asymptotic distributions of quasi maximum likelihood estimators for spatial autoregressive models this paper investigates asymptotic properties of the maximum likelihood estimator and the quasi maximum likelihood estimator for the spatial autoregressive model the rates of convergence of those estimators may depend on some general features of the spatial weights matrix of the model it is important to make the distinction with dif ferent spatial scenarios under the scenario that each unit will be influenced by only a few neighboring units the estimators may have n rate of convergence and be asymp totically normal when each unit can be influenced by many neighbors irregularity of the information matrix may occur and various components of the estimators may have different rates of convergence

relations between auditory functions in impaired hearing relations between auditory functions as expressed by coefficients of correlation were studied for a group of 22 sensorineurally hearing impaired subjects with moderate losses in addition to the audiogram we measured frequency resolution temporal resolution and speech reception in quiet and in noise frequency resolution was derived from masking with comb filtered noise and from the psychophysical tuning curve for both paradigms in simultaneous and in nonsimultaneous masking the critical ratio was also determined temporal resolution was determined with intensity modulated noise and from backward and forward masking all tests were performed at 1000 hz correlations among tests were gathered in a matrix and subjected to a principal components analysis it turned out that tests on frequency resolution form a cluster and are approximately independent of audiometric loss furthermore hearing loss for speech in noise is closely allied to frequency resolution whereas hearing loss for speech in quiet is governed by audiometric loss

recursive estimation in large panel data models theory and practice abstract bai 2009 proposes recursive estimation for panel data models with interactive effects we study the behaviours of this recursive estimator the recursive formula is established that shows the behaviours of recursive estimators depend on the initial estimator the population structure and the iterative steps under some general scenarios we find that the recursive estimator becomes consistent after the first iteration from any initials we also obtain the optimal number of iterative steps under some prescribed conditions the central limit theorem of the recursive estimator is established when the initial estimator is ols various simulations are conducted to support our theoretical findings

value at risk forecasts by a spatiotemporal model in chinese stock market abstract this paper generalizes a recently proposed spatial autoregressive model and introduces a spatiotemporal model for forecasting stock returns we support the view that stock returns are affected not only by the absolute values of factors such as firm size book to market ratio and momentum but also by the relative values of factors like trading volume ranking and market capitalization ranking in each period this article studies a new method for constructing stocks reference groups the method is called quartile method applying the method empirically to the shanghai stock exchange 50 index we compare the daily volatility forecasting performance and the out of sample forecasting performance of value at risk var estimated by different models the empirical results show that the spatiotemporal model performs surprisingly well in terms of capturing spatial dependences among individual stocks and it produces more accurate var forecasts than the other three models introduced in the previous literature moreover the findings indicate that both allowing for serial correlation in the disturbances and using time varying spatial weight matrices can greatly improve the predictive accuracy of a spatial autoregressive model

central limit theorems and uniform laws of large numbers for arrays of random fields over the last decades spatial interaction models have been increasingly used in economics however the development of a sufficiently general asymptotic theory for nonlinear spatial models has been hampered by a lack of relevant central limit theorems clts uniform laws of large numbers ullns and pointwise laws of large numbers llns these limit theorems form the essential building blocks towards developing the asymptotic theory of m estimators including maximum likelihood and generalized method of moments estimators the paper establishes a clt ulln and lln for spatial processes or random fields that should be applicable to a broad range of data processes

effects of spectro temporal modulation changes produced by multi channel compression on intelligibility in a competing speech task these experiments are concerned with the intelligibility of target speech in the presence of a background talker using a noise vocoder stone and moore j acoust soc am 114 1023 1034 2003 showed that single channel fast acting compression degraded intelligibility but slow compression did not stone and moore j acoust soc am 116 2311 2323 2004 showed that intelligibility was lower when fast single channel compression was applied to the target and background after mixing rather than before and suggested that this was partly due to compression after mixing introducing comodulation between the target and background talkers experiment 1 here showed a similar effect for multi channel compression in experiment 2 intelligibility was measured as a function of the speed of multi channel compression applied after mixing for both eight and 12 channel vocoders with one compressor per channel intelligibility decreased as compression speed increased for the eight channel vocoder a compresso

