foreign exchange rates a multiple currency and maturity analysis abstract three dollar exchange rate series the german mark the british pound and the japanese yen are converted to market price revisions by calculating the difference between the price for delivery at a fixed date and the preceding period s price for delivery at the same date these price revisions are likely to meet the stationary stochastic process assumptions required for time series modeling even if the original series are non stationary using error covariance assumptions analogous to a pooled time series of cross sections a multivariate time series model is fitted and tested on 168 observations out of sample forecasting performance is evaluated using both mean squared errors and a non parametric test of direction

efficient computation of the hutchinson metric between digitized images the hutchinson metric is a natural measure of the discrepancy between two images for use in fractal image processing an efficient solution to the problem of computing the hutchinson metric between two arbitrary digitized images is considered the technique proposed here based on the shape of the objects as projected on the digitized screen can be used as an effective way to establish the error between the original and the possibly compressed decoded image to test the performance of our method we apply it to compare pairs of fractal objects as well as to compare real world images with the corresponding reconstructed ones

testing and modeling multivariate threshold models abstract threshold autoregressive models in which the process is piecewise linear in the threshold space have received much attention in recent years in this article i use predictive residuals to construct a test statistic for detecting threshold nonlinearity in a vector time series and propose a procedure for building a multivariate threshold model the thresholds and the model are selected jointly based on the akaike information criterion the finite sample performance of the proposed test is studied by simulation the modeling procedure is then used to study arbitrage in security markets and results in a threshold cointegration between logarithms of future contracts and spot prices of a security after adjusting for the cost of carrying the contracts in this particular application thresholds are determined in part by the transaction costs i also apply the proposed procedure to u s monthly interest rates and two river flow series of iceland

testing for high dimensional white noise using maximum cross correlations we propose a new omnibus test for vector white noise using the maximum absolute autocorrelations and cross correlations of the component series based on an approximation by the l norm of a normal random vector the critical value of the test can be evaluated by bootstrapping from a multivariate normal distribution in contrast to the conventional white noise test the new method is proved to be valid for testing departure from white noise that is not independent and identically distributed we illustrate the accuracy and the power of the proposed test by simulation which also shows that the new test outperforms several commonly used methods including the lagrange multiplier test and the multivariate box pierce portmanteau tests especially when the dimension of the time series is high in relation to the sample size the numerical results also indicate that the performance of the new test can be further enhanced when it is applied to pre transformed data obtained via the time series principal component analysis proposed by j chang b guo and q yao arxiv 1410 2323 the proposed procedures have been implemented in an r package

testing serial correlations in high dimensional time series via extreme value theory abstract this paper proposes a simple test for detecting serial correlations in high dimensional time series the proposed test makes use of the robust properties of spearman s rank correlation and the theory of extreme values asymptotic properties of the test statistics are derived under some minor conditions as both the sample size and dimension go to infinity the test is not sensitive to the underlying distribution of the time series so long as the data are continuously distributed in particular the existence of finite order moments of the underlying distribution is not required and asymptotic critical values of the test statistics are available in closed form in finite samples we correct biases of the sample autocorrelations and conduct simulations to study the performance of the proposed test statistics simulation results show that the proposed test statistics enjoy good properties of size and power in finite samples we apply the proposed test to a 92 dimensional series of asset returns finally a simple r code is available to obtain finite sample critical values of the test statistics if needed

on the use of the general partial autocorrelation function for order determination in arma p q processes abstract we show that the general partial autocorrelation function gpac which has recently been suggested to be used as one of a set of convenient tools for order identification in arma models has unstable behavior when applied to time series of moderate length its use in detecting the order of ma components in real series is very limited and can only be recommended as a means to confirm a pure ar fit to the data

multivariate periodic arma 1 1 processes the main objective of this paper is to investigate the properties of multivariate periodic autoregressive moving average arma l 1 processes such properties include the covariance structure the parameter space and estimation the parameter space of such periodic processes is derived by aggregation for instance monthly models will aggregate to annual models in particular the parameter space and estimation are analyzed for contemporaneous arma carma models it is shown that in general the aggregation of a multivariate periodic arma 1 1 model leads to a stationary multivariate arma 1 1 model however not always a periodic carma 1 1 model aggregates into a stationary carma 1 1 model furthermore it is shown that in estimating the aggregated model parameters the method based on the periodic model and corresponding parameters is superior to that based on the aggregated series

