class of residual based tests is proposed for checking the validity of dynamic panel data models with both large cross sectional units and time series dimensions the individual and time effects can be fixed or random and panel data can be balanced or unbalanced the tests can detect a wide range of model misspecifications in the conditional mean of a dynamic panel data model including functional form and lag misspecification they check a large number of lags so that they can capture misspecification at any lag order asymptotically no common alternative is assumed thus allowing for heterogeneity in the degrees and directions of functional form misspecification across individuals thanks to the use of panel data with large n and t the proposed nonparametric tests have an asymptotic normal distribution under the null hypothesis without requiring the smoothing parameters to grow with the sample sizes this suggests better nonparametric asymptotic approximation for the panel data than for time series or cross sectional data this is confirmed in a simulation study we apply the new tests to test linear specification of cross country growth equations and found significant nonlinearities in mean for oecd countries growth equation for annual and quintannual panel data

estimation of a fixed effect cobb douglas system using panel data abstract this paper considers the estimation of a model consisting of a cobb douglas production function and the associated first order conditions for cost minimization there is a fixed individual firm effect in each equation and the availability of panel data is assumed the conditional mle is derived and shown to be mle after a within transformation it can be written explicitly as a corrected ols estimator or as an iv estimator in the special case in which the errors in the production function are independent of the errors in the first order conditions the conditional mle does not require input price data

global stability of a generalized epidemic model abstract the competitive exclusion principle is one of the most interesting and important phenomena in both theoretical epidemiology and biology we show that the equilibrium in which only the strain with the maximum basic reproductive number exists is globally asymptotically stable by using an average lyapunov function theorem and some dynamical system theory this result is anticipated by h j bremermann and h r thieme 1989 6 where they showed that the equilibrium is locally stable the global result has not been established previously

brief variable structure control design for uncertain dynamic systems with disturbances in input and output channels in this paper a design scheme of variable structure model reference control using only input and output measurements is presented for linear time invariant systems with uncertain additive and multiplicative dynamics and disturbances existing in the input and output channels by introducing auxiliary signals and normalized signals with memory and an appropriate choice of control parameters the controller guarantees the global stability of the closed loop system and the tracking error reach zero in finite time

a robust direct adaptive controller this paper proposes a new direct adaptive control algorithm which is robust with respect to additive and multiplicative plant unmodeled dynamics the algorithm is designed based on the reduced order plant which is assumed to be minimum phase and of known order and relative degree but is analyzed with respect to the overall plant which due to the unmodeled dynamics may be nonminimum phase and of unknown order and relative degree it is shown that if the unmodeled dynamics are sufficiently small in the low frequency range then the algorithm guarantees boundedness of all signals in the adaptive loop and small residual tracking errors for any bounded initial conditions in the absence of unmodeled dynamics the residual tracking error is shown to be zero

a design scheme of variable structure adaptive control for uncertain dynamic systems a design scheme of variable structure adaptive control for linear time invariant system with uncertain dynamics is proposed both additive and multiplicative unmodeled dynamics are taken into consideration the transfer function of the modeled part of the plant may have unstable zeros and unstable poles a sign following system and logic switchings are introduced into the control system the global stability of the overall system is proved simulation results show the effectiveness of the proposed method

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

pooling an experimental study of alternative testing and estimation procedures in a two way error component model abstract this paper considers a two way error component model with no lagged dependent variable and investigates the performance of various testing and estimation procedures applied to this model by means of monte carlo experiments the following results were found 1 the chow test performed poorly in testing the stability of cross section regressions over time and in testing the stability of time series regression across regions 2 the roy zellner test performed well and is recommended for testing the poolability of the data 3 the hausman specification test employed to test the orthogonality assumption gave a low frequency of type i errors 4 the lagrange multiplier test employed to test for zero variance components did well except in cases where it was badly needed 5 the problem of negative estimates of the variance components was found to be more serious in the two way model than in the one way model however replacing the negative variance estimates by zero did not have a serious effect on the performance of the second round gls estimates of the regression coefficients 6 as in the one way model all the two stage estimation methods performed reasonably well 7 better estimates of the variance components did not necessarily lead to better second round gls estimates of the regression coefficients