the effect of spatial separation on informational and energetic masking of speech the effect of spatial separation of sources on the masking of a speech signal was investigated for three types of maskers ranging from energetic to informational normal hearing listeners performed a closed set speech identification task in the presence of a masker at various signal to noise ratios stimuli were presented in a quiet sound field the signal was played from 0 azimuth and a masker was played either from the same location or from 90 to the right signals and maskers were derived from sentences that were preprocessed by a modified cochlear implant simulation program that filtered each sentence into 15 frequency bands extracted the envelopes from each band and used these envelopes to modulate pure tones at the center frequencies of the bands in each trial the signal was generated by summing together eight randomly selected frequency bands from the preprocessed signal sentence three maskers were derived from the preprocessed masker sentences 1 different band sentence which was generated by summing together six randomly selected frequency bands out of the seven bands not present in the signal resulting in primarily informational masking 2 different band noise which was generated by convolving the different band sentence with gaussian noise and 3 same band noise which was generated by summing the same eight bands from the preprocessed masker sentence that were used in the signal sentence and convolving the result with gaussian noise resulting in primarily energetic masking results revealed that in the different band sentence masker the effect of spatial separation averaged 18 db at 51 correct while in the different band and same band noise maskers the effect was less than 10 db these results suggest that in these conditions the advantage due to spatial separation of sources is greater for informational masking than for energetic masking

inference and testing breaks in large dynamic panels with strong cross sectional dependence in this paper we provide a new central limit theorem for estimators of the slope papers in large dynamic panel data models where both n and t increase without bound in the presence of possibly strong cross sectional dependence we proceed by providing two related tests for breaks homogeneity in the time dimension the first test is based on the cusum principle the second test is based on a hausman durbin wu approach some of the key features of the tests are that they have nontrivial power when the number of individuals for which the slope parameters may differ is a negligible fraction or when the break happens to be towards the end of the sample and do not suffer from the incidental parameter problem we provide a simple bootstrap algorithm to obtain asymptotic valid critical values for our statistics an important feature of the bootstrap is that there is no need to know the underlying model of the cross sectional dependence a monte carlo simulation analysis sheds some light on the small sample behaviour of the tests and their bootstrap analogues we implement our test to some real economic data

the importance for speech intelligibility of random fluctuations in steady background noise spectrally shaped steady noise is commonly used as a masker of speech the effects of inherent random fluctuations in amplitude of such a noise are typically ignored here the importance of these random fluctuations was assessed by comparing two cases for one speech was mixed with steady speech shaped noise and n channel tone vocoded a process referred to as signal domain mixing sdm this preserved the random fluctuations of the noise for the second the envelope of speech alone was extracted for each vocoder channel and a constant was added corresponding to the root mean square value of the noise envelope for that channel this is referred to as envelope domain mixing edm it removed the random fluctuations of the noise sinusoidally modulated noise and a single talker were also used as backgrounds with both sdm and edm speech intelligibility was measured for n 12 19 and 30 with the target to background ratio fixed at 7 db for sdm performance was best for the speech background and worst for the steady noise for edm this pattern was reversed intelligibility with steady noise was consistently very poor for sdm but near ceiling for edm demonstrating that the random fluctuations in steady noise have a large effect

panel data models with spatially correlated error components abstract in this paper we consider a panel data model with error components that are both spatially and time wise correlated the model blends specifications typically considered in the spatial literature with those considered in the error components literature we introduce generalizations of the generalized moments estimators suggested in kelejian and prucha 1999 a generalized moments estimator for the autoregressive parameter in a spatial model international economic review 40 509 533 for estimating the spatial autoregressive parameter and the variance components of the disturbance process we then use those estimators to define a feasible generalized least squares procedure for the regression parameters we give formal large sample results for the proposed estimators we emphasize that our estimators remain computationally feasible even in large samples