modelling multivariate volatilities via conditionally uncorrelated components summary we propose to model multivariate volatility processes on the basis of the newly defined conditionally uncorrelated components cucs this model represents a parsimonious representation for matrix valued processes it is flexible in the sense that each cuc may be fitted separately with any appropriate univariate volatility model computationally it splits one high dimensional optimization problem into several lower dimensional subproblems consistency for the estimated cucs has been established a bootstrap method is proposed for testing the existence of cucs the methodology proposed is illustrated with both simulated and real data sets

embedded image coding using zerotrees of wavelet coefficients the embedded zerotree wavelet algorithm ezw is a simple yet remarkably effective image compression algorithm having the property that the bits in the bit stream are generated in order of importance yielding a fully embedded code the embedded code represents a sequence of binary decisions that distinguish an image from the null image using an embedded coding algorithm an encoder can terminate the encoding at any point thereby allowing a target rate or target distortion metric to be met exactly also given a bit stream the decoder can cease decoding at any point in the bit stream and still produce exactly the same image that would have been encoded at the bit rate corresponding to the truncated bit stream in addition to producing a fully embedded bit stream the ezw consistently produces compression results that are competitive with virtually all known compression algorithms on standard test images yet this performance is achieved with a technique that requires absolutely no training no pre stored tables or codebooks and requires no prior knowledge of the image source the ezw algorithm is based on four key concepts 1 a discrete wavelet transform or hierarchical subband decomposition 2 prediction of the absence of significant information across scales by exploiting the self similarity inherent in images 3 entropy coded successive approximation quantization and 4 universal lossless data compression which is achieved via adaptive arithmetic coding

generalized dynamic factor models and volatilities consistency rates and prediction intervals abstract volatilities in high dimensional panels of economic time series with a dynamic factor structure on the levels or returns typically also admit a dynamic factor decomposition we consider a two stage dynamic factor model method recovering the common and idiosyncratic components of both levels and log volatilities specifically in a first estimation step we extract the common and idiosyncratic shocks for the levels from which a log volatility proxy is computed in a second step we estimate a dynamic factor model which is equivalent to a multiplicative factor structure for volatilities for the log volatility panel by exploiting this two stage factor approach we build one step ahead conditional prediction intervals for large n t panels of returns those intervals are based on empirical quantiles not on conditional variances they can be either equal or unequal tailed we provide uniform consistency and consistency rates results for the proposed estimators as both n and t tend to infinity we study the finite sample properties of our estimators by means of monte carlo simulations finally we apply our methodology to a panel of asset returns belonging to the s p100 index in order to compute one step ahead conditional prediction intervals for the period 2006 2013 a comparison with the componentwise garch benchmark which does not take advantage of cross sectional information demonstrates the superiority of our approach which is genuinely multivariate and high dimensional nonparametric and model free

adjustment of provisional mortality series the dynamic linear model with structured measurement errors abstract we consider the problem of adjusting provisional time series using a bivariate structural model with correlated measurement errors maximum likelihood estimators and a minimum mean squared error adjustment procedure are derived for a provisional and final series containing common trend and seasonal components the model also includes measurement errors common to both series and errors that are specific to the provisional series we illustrate the technique by using provisional data to forecast ischemic heart disease mortality

simplified conditions for noncausality between vectors in multivariate arma models this article derives necessary and sufficient conditions for noncausality between two vectors of variables in stationary invertible arma processes earlier conditions proposed by boudjellaba dufour and roy 1992a are shown to hold under weaker regularity assumptions and then generalized to cover the important case where the two vectors do not necessarily embody all the variables considered in the analysis the conditions so obtained can be considerably simpler and easier to implement than earlier ones testing of the conditions derived is also discussed and the results are applied to a model of canadian money income and interest rates

a bivariate first order autoregressive time series model in exponential variables bear 1 a simple time series model for bivariate exponential variables having first order autoregressive structure is presented the bear 1 model the linear random coefficient difference equation model is an adaptation of the new exponential autoregressive model near 2 the process is markovian in the bivariate sense and has correlation structure analogous to that of the gaussian ar 1 bivariate time series model the model exhibits a full range of positive correlations and cross correlations with some modification in either the innovation or the random coefficients the model admits some negative values for the cross correlations the marginal processes are shown to have correlation structure of arma 2 1 models