panel estimators and the identification of firm specific efficiency levels in parametric semiparametric and nonparametric settings abstract the paper analyzes a number of competing approaches to modeling efficiency in panel studies the specifications considered include the fixed effects stochastic frontier the random effects stochastic frontier the hausman taylor random effects stochastic frontier and the random and fixed effects stochastic frontier with an ar 1 error i have summarized the foundations and properties of estimators that have appeared elsewhere and have described the model assumptions under which each of the estimators have been developed i discuss parametric and nonparametric treatments of time varying efficiency including the battese coelli estimator and linear programming approaches to efficiency measurement monte carlo simulation is used to compare the various estimators and to assess their relative performances under a variety of misspecified settings a brief illustration of the estimators is conducted using u s banking data

a nonparametric test for poolability using panel data this paper derives a nonparametric procedure for testing poolability in a panel data framework this is contrasted with alternative parametric tests which are not robust to functional misspecification this test is illustrated with an earning equation using data from the panel study of income dynamics psid

panel data models with multiple time varying individual effects abstract this paper considers a panel data model with time varying individual effects the data are assumed to contain a large number of cross sectional units repeatedly observed over a fixed number of time periods the model has a feature of the fixed effects model in that the effects are assumed to be correlated with the regressors the unobservable individual effects are assumed to have a factor structure for consistent estimation of the model it is important to estimate the true number of individual effects we propose a generalized methods of moments procedure by which both the number of individual effects and the regression coefficients can be consistently estimated some important identification issues are also discussed our simulation results indicate that the proposed methods produce reliable estimates

maximum likelihood estimation of random effects models abstract iterated gls has a remarkable property when applied to the random effects model in its usual parameterization the values for the parameter that measures relative variance obtained through successive iterations form a monotonic sequence this property provides convenient checks for multiple maxima of the likelihood function and for existence of a local maximum that satisfies the non negativity condition

multilevel modeling with correlated effects when there exist omitted effects measurement error and or simultaneity in multilevel models explanatory variables may be correlated with random components and standard estimation methods do not provide consistent estimates of model parameters this paper introduces estimators that are consistent under such conditions by employing generalized method of moments gmm estimation techniques in multilevel modeling the authors present a series of estimators along a robust to efficient continuum this continuum depends on the assumptions that the analyst makes regarding the extent of the correlated effects it is shown that the gmm approach provides an overarching framework that encompasses well known estimators such as fixed and random effects estimators and also provides more options these gmm estimators can be expressed as instrumental variable iv estimators which enhances their interpretability moreover by exploiting the hierarchical structure of the data the current technique does not require additional variables unlike traditional iv methods further statistical tests are developed to compare the different estimators a simulation study examines the finite sample properties of the estimators and tests and confirms the theoretical order of the estimators with respect to their robustness and efficiency it further shows that not only are regression coefficients biased but variance components may be severely underestimated in the presence of correlated effects empirical standard errors are employed as they are less sensitive to correlated effects when compared to model based standard errors an example using student achievement data shows that gmm estimators can be effectively used in a search for the most efficient among unbiased estimators

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

grouped effects estimators in fixed effects models abstract we consider estimation of nonlinear panel data models with common and individual specific parameters fixed effects estimators are known to suffer from the incidental parameters problem which can lead to large biases in estimates of common parameters pooled estimators which ignore heterogeneity across individuals are also generally inconsistent we assume that individuals in the data are grouped on multiple levels where groups are defined by some observable external classification we consider group effects estimators where individual specific parameters are assumed common across groups at some level we provide conditions under which group effects estimates of common parameters are asymptotically unbiased and normal the conditions suggest a tradeoff between two sources of bias one due to incidental parameters and the other due to misspecification of unobserved heterogeneity

the mathematics of infectious diseases many models for the spread of infectious diseases in populations have been analyzed mathematically and applied to specific diseases threshold theorems involving the basic reproduction number r 0 the contact number sigma and the replacement number r are reviewed for the classic sir epidemic and endemic models similar results with new expressions for r 0 are obtained for mseir and seir endemic models with either continuous age or age groups values of r 0 and sigma are estimated for various diseases including measles in niger and pertussis in the united states previous models with age structure heterogeneity and spatial structure are surveyed