relations between frequency selectivity temporal fine structure processing and speech reception in impaired hearing frequency selectivity temporal fine structure tfs processing and speech reception were assessed for six normal hearing nh listeners ten sensorineurally hearing impaired hi listeners with similar high frequency losses and two listeners with an obscure dysfunction od tfs processing was investigated at low frequencies in regions of normal hearing through measurements of binaural masked detection tone lateralization and monaural frequency modulation fm detection lateralization and fm detection thresholds were measured in quiet and in background noise speech reception thresholds were obtained for full spectrum and lowpass filtered sentences with different interferers both the hi listeners and the od listeners showed poorer performance than the nh listeners in terms of frequency selectivity tfs processing and speech reception while a correlation was observed between the monaural and binaural tfs processing deficits in the hi listeners no relation was found between tfs processing and frequency selectivity the effect of noise on tfs processing was not larger for the hi listeners than for the nh listeners finally tfs processing performance was correlated with speech reception in a two talker background and lateralized noise but not in amplitude modulated noise the results provide constraints for future models of impaired auditory signal processing

suggested formulae for calculating auditory filter bandwidths and excitation patterns recent estimates of auditory filter shape are used to derive a simple formula relating the equivalent rectangular bandwidth erb of the auditory filter to center frequency the value of the auditory filter bandwidth continues to decrease as center frequency decreases below 500 hz a formula is also given relating erb rate to frequency finally a method is described for calculating excitation patterns from filter shapes

estimation of fixed effects panel regression models with separable and nonseparable space time filters abstract this paper considers a quasi maximum likelihood estimation for a linear panel data model with time and individual fixed effects where the disturbances have dynamic and spatial correlations which might be spatially stable or unstable we first consider both separable and nonseparable space time filters for the stable model the separable space time filter is subject to a parametric restriction which results in relative computational simplicity in contrast to the spatial econometrics literature we expose economic restrictions imposed by the separable space time filter model and explore computational tractability of the nonseparable filter model throughout this paper the effect of initial observations is taken into account which results in an exact likelihood function for estimation this is important when the span of time periods is short we then investigate spatial unstable cases where we propose to apply a spatial differencing to all variables in the regression equation as a data transformation which may eliminate unstable or explosive spatial components in order to achieve a robust estimator for estimates of the parameters in both the regression part and the disturbance process they are n t consistent and asymptotically centered normal regardless of whether t is large or not and whether the process is stable or not

fixed effects dynamic spatial panel data models and impulse response analysis abstract real data often have complicated correlations over cross section and time such correlations are of particular interests in empirical studies this paper considers using high order spatial lags and high order time lags to model complicated correlations over cross section and time we propose to use the quasi maximum likelihood qml method to estimate the model we establish the asymptotic theory of the quasi maximum likelihood estimator qmle including the consistency and limiting distribution under large n and large t setup where n denotes the number of individuals and t the number of time periods we investigate the problem of estimating impulse response functions and the associated 1 confidence intervals average direct indirect and total impacts are defined along the same spirits of lesage and pace 2009 under the dynamic spatial panel data setup the estimation and inferential theory for the three impacts are studied model selection issue is also considered monte carlo simulations confirm our theoretical results and show that the qmle after bias correction has good finite sample performance