modeling multiple time series with applications abstract an approach to the modeling and analysis of multiple time series is proposed properties of a class of vector autoregressive moving average models are discussed modeling procedures consisting of tentative specification estimation and diagnostic checking are outlined and illustrated by three real examples

noncausal vector ar processes with application to economic time series abstract inference procedures for noncausal autoregressive ar models have been well studied and applied in a variety of applications from environmental to financial for such processes the observation at time t may depend on both past and future shocks in the system in this paper we consider extension of the univariate noncausal ar models to the vector ar var case the extension presents several interesting challenges since even a first order var can possess both causal and noncausal components assuming a non gaussian distribution for the noise we show how to compute an approximation to the likelihood function under suitable conditions it is shown that the maximum likelihood estimator mle of the vector of ar parameters is asymptotically normal the estimation procedure is illustrated with a simulation study for a var 1 process and with two real data examples

image coding using wavelet transform a scheme for image compression that takes into account psychovisual features both in the space and frequency domains is proposed this method involves two steps first a wavelet transform used in order to obtain a set of biorthogonal subclasses of images the original image is decomposed at different scales using a pyramidal algorithm architecture the decomposition is along the vertical and horizontal directions and maintains constant the number of pixels required to describe the image second according to shannon s rate distortion theory the wavelet coefficients are vector quantized using a multiresolution codebook to encode the wavelet coefficients a noise shaping bit allocation procedure which assumes that details at high resolution are less visible to the human eye is proposed in order to allow the receiver to recognize a picture as quickly as possible at minimum cost a progressive transmission scheme is presented it is shown that the wavelet transform is particularly well adapted to progressive transmission

a fast estimation method for the vector autoregressive moving average model with exogenous variables abstract a very fast and simple algorithm for estimation of the parameters of large multivariate time series and distributed lag models is presented an analysis of the distribution of the estimates shows that they are asymptotically normal and unbiased and that they have a variance that decreases like 1 n n being the sample size the algorithm is especially applicable for estimation of large multivariate models where it is generally many times faster than maximalization algorithms

nonparametric transfer function models in this paper a class of nonparametric transfer function models is proposed to model nonlinear relationships between input and output time series the transfer function is smooth with unknown functional forms and the noise is assumed to be a stationary autoregressive moving average arma process the nonparametric transfer function is estimated jointly with the arma parameters by modelling the correlation in the noise the transfer function can be estimated more efficiently the parsimonious arma structure improves the estimation efficiency in finite samples the asymptotic properties of the estimators are investigated the finite sample properties are illustrated through simulations and one empirical example

parameter identification of 1d fractal interpolation functions using bounding volumes fractal interpolation functions are very useful in capturing data that exhibit an irregular non smooth structure two new methods to identify the vertical scaling factors of such functions are presented in particular they minimize the area of the symmetric difference between the bounding volumes of the data points and their transformed images comparative results with existing methods are given that establish the proposed ones as attractive alternatives in general they outperform existing methods for both low and high compression ratios moreover lower and upper bounds for the vertical scaling factors that are computed by the first method are presented

accounting for covariates observed post randomization for discrete and continuous repeated measures data adjusting for covariates observed post randomization is a difficult issue in randomized clinical trials one approach is to enter these covariates as time dependent covariates in a general linear model however this approach fails to account for interactions between the primary end point and confounding variable as they evolve over time in this paper we adopt the view that since the confounder is observed following randomization it should be treated as an outcome and analysed accordingly we consider the repeated measures design where both the primary end point and the confounding measure are observed repeatedly over patient follow up a generalized estimating equation model is applied to allow each set of repeated measures to be modelled in terms of important explanatory variables seemingly unrelated regression combines the two models into an overall framework for analysis inference is then performed by imposing restrictions on the confounder regression parameters to reflect the behaviour profile of interest estimation and identification procedures are described and the methodology is illustrated with an example