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

econometric models for count data with an application to the patents r d relationship this paper focuses on developing and adapting statistical models of counts nonnegative integers in the context of panel data and using them to analyze the relationship between patents and r d expenditures since a variety of other economic data come in the form of repeated counts of some individual actions or events the methodology should have wide applications the statistical models we develop are applications and generalizations of the poisson distribution two important issues are i given the panel nature of our data how can we allow for separate persistent individual fixed or random effects ii how does one introduce the equivalent of disturbances in the equation into the analysis of poisson and other discrete probability functions the first problem is solved by conditioning on the total sum of outcomes over the observed years while the second problem is solved by introducing an additional source of randomness allowing the poisson parameter to be itself randomly distributed and compounding the two distributions lastly we develop a test statistic for the presence of serial correlation when fixed effects estimators are used in nonlinear conditional models

on a basic model of a two disease epidemic this paper considers a basic model for a spread of two diseases in a population the equilibria of the model are found and their stability is investigated in particular we prove the stability result for a disease free and a one disease steady states bifurcation diagrams are used to analyse the stability of possible branches of equilibria and also they indicate the existence of a co infected equilibrium with both diseases present finally numerical simulations of the model are performed to study the behaviour of the solutions in different regions of the parameter space

grouped data estimation and testing in simple labor supply models abstract inappropriate identifying assumptions and measurement error in hourly wage data may account for the poor performance of some empirical labor supply models this paper discusses an efficient generalization of wald s method of fitting straigth lines that is robust to measurement error imposes mild identifying assumptions and is useful for the estimation of labor supply models with panel data a two stage least squares tsls equivalent of the efficient wald estimator is presented and a tsls overidentification test statistic is shown to be a test statistic for the equality of alternative wald estimates of the same parameter in an emperical example estimated labor supply elasticities range from 0 6 to 0 8 a test for measurement error based on the difference between efficient wald and analysis of covariance estimators is also proposed application of the test indicates that measurement error may be responsible for low or negative estimates of labor supply elasticities

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

a reformulation of the hausman test for regression models with pooled cross section time series data abstract a hausman test has been typically used to determine the consistency of the gls estimator in static models with pooled cross section time series data based on a gmm approach we reformulate the hausman test and find that it incorporates and tests only a limited set of moment restrictions we also consider an alternative gmm statistic incorporating additional restrictions which has power toward additional sources of model misspecification our monte carlo experiments demonstrate that while both the hausman test and the alternative have good power detecting endogenous regressors the alternative dominates if coefficients of regressors are nonstationary

rank estimation of a generalized fixed effects regression model abstract this paper considers estimation of a fixed effects version of the generalized regression model of han 1987 journal of econometrics 35 303 316 the model allows for censoring places no parametric assumptions on the error disturbances and allows the fixed effects to be correlated with the covariates we introduce a class of rank estimators that consistently estimate the coefficients in the generalized fixed effects regression model the maximum score estimator for the binary choice fixed effects model is part of this class like the maximum score estimator the class of rank estimators converge at less than the n rate smoothed versions of these estimators however converge at rates approaching the n rate in a version of the model that allows for truncated data a sufficient condition for consistency of the estimators is that the error disturbances have an increasing hazard function

simultaneous equations with error components abstract in this paper we derive a limited as well as a full information estimator for the structural parameters of a simultaneous equations model with error components under this model the gain in efficiency by performing these estimators rather than the classical two stage and three stage least squares procedures is demonstrated it is shown that the full information estimator will reduce to the limited information estimator when the disturbances of different structural equations are uncorrelated with each other but not necessarily when all structural equations are just identified this is different from the analogous situation in the classical case

direct adaptive tuning of robust controllers with guaranteed stability properties we address the following problem given a fixed regulator that insures closed loop stability for a partially known plant design a mechanism to adaptively tune the controller parameters in order to improve performance our main concern is to insure that global l stability of the overall system is preserved the proposed parameter update law includes a signal normalization 1 and a modification 2 the condition for global l stability relates the margin of stability of the closed loop linear system the speed of adaptation and the size of the residual set for the tracking error

efficient estimation of panel data models with strictly exogenous explanatory variables abstract with panel data exogeneity assumptions imply many more moment conditions than standard estimators use however many of the moment conditions may be redundant in the sense that they do not increase efficiency if so we may establish the standard estimators efficiency we prove efficiency results for gls in a model with unrestricted error covariance matrix and for 3sls in models where regressors and errors are correlated such as the hausman taylor model for models with correlation between regressors and errors and with unrestricted error covariance structure we provide a simple estimator based on a gls generalization of deviations from means