a test of cross section dependence for a linear dynamic panel model with regressors abstract this paper proposes a new testing procedure for detecting error cross section dependence after estimating a linear dynamic panel data model with regressors using the generalised method of moments gmm the test is valid when the cross sectional dimension of the panel is large relative to the time series dimension importantly our approach allows one to examine whether any error cross section dependence remains after including time dummies or after transforming the data in terms of deviations from time specific averages which will be the case under heterogeneous error cross section dependence finite sample simulation based results suggest that our tests perform well particularly the version based on the blundell r bond s 1998 initial conditions and moment restrictions in dynamic panel data models journal of econometrics 87 115 143 system gmm estimator in addition it is shown that the system gmm estimator based only on partial instruments consisting of the regressors can be a reliable alternative to the standard gmm estimators under heterogeneous error cross section dependence the proposed tests are applied to employment equations using uk firm data and the results show little evidence of heterogeneous error cross section dependence

central auditory mechanisms of perceptual compensation for spectral envelope distortion the spectral envelope is a major determinant of the perceptual identity of many classes of sound including speech when sounds are transmitted from the source to the listener the spectral envelope is invariably and diversely distorted by factors such as room reverberation perceptual compensation for spectral envelope distortion was investigated here carrier sounds were distorted by spectral envelope difference filters whose frequency response is the spectral envelope of one vowel minus the spectral envelope of another the filter i minus q and its inverse were used subjects identified a test sound that followed the carrier the test sound was drawn from an an itch to qtch continuum perceptual compensation produces a phoneme boundary difference between i minus q and its inverse carriers were the phrase the next word is spoken by the same male speaker as the test sounds signal correlated noise derived from this phrase the same phrase spoken by a female speaker male and female versions played backwards and a repeated end point vowel the carrier and test were presented to the same ear to different ears and from different apparent directions by varying interaural time delay the results show that compensation is unlike peripheral phenomena such as adaptation and unlike phonetic perceptual phenomena the evidence favors a central auditory mechanism

qml estimation of spatial dynamic panel data models with endogenous time varying spatial weights matrices in spatial panel data models when a spatial weights matrix is constructed from economic or social distance spatial weights could be endogenous and also time varying this paper presents model specification and proposes qmle estimation of spatial dynamic panel data models with endogenous time varying spatial weights matrices asymptotic properties of the proposed qmle are rigorously established we extend the notion of spatial near epoch dependence to allow time dependence by using spatial time lln for near epoch dependence process and clt for martingale difference sequence we establish the consistency and asymptotic normality of qmle monte carlo experiments show that the proposed estimators have satisfactory finite sample performance

estimation of simultaneous systems of spatially interrelated cross sectional equations in this paper we consider a simultaneous system of spatially interrelated cross sectional equations our speci cation incorporates spatial lags in the endogenous and exogenous variables in modelling the disturbance process we allow for both spatial correlation as well as correlation across equations the data set is taken to be a single cross section of observations the model may be viewed as an extension of the widely used single equation cli3 ord model we suggest computationally simple limited and full information instrumental variable estimators for the parameters of the system and give formal large sample results

estimation of spatial autoregressive panel data models with fixed effects this paper establishes asymptotic properties of quasi maximum likelihood estimators for sar panel data models with fixed effects and sar disturbances a direct approach is to estimate all the parameters including the fixed effects because of the incidental parameter problem some parameter estimators may be inconsistent or their distributions are not properly centered we propose an alternative estimation method based on transformation which yields consistent estimators with properly centered distributions for the model with individual effects only the direct approach does not yield a consistent estimator of the variance parameter unless t is large but the estimators for other common parameters are the same as those of the transformation approach we also consider the estimation of the model with both individual and time effects

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

efficient gmm estimation of spatial dynamic panel data models with fixed effects abstract in this paper we derive the asymptotic properties of gmm estimators for the spatial dynamic panel data model with fixed effects when n is large and t can be large but small relative to n the gmm estimation methods are designed with the fixed individual and time effects eliminated from the model and are computationally tractable even under circumstances where the ml approach would be either infeasible or computationally complicated the ml approach would be infeasible if the spatial weights matrix is not row normalized while the time effects are eliminated and would be computationally intractable if there are multiple spatial weights matrices in the model also consistency of the mle would require t to be large and not small relative to n if the fixed effects are jointly estimated with other parameters of interest the gmm approach can overcome all these difficulties we use exogenous and predetermined variables as instruments for linear moments along with several levels of their neighboring variables and additional quadratic moments we stack up the data and construct the best linear and quadratic moment conditions an alternative approach is to use separate moment conditions for each period which gives rise to many moments estimation we show that these gmm estimators are n t consistent asymptotically normal and can be relatively efficient we compare these approaches on their finite sample performance by monte carlo