distribution of the residual autocorrelations in multivariate arma time series models the large sample distribution of the multivariate residual autocorrelations in the vector arma model is derived this result is somewhat less complicated for the vector autoregressive model a new multivariate portmanteau test for checking the adequacy of fitted vector arma models is developed a simulation study shows that a simple modification of the portmanteau test improves its accuracy in small samples

iterated function systems and the global construction of fractals iterated function systems i f ss are introduced as a unified way of generating a broad class of fractals these fractals are often attractors for i f ss and occur as the supports of probability measures associated with functional equations the existence of certain p balanced measures for i f ss is established and these measures are uniquely characterized for hyperbolic i f ss the hausdorff besicovitch dimension for some attrac tors of hyperbolic i f ss is estimated with the aid of p balanced measures what appears to be the broadest framework for the exactly computable moment theory of p balanced measures that of linear i f ss and of probabilistic mixtures of iterated riemann surfaces is presented this extensively generalizes earlier work on orthogonal polynomials on julia sets an example is given of fractal reconstruction with the use of linear i f ss and moment theory

technical communique a note on connectivity of multi agent systems with proximity graphs and linear feedback protocol in this note we investigate the possibility of simplifying the connectivity verification for multi agent systems with proximity graphs and the linear feedback protocol a lyapunov like approach is developed for analyzing the monotonicity of the largest edge length in the network based on this approach we provide an initial connectivity based sufficient condition for dynamic connectivity in particular when each initial node degree is not less than three fourth of the graph order initial network connectivity implies dynamic connectivity

hybrid control for connectivity preserving flocking in this technical note we address the combined problem of motion and network topology control in a group of mobile agents with common objective the flocking behavior of the group instead of assuming network connectivity we enforce it by means of distributed topology control that decides on both deletion and creation of communication links between agents adapting the network to the group s spatial distribution with this protocol ensuring network connectivity a decentralized motion controller aligns agent velocity vectors and regulates inter agent distances to maintain existing network links the stability of the flocking controller is established in continuous time by means of an observability argument on a quadratic form of the graph laplacian that exploits the time delay between link deletion and creation caused by the topology control protocol which induces a dwell time between network switches

modeling the predictive power of the singular value decomposition based entropy empirical evidence from the dow jones global titans 50 index abstract in this paper we are analyzing the predictive power of the singular value decomposition entropy for the dow jones global titans 50 djgt index of the new york stock exchange nyse market for the correlation matrix of the djgt index components we then calculate the entropy of the singular value decomposition granger causality test was used to prove that entropy has a predictive power for stock market dynamics of the djgt index the results of our paper are revealing the importance of considering structural breaks when analyzing financial stock markets our analysis is conducting to the conclusion that the singular value decomposition entropy has predictive power for the nyse djgt index

some results on multivariate autoregressive index models summary we discuss methods for modelling multivariate autoregressive time series in terms of a smaller number of index series which are chosen to provide as complete a summary as possible of the past information contained in the original series necessary for prediction purposes the maximum likelihood method of estimation and asymptotic properties of estimators of the coefficients which determine the index variables as well as the corresponding autoregressive coefficients are discussed a numerical example is presented to illustrate the use of the autoregressive index models

a method for approximate representation of vector valued time series and its relation to two alternatives abstract a method for constructing a state space representation of vector valued time series is discussed and related to two other methods for markovian model construction from a unifying point of view the three methods are shown to employ different normalizations and to employ different optimality criteria for summarizing in a finite dimensional state vector information contained in past data the method proposed in this paper is shown to lead to stable innovation models for time series a numerical example is included to illustrate the potential of the proposed method

identifying a simplifying structure in time series abstract this article studies how to identify hidden factors in multivariate time series process this problem is important because when the series are driven by a set of common factors a a large number of parameters may be needed to obtain an adequate representation of the system and b the estimated parameters will be highly correlated therefore a complex and badly defined relationship can appear when in fact a simpler and parsimonious model in terms of a few common factors can be operating this article develops a methodology to identify the number of factors and to build a simplifying transformation to represent the series it is proved that the number of factors is equal to the rank of the covariance matrices and the parameter matrices of the infinite moving average representation of the process the eigenvectors of these matrices will provide the canonical transformation the method is illustrated with one example using series of the price of wheat in five provinces of spain in the 19th ce