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

testing for individual effects in autoregressive models abstract this note describes a test for the presence of individual effects in dynamic models the test employs restrictions on sample moments implied by the absence of individual effects testing the assumption of unobserved heterogeneity is desirable because estimation methods which control for heterogeneity are typically more complicated and require more data than would be required without individual effects the test is easily implemented as the calculation of statistics follows directly from a linear instrumental variables estimation technique the test is illustrated in the context of estimating a dynamic wage equation using data from the panel study of income dynamics

stochastic production frontiers and panel data a latent variable framework abstract this paper deals with the issue of estimating production frontier and measuring efficiency from a panel data set first it proposes an alternate method for the estimation of a production frontier on a short panel data set the method is based on the so called mean and covariance structure analysis which is closely related to the generalized method of moments one advantage of the method is that it allows us to investigate the presence of correlations between individual effects and exogenous variables without the requirement of some available instruments uncorrelated with the individual effects as in instrumental variable estimation another advantage is that the method is well suited to a panel data set with a short number of periods second the paper considers the question of recovering individual efficiency levels from the estimates obtained from the mean and covariance structure analysis since individual effects are here viewed as latent variables they can be estimated as factor scores i e weighted sums of the observed variables we illustrate the proposed methods with the estimation of a stochastic production frontier on a short panel data of french fruit growers

on the testing of correlated effects with panel data abstract using orthogonal deviations of the variables correlated effects biases are regarded as misspecification biases due to the exclusion of relevant variables in a standard regression model following this approach hausman and chamberlain type tests of correlated effects are obtained as wald tests in an extended model estimated by ols and robust generalisations are suggested alternative estimators which introduce restrictions in the regression of the effects on the explanatory variables are proposed finally the paper extends the results to dynamic models

estimating multi way error components models with unbalanced data structures abstract i develop simple matrix algebra techniques that simplify and unify much of the previous literature on estimating error components models ecms in fact the simple analytic results provided here are useful for analyzing a very broad set of models with complex error structures to illustrate the techniques i develop the algebra for three and four way ecms explicitly in addition i provide monte carlo simulation evidence on the performance of several estimators for the three way ecm and estimate the model using data from a retail market where the three dimensions of data variation are products selling in many locations over time

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

a dynamical model for hiv tb co infection abstract a nonlinear mathematical model is proposed and analyzed to study the dynamics of hiv tb and co infection the basic reproduction number for each of the two diseases hiv and tb is obtained four equilibrium points are found out of which one is disease free equilibrium while the remaining three equilibrium points are such that they are having at least one of the diseases hiv and or tb is present when the basic reproduction number of the two diseases is less than one then the disease free equilibrium point is stable this provides a threshold for the control of diseases when tb free equilibrium point exists then it is locally stable if the basic reproduction number of hiv is more than that of tb a condition for local stability of hiv free equilibrium point is also obtained there exist some situations in which bi stability is possible that is both of the diseases persist the conditions for global stability of these points are also established the conditions for existence of endemic equilibrium point are explored and it was found that it remains unstable whenever the endemic equilibrium point exists the instability of endemic equilibrium point is used to interpret that the co infection is not long lasting

panel data and unobservable individual effects abstract an important purpose in pooling time series and cross section data is to control for individual specific unobservable effects which may be correlated with other explanatory variables e g latent ability in measuring returns to schooling in earnings equations or managerial ability in measuring returns to scale in firm cost functions using instrumental variables and the time invariant characteristics of the latent variable we derive 1 1 a test for the presence of this effect and for the over identifying restriction we use 2 2 necessary and sufficient conditions for identification of all the parameters in the model and 3 3 the asymptotically efficient instrumental variables estimator and conditions under which it differs from the within groups estimator we calculate efficient estimates of a wage equation from the michigan income dynamics data which indicate substantial differences from within groups and balestra nerlove estimates particularly a significantly higher estimate of the returns to schooling

production frontiers with cross sectional and time series variation in efficiency levels abstract in this paper we consider the efficient instrumental variables estimation of a panel data model with heterogeneity in slopes as well as intercepts using a panel of u s airlines we apply our methodology to a frontier production function with cross sectional and temporal variation in levels of technical efficiency our approach allows us to estimate time varying efficiency levels for individual firms without invoking strong distributional assumptions for technical inefficiency or random noise we do so by including in the production function a flexible function of time whose parameterization depends on the firm we also generalize the results of hausman and taylor 1981 to exploit assumptions about the uncorrelatedness of certain exogenous variables with the temporal pattern of the firm s technical inefficiency our empirical analysis of the airline industry over two periods of regulation yields believable evidence on the pattern of changes in efficiency across regulatory environments