testing panel data regression models with spatial error correlation this paper derives several lagrange multiplier lm tests for the panel data regression model with spatial error correlation these tests draw upon two strands of earlier work the first is the lm tests for the spatial error correlation model discussed in anselin spatial econometrics methods and models kluwer academic publishers dordrecht rao s score test in spatial econometrics j statist plann inference 97 2001 113 and anselin et al regional sci urban econom 26 1996 77 and the second is the lm tests for the error component panel data model discussed in breusch and pagan rev econom stud 47 1980 239 and baltagi et al j econometrics 54 1992 95 the idea is to allow for both spatial error correlation as well as random region effects in the panel data regression model and to test for their joint significance additionally this paper derives conditional lm tests which test for random regional effects given the presence of spatial error correlation also spatial error correlation given the presence of random regional effects these conditional lm tests are an alternative to the one directional lm tests that test for random regional effects ignoring the presence of spatial error correlation or the one directional lm tests for spatial error correlation ignoring the presence of random regional effects we argue that these joint and conditional lm tests guard against possible misspecification extensive monte carlo experiments are conducted to study the performance of these lm tests as well as the corresponding likelihood ratio tests

contracting in space an application of spatial statistics to discrete choice models abstract we develop tests for spatial error correlation and methods of estimation in the presence of such correlation for discrete choice models the tests which are based on the notion of a generalized residual are a set of orthogonality conditions that should be satisfied under the null when the restrictions are rejected the full model can be estimated by generalized method of moments these techniques are used to evaluate spatial patterns in retail gasoline contracts we examine whether the spatial configuration is random or whether there is a tendency towards clustering or dispersion of contract types the data consist of all contracts between integrated oil companies and their branded service stations in the city of vancouver six metrics or measures of closeness are examined euclidean distance competition along streets a combination of the first two nearest neighbors along streets nearest neighbors in euclidean distance and neighbors that share a common marked boundary

limit theory for panel data models with cross sectional dependence and sequential exogeneity abstract the paper derives a general central limit theorem clt and asymptotic distributions for sample moments related to panel data models with large n the results allow for the data to be cross sectionally dependent while at the same time allowing the regressors to be only sequentially rather than strictly exogenous the setup is sufficiently general to accommodate situations where cross sectional dependence stems from spatial interactions and or from the presence of common factors the latter leads to the need for random norming the limit theorem for sample moments is derived by showing that the moment conditions can be recast such that a martingale difference array central limit theorem can be applied we prove such a central limit theorem by first extending results for stable convergence in hall and heyde 1980 to non nested martingale arrays relevant for our applications we illustrate our result by establishing a generalized estimation theory for gmm estimators of a fixed effect panel model without imposing i i d or strict exogeneity conditions we also discuss a class of maximum likelihood ml estimators that can be analyzed using our clt

unified m estimation of fixed effects spatial dynamic models with short panels abstract it is well known that quasi maximum likelihood qml estimation of dynamic panel data dpd models with short panels depends on the assumptions on the initial values and a wrong treatment of them will result in inconsistency and serious bias the same issues apply to spatial dpd sdpd models with short panels in this paper a unified m estimation method is proposed for estimating the fixed effects sdpd models containing three major types of spatial effects namely spatial lag spatial error and space time lag the method is free from the specification of the distribution of the initial observations and robust against nonnormality of the errors consistency and asymptotic normality of the proposed m estimator are established a martingale difference representation of the underlying estimating functions is developed which leads to an initial condition free estimate of the variance of the m estimators monte carlo results show that the proposed methods have excellent finite sample performance

specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances this study develops a methodology of inference for a widely used cliff ord type spatial model containing spatial lags in the dependent variable exogenous variables and the disturbance terms while allowing for unknown heteroskedasticity in the innovations we first generalize the gmm estimator suggested in kelejian and prucha 1998 1999 for the spatial autoregressive parameter in the disturbance process we also define iv estimators for the regression parameters of the model and give results concerning the joint asymptotic distribution of those estimators and the gmm estimator much of the theory is kept general to cover a wide range of settings

the effects of age and cochlear hearing loss on temporal fine structure sensitivity frequency selectivity and speech reception in noise temporal fine structure tfs sensitivity frequency selectivity and speech reception in noise were measured for young normal hearing nhy old normal hearing nho and hearing impaired hi subjects two measures of tfs sensitivity were used the tfs lf test interaural phase difference discrimination and the tfs2 test discrimination of harmonic and frequency shifted tones these measures were not significantly correlated with frequency selectivity after partialing out the effect of audiometric threshold suggesting that insensitivity to tfs cannot be wholly explained by a broadening of auditory filters the results of the two tests of tfs sensitivity were significantly but modestly correlated suggesting that performance of the tests may be partly influenced by different factors the nho group performed significantly more poorly than the nhy group for both measures of tfs sensitivity but not frequency selectivity suggesting that tfs sensitivity declines with age in the absence of elevated audiometric thresholds or broadened auditory filters when the effect of mean audiometric threshold was partialed out speech reception thresholds in modulated noise were correlated with tfs2 scores but not measures of frequency selectivity or tfs lf test scores suggesting that a reduction in sensitivity to tfs can partly account for the speech perception difficulties experienced by hearing impaired subjects

gmm and 2sls estimation of mixed regressive spatial autoregressive models the gmm method and the classical 2sls method are considered for the estimation of mixed regressive spatial autoregressive models these methods have computational advantage over the conventional maximum likelihood method the proposed gmm estimators are shown to be consistent and asymptotically normal within certain classes of gmm estimators best ones are derived the proposed gmm estimators improve upon the 2sls estimators and are applicable even if all regressors are irrelevant a best gmm estimator may have the same limiting distribution as the ml estimator with normal disturbances

testing for serial correlation spatial autocorrelation and random effects using panel data abstract this paper considers a spatial panel data regression model with serial correlation on each spatial unit over time as well as spatial dependence between the spatial units at each point in time in addition the model allows for heterogeneity across the spatial units using random effects the paper then derives several lagrange multiplier tests for this panel data regression model including a joint test for serial correlation spatial autocorrelation and random effects these tests draw upon two strands of earlier work the first is the lm tests for the spatial error correlation model discussed in anselin and bera 1998 spatial dependence in linear regression models with an introduction to spatial econometrics in ullah a giles d e a eds handbook of applied economic statistics marcel dekker new york and in the panel data context by baltagi et al 2003 testing panel data regression models with spatial error correlation journal of econometrics 117 123 150 the second is the lm tests for the error component panel data model with serial correlation derived by baltagi and li 1995 testing ar 1 against ma 1 disturbances in an error component model journal of econometrics 68 133 151 hence the joint lm test derived in this paper encompasses those derived in both strands of earlier works in fact in the context of our general model the earlier lm tests become marginal lm tests that ignore either serial correlation over time or spatial error correlation the paper then derives conditional lm and lr tests that do not ignore these correlations and contrast them with their marginal lm and lr counterparts the small sample performance of these tests is investigated using monte carlo experiments as expected ignoring any correlation when it is significant can lead to misleading inference