on the rendezvous problem for multiple nonholonomic agents in this note a decentralized feedback control strategy that drives a system of multiple nonholonomic unicycles to a rendezvous point in terms of both position and orientation is introduced the proposed nonholonomic control law is discontinuous and time invariant and using tools from nonsmooth lyapunov theory and graph theory the stability of the overall system is examined similarly to the linear case the convergence of the multi agent system relies on the connectivity of the communication graph that represents the inter agent communication topology the control law is first defined in order to guarantee connectivity maintenance for an initially connected communication graph moreover the cases of static and dynamic communication topologies are treated as corollaries of the proposed framework

optimal tests of noncorrelation between multivariate time series the problem of testing noncorrelation between two multivariate time series is considered assuming that the global process admits a joint vector autoregressive var representation noncorrelation between the two component series is equivalent to the hypothesis that all off diagonal blocks in the matrix coefficients and the innovation covariance of the joint var representation are zero we establish an adequate local asymptotic normality lan property for this var model in the vicinity of noncorrelation this lan structure allows construction of optimal pseudo gaussian tests that is tests that are locally and asymptotically optimal under gaussian innovations but remain valid under non gaussian ones for the null hypothesis of noncorrelation and for comparing their local asymptotic powers with those of the heuristic tests haugh el himdi roy and koch yang hallin saidi proposed in the literature

encompassing univariate models in multivariate time series a case study abstract through the encompassing principle univariate arima analysis could provide an important tool for diagnosis of var models the univariate arima models implied by the var should explain the results from univariate analysis this comparison is seldom performed possibly due to the paradox that while the implied arima models typically contain a very large number of parameters univariate analysis yields highly parsimonious models using a var application to six french macro economic variables it is seen how the encompassing check is straight forward to perform and surprisingly accurate

brief paper a novel approach to coordination of multiple robots with communication failures via proximity graph in this paper we investigate the coordination problem of multiple robots with limited communication ranges and communication failures a novel rendezvous algorithm via proximity graph is developed so that the robot group can achieve rendezvous when the communication links satisfy an ergodic assumption the convergence proof of the algorithm is established based on the tools from rooted graph theory the effectiveness of the algorithm is illustrated by numerical examples in a 3d space

multivariate time series projections of parameterized age specific fertility rates abstract projection of individual age specific fertility rates is a forecasting problem of high dimension we solve this dimensionality problem by using parametric curves to approximate the annual age specific rates and a multivariate time series model to forecast the curve parameters these yield forecasts of future fertility curves which are then used to compute age specific fertility rate forecasts this reduces the dimensionality of the forecasting problem and also guarantees that long run projections of age specific fertility rates will exhibit a smooth shape across age similar to historical data short term projections are improved by also using simple techniques to forecast the deviations of the fitted curves from the actual rates the article applies this approach to age specific fertility data for u s white women from 1921 1984 the resulting forecasts are examined and the multivariate model is used to investigate possible relations between the curve parameters expressed as the total fertilit

a neural network to compute the hutchinson metric in fractal image processing the hutchinson metric is a natural measure of the discrepancy between two images for use in fractal image processing a neural network is described which can quickly calculate this metric by combining this with the architecture previously described by the author for implementing the markov operator of an iterated function system ifs on a neural network a fast method is obtained for determining the distance between a target image and the invariant measure of a trial ifs this has obvious applications to barnsley s fractal image compression scheme

the multi agent rendezvous problem part 2 the asynchronous case this paper is concerned with the collective behavior of a group of n 1 mobile autonomous agents labelled 1 through n which can all move in the plane each agent is able to continuously track the positions of all other agents currently within its sensing region where by an agent s sensing region we mean a closed disk of positive radius r centered at the agent s current position the multi agent rendezvous problem is to devise local control strategies one for each agent which without any active communication between agents cause all members of the group to eventually rendezvous at a single unspecified location this paper describes a family of unsynchronized strategies for solving the problem correctness is established appealing to the concept of analytic synchronization

a new look at the relationship between time series and structural econometric models abstract this paper examines the relationship between dynamic structural econometric models sem and time series ts models it extends the work of others by suggesting a reconciliation of sem and ts models based on classical linear parameter restrictions in regression models rather than on time series methods the paper demonstrates that in a number of common economic contexts there exist sets of plausible restrictions on the stochastic properties of the disturbances and on the dynamic adjustment processes in a sem such that familiar structural models take on the form of univariate ts models consequently it is argued that ts models should not be arbitrarily dismissed as being devoid of economic content