incomplete panels a comparative study of alternative estimators for the unbalanced one way error component regression model abstract this paper considers a one way error component regression model with unbalanced data and investigates by means of monte carlo experiments the performance of anova mle and mivque type estimators of the variance components some of the basic findings are the following 1 for the regression coefficients the computationally simple anova methods perform reasonably well when compared with the computationally involved mle and mivque methods 2 mle and mivque perform better than the anova methods in the estimation of the individual variance component especially for severely unbalanced patterns and large variance component ratio however for the remainder variance component there is nothing much to choose among these methods 3 better estimates of the variance components do not necessarily imply better estimates of the regression coefficients 4 making the data balanced by dropping observations worsens the performance of these estimators when compared to those from the entire unbalanced data

panel data models with interactive fixed effects this paper considers large n and large t panel data models with unobservable multiple interactive eects these models are useful for both micro and macro econometric modelings in earnings studies for example workers motivation persistence and diligence combined to influence the earnings in addition to the usual argument of innate ability in macroeconomics the interactive eects represent unobservable common shocks and their heterogeneous responses over cross sections since the interactive eects are allowed to be correlated with the regressors they are treated as fixed eects parameters to be estimated along with the common slope coecients the model is estimated by the least squares method which provides the interactive eect s counterpart of the within estimator we first consider model identification and then derive the rate of convergence and the limiting distribution of the interactive eect s estimator of the common slope coecients the estimator is shown to be p nt consistent this rate is valid even in the presence of correlations and heteroskedasticities in both dimensions a striking contrast with fixed t framework in which serial correlation and heteroskedasticity imply unidentification the asymptotic distribution is not necessarily centered at zero biased corrected estimators are derived we also derive the constrained estimator and its limiting distribution imposing additivity coupled with interactive eects the problem of testing additive versus interactive eects is also studied we also derive identification conditions for models with grand mean time invariant regressors and common regressors it is shown that there exists a set of necessary and sucient identification conditions for those models given identification the rate of convergence and limiting results continue to hold

specification issues and confidence intervals in unilateral price effects analysis this paper contributes to the economics and econometrics literature on unilateral effects analysis it introduces the rotterdam demand system considers the effect and importance of demand restrictions for minimizing the mean squared error of price simulations demonstrates that approximate price changes are often misleading indicators of exact price changes and uses bootstrap techniques to determine confidence intervals and standard errors for simulated price changes allowing determination not only of their economic significance but also of their statistical significance

efficient estimation of models for dynamic panel data abstract in this paper we consider a dynamic model for panel data we show that under standard assumptions there are more moment conditions than are currently exploited in the literature some of these are linear but others are quadratic so that nonlinear gmm is required we also show that exogenous regressors generate a larger number of relevant moment conditions in a dynamic model than they would in a static model

new approach to robust model reference adaptive control for a class of plants motivated by recent advances in designing robust adaptive controllers and in dealing with uncertain dynamical systems a new model reference adaptive control which is robust to a class of unmodelled dynamics and bounded output disturbances in the case of relative degree one is presented the implementation of the controllers includes a switching mechanism which plays a crucially important role in functions of stabilizing as well as tracking it is shown that global stability of the overall system is achieved under no assumption of persistency of excitation and tracking errors will converge to a residual set whose size can be directly related to the size of unmodelled dynamics and of output disturbances explicitly in the ideal case the residual set degenerates to a single null point and convergence can be achieved in finite time without any requirement of persistency of excitation

the unbalanced nested error component regression model abstract this paper considers a nested error component model with unbalanced data and proposes simple analysis of variance anova maximum likelihood mle and minimum norm quadratic unbiased estimators minque type estimators of the variance components these are natural extensions from the biometrics statistics and econometrics literature the performance of these estimators is investigated by means of monte carlo experiments while the mle and minque methods perform the best in estimating the variance components and the standard errors of the regression coefficients the simple anova methods perform just as well in estimating the regression coefficients these estimation methods are also used to investigate the productivity of public capital in private production