reconstruction of nonstationary sound fields based on the time domain plane wave superposition method a time domain plane wave superposition method is proposed to reconstruct nonstationary sound fields in this method the sound field is expressed as a superposition of time convolutions between the estimated time wavenumber spectrum of the sound pressure on a virtual source plane and the time domain propagation kernel at each wavenumber by discretizing the time convolutions directly the reconstruction can be carried out iteratively in the time domain thus providing the advantage of continuously reconstructing time dependent pressure signals in the reconstruction process the tikhonov regularization is introduced at each time step to obtain a relevant estimate of the time wavenumber spectrum on the virtual source plane because the double infinite integral of the two dimensional spatial fourier transform is discretized directly in the wavenumber domain in the proposed method it does not need to perform the two dimensional spatial fast fourier transform that is generally used in time domain holography and real time near field acoustic holography and therefore it avoids some errors associated with the two dimensional spatial fast fourier transform in theory and makes possible to use an irregular microphone array the feasibility of the proposed method is demonstrated by numerical simulations and an experiment with two speakers

moment based tests for individual and time effects in panel data models abstract this paper proposes two hausman type tests respectively for individual and time effects in a two way error component regression model by comparing estimators of the variance of the idiosyncratic error at different robust levels they are both robust to the presence of the other effect and the test for the individual effect has a larger asymptotic power than the corresponding anova f test when the effects are correlated with covariates tests jointly for both effects are also discussed monte carlo evidence shows their good size properties and better power properties than competing tests and the application to the crime rate study gives further support

qml estimation of dynamic panel data models with spatial errors abstract we propose quasi maximum likelihood qml estimation of dynamic panel models with spatial errors when the cross sectional dimension n is large and the time dimension t is fixed we consider both the random effects and fixed effects models and prove consistency and derive the limiting distributions of the qml estimators under different assumptions on the initial observations we propose a residual based bootstrap method for estimating the standard errors of the qml estimators monte carlo simulation shows that both the qml estimators and the bootstrap standard errors perform well in finite samples under a correct assumption on initial observations but may perform poorly when this assumption is not met

testing super diagonal structure in high dimensional covariance matrices abstract the covariance matrices are essential quantities in econometric and statistical applications including portfolio allocation asset pricing and factor analysis testing the entire covariance under high dimensionality endures large variability and causes a dilution of the signal to noise ratio and hence a reduction in the power we consider a more powerful test procedure that focuses on testing along the super diagonals of the high dimensional covariance matrix which can infer more accurately on the structure of the covariance we show that the test is powerful in detecting sparse signals and parametric structures in the covariance the properties of the test are demonstrated by theoretical analyses simulation and empirical studies

gmm estimation with cross sectional dependence this paper presents a spatial model of dependence among agents using a metric of economic distance measurements of this economic distance provide cross sectional data with a structure similar to that provided by the time index in time series data generalized method of moments estimators using such dependent data are shown to be consistent and asymptotically normal this paper presents a class of non parametric positive semi definite covariance matrix estimators that allow for general forms of dependence characterized by economic distance these covariance matrix estimators are shown to remain consistent when economic distances are not precisely observed

quasi maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and t are large this paper investigates the asymptotic properties of quasi maximum likelihood estimators for spatial dynamic panel data with xed eects when both the number of individuals n and the number of time periods t are large we consider the case where t is asymptotically large relative to n the case where t is asymptotically proportional to n and the case where n is asymptotically large relative to t in the case where t is asymptotically large relative to n the estimators are p nt consistent and asymptotically normal with the limit distribution centered around 0 when n is asymptotically proportional to t the estimators are p nt consistent and asymptotically normal but the limit distribution is not centered around 0 and when n is large relative to t the estimators are consistent with rate t and have a degenerate limit distribution the estimators of the xed eects are p t consistent and asymptotically normal we also propose a bias correction for our estimators we show that when t grows faster than n 1 3 the correction will asymptotically eliminate the bias and yield a centered con dence interval

kernel estimation of hazard functions when observations have dependent and common covariates abstract we propose a hazard model where dependence between events is achieved by assuming dependence between covariates this model allows for correlated variables specific to observations as well as macro variables which all observations share this setup better fits many economic and financial applications where events are not independent nonparametric estimation of the hazard function is then studied kernel estimators proposed in nielsen and linton 1995 and linton et al 2003 are shown to have similar asymptotic properties compared with the i i d case mixing conditions ensure the asymptotic results follow these results depend on adjustments to bandwidth conditions simulations are conducted which verify the impact of dependence on estimators bandwidth selection accounting for dependence is shown to improve performance in an empirical application trade intensity in high frequency financial data is estimated

effect of enhancement of spectral changes on speech intelligibility and clarity preferences for the hearing impaired most information in speech is carried in spectral changes over time rather than in static spectral shape per se a form of signal processing aimed at enhancing spectral changes over time was developed and evaluated using hearing impaired listeners the signal processing was based on the overlap add method and the degree and type of enhancement could be manipulated via four parameters two experiments were conducted to assess speech intelligibility and clarity preferences three sets of parameter values one corresponding to a control condition two types of masker steady speech spectrum noise and two talker speech and two signal to masker ratios smrs were used for each masker type generally the effects of the processing were small although intelligibility was improved by about 8 percentage points relative to the control condition for one set of parameter values using the steady noise masker at 6 db smr the processed signals were not preferred over those for the control condition except for the steady noise masker at 6 db smr further work is needed to determine whether tailoring the processing to the characteristics of the individual hearing impaired listener is beneficial

a lagrange multiplier test for cross sectional dependence in a fixed effects panel data model abstract it is well known that the standard breusch and pagan 1980 lm test for cross equation correlation in a sur model is not appropriate for testing cross sectional dependence in panel data models when the number of cross sectional units n is large and the number of time periods t is small in fact a scaled version of this lm test was proposed by pesaran 2004 and its finite sample bias was corrected by pesaran et al 2008 this was done in the context of a heterogeneous panel data model this paper derives the asymptotic bias of this scaled version of the lm test in the context of a fixed effects homogeneous panel data model this asymptotic bias is found to be a constant related to n and t which suggests a simple bias corrected lm test for the null hypothesis additionally the paper carries out some monte carlo experiments to compare the finite sample properties of this proposed test with existing tests for cross sectional dependence

large panels with common factors and spatial correlation this paper considers the statistical analysis of large panel data sets where even after condi tioning on common observed eects the cross section units might remain dependently distrib uted this could arise when the cross section units are subject to unobserved common eects and or if there are spill over eects due to spatial or other forms of local dependencies the paper provides an overview of the literature on cross section dependence introduces the con cepts of time speci c weak and strong cross section dependence and shows that the commonly used spatial models are examples of weak cross section dependence it is then established that the common correlated eects cce estimator of panel data model with a multifactor error structure recently advanced by pesaran 2006 continues to provide consistent estimates of the slope coe cient even in the presence of spatial error processes small sample properties of the cce estimator under various patterns of cross section dependence including spatial forms are investigated by monte carlo experiments results show that the cce approach works well in the presence of weak and or strong cross sectionally correlated errors we also explore the role of certain characteristics of spatial processes in determining the performance of cce estimators such as the form and intensity of spatial dependence and the sparseness of the spatial weight

on the asymptotic distribution of the moran i test statistic with applications by far the most popular test for spatial correlation is the one based on moran s 1950 i test statistic despite this the available results in the literature concerning the large sample distribution of this statistic are limited and have been derived under assumptions that do not cover many applications of interest in this paper we 3rst give a general result concerning the large sample distribution of moran i type test statistics we then apply this result to derive the large sample distribution of the moran i test statistic for a variety of important models in order to establishth ese results we also give a new central limit theorem for linear quadratic forms 2001 elsevier science s a all rights reserved jel classi cation c12 c21