nonparametric identification of discrete choice models with lagged dependent variables abstract variation in covariates can be used to nonparametrically identify a discrete choice model with a lagged dependent variable and discrete unobserved heterogeneity kasahara and shimotsu 2009 browning and carro 2014 in some cases the number of support points of the unobserved heterogeneity distribution is restricted only by the number of points of support in the distribution of the covariates this paper provides conditions under which these models can be identified with continuous heterogeneity using continuous variation in the covariates the identification argument is related to that of honore and lewbel 2002 in that it requires a special regressor lewbel 1998 but it does not assume an additively separable latent index identification requires only 3 time periods neither stationarity nor time homogeneity is imposed and the distribution of the initial condition is not restricted apart from conditions required for the special regressor i demonstrate the result through a monte carlo simulation

robust penalized quantile regression estimation for panel data this paper investigates a class of penalized quantile regression estimators for panel data the penalty serves to shrink a vector of individual specific effects toward a common value the degree of this shrinkage is controlled by a tuning parameter it is shown that the class of estimators is asymptotically unbiased and gaussian when the individual effects are drawn from a class of zero median distribution functions the tuning parameter can thus be selected to minimize estimated asymptotic variance monte carlo evidence reveals that the estimator can significantly reduce the variability of the fixed effect version of the estimator without introducing bias

specification tests in simultaneous equations systems abstract this paper makes two contributions a it provides a new derivation of 2sls and 3sls estimators in which they are represented as restricted least squares and restricted generalized least squares respectively b in the convext thus created it provides a perfectly routine and flexible vehicle for carrying out identifiability and specification tests utilizing 2sls or 3sls estimators the tests in question are simple lagrange multiplier tests lmt carried out directly on the prior restrictions in contrast to the current literature which approaches the problem indirectly through tests on the implications of such prior restrictions on the reduced form in addition it provides a limited monte carlo study comparing the performance of the newly created tests to that of the hausman test ht

identification and sequential estimation of panel data models with insufficient exclusion restrictions abstract this paper presents estimators for nonparametric panel data models with additive fixed effects we analyze a model in which the entire regressor vector consisting of time varying as well as time invariant regressors is correlated with the individual fixed effect but there are insufficient exclusion restrictions to permit direct instrumental variables estimation of the model it is shown that when the model satisfies the additive interactive regression air representation of andrews and whang economic theory 6 1990 466 480 a sequential estimator consistently estimates the model the limiting distribution of scalar nonlinear functionals of the model is shown to be standard normal finite sample properties of the estimator are considered in a monte carlo simulation study and used to estimate the returns to education in a cohort of mature men from the national longitudinal survey

assessing cross sectional correlation in panel data abstract consider drawing a sample of n experimental units where each unit is observed over t time periods are the draws independent a test statistic introduced by breusch and pagan 1980 is one measure used to quantify the amount of cross sectional correlation it is effective when t is large relative to n and as shown by breusch and pagan has desirable asymptotic in t properties in this paper we consider the case often encountered in the analysis of panel data where n is large relative to t here the breusch and pagan statistic does not enjoy the same desirable asymptotic in n properties in fact we show that the asymptotic distribution depends on the parent population even under the hypothesis of no cross sectional correlations thus we introduce a distribution free statistic that does not have this drawback asymptotic properties of the new statistic are established when the data are drawn from identical distributions and when the statistic is evaluated using residuals from a complex model both the breusch and pagan statistic and the new statistic are shown to be members of a more general family by considering this generalization finite sample properties of members of the family are easily established further it turns out that a statistic introduced by friedman 1937 is another special case of this family

a new robust mrac using variable structure design for relative degree two plants abstract variable structure design concept has been shown not only useful in dealing with uncertain systems but also useful in establishing controllers in the adaptive context continuing the work this research proposed a new robust model reference adaptive control for single input single output linear plants with relative degree two a class which encompasses a large number of linearized mechanical systems when compared with the conventional mrac schemes the current approach drastically improves the transient behavior and the tracking performance of the closed loop system from several numerical simulations furthermore if the reference input satisfies the persistently exciting condition or if the prior knowledge about the interval in which the true parameter vector lies is available the magnitude of the input force can be considerably reduced into a more realistic level in the presence of mild unmodelled dynamics and bounded output disturbances the proposed scheme possesses similar well behaved properties it can be shown that the tracking error will in fact fall into a residual set whose size can be directly related to the size of unmodelled dynamics and of output disturbances