state estimation of heterogeneous oscillators by means of proximity measurements in this paper we aim at estimating the state of an ensemble of mobile agents specifically each agent is an oscillator which moves along a circle at a given time varying angular velocity we assume that an agent can measure the distance from the other oscillators only when they are in its proximity we propose a recursive algorithm which based on these intermittent and uncertain measurements is capable of estimating in real time the relative angular position of the agents the algorithm combines the information coming from the collected measures with the information on the agents dynamics and its convergence is proved by means of interval analysis the theoretical analysis is complemented with extensive numerical simulations

lotka volterra signals in asean currency exchange rates abstract the 1997 asian financial crisis stimulated interest in whether there are regional currency linkages that can transmit exchange rate fluctuations and pave the way to a single regional currency most such investigations to date are statistical analyses such as calculating cross correlations rather than applications of nonlinear statistical models such as the lotka volterra model however statistical significance is not the same as meaningful significance and exchange rate time series appear to be noisy how can we determine whether we are looking at temporally correlated signals or at temporally correlated noise we utilized the lotka volterra model to extract and reconstruct signal from noisy long term time series of daily exchange rates of asean 5 3 currencies the lotka volterra model revealed hitherto undetected relationships with meaningful significance between certain currency pairs and provided four interpretable parameters for each pair wise interaction statically significant fits were obtained for eleven of the sixty four pair combinations there is no evidence for trading blocs

outliers in multivariate time series summary this paper generalises four types of disturbance commonly used in univariate time series analysis to the multivariate case highlights the differences between univariate and multivariate outliers and investigates dynamic effects of a multivariate outlier on individual components the effect of a multivariate outlier depends not only on its size and the underlying model but also on the interaction between the size and the dynamic structure of the model the latter factor does not appear in the univariate case a multivariate outlier can introduce various types of outlier for the marginal component models by comparing and contrasting results of univariate and multivariate outlier detections one can gain insights into the characteristics of an outlier we use real examples to demonstrate the proposed analysis

consensus seeking in multiagent systems under dynamically changing interaction topologies this note considers the problem of information consensus among multiple agents in the presence of limited and unreliable information exchange with dynamically changing interaction topologies both discrete and continuous update schemes are proposed for information consensus this note shows that information consensus under dynamically changing interaction topologies can be achieved asymptotically if the union of the directed interaction graphs have a spanning tree frequently enough as the system evolves

reaching a consensus in a dynamically changing environment a graphical approach this paper presents new graph theoretic results appropriate for the analysis of a variety of consensus problems cast in dynamically changing environments the concepts of rooted strongly rooted and neighbor shared are defined and conditions are derived for compositions of sequences of directed graphs to be of these types the graph of a stochastic matrix is defined and it is shown that under certain conditions the graph of a sarymsakov matrix and a rooted graph are one and the same as an illustration of the use of the concepts developed in this paper graph theoretic conditions are obtained which address the convergence question for the leaderless version of the widely studied vicsek consensus problem

an approach to causal modeling in fuzzy environment and its application abstract this paper suggests an approach to building a causal model in fuzzy environment the fuzzy vector autoregressive fvar model is defined and a causality definition in the fuzzy environment is provided three types of causality namely nonfuzzy non causality nonfuzzy causality and fuzzy causality are defined based on the fuzzy causality relation introduced in this paper necessary and sufficient conditions for the three types of causality are also provided when fuzzy parameters have triangular membership functions a procedure to implement the fuzzy causal modeling is also suggested we applied the procedure to real data and compared it with other conventional empirical studies

a bayesian multivariate nonstationary time series model for estimating mutual relationships among variables abstract the purpose of this paper is to propose a bayesian multivariate stochastic model with latent nonstationary trends and seasonal components and show its use to determine the relationships among the variables the model is expressed in state space form and the parameters of the model are estimated by maximum likelihood using a numerical optimization algorithm the kalman filter is used to compute the likelihood of the model and the information criterion aic is used to select the best fitting model the relationships among variables are examined in the frequency domain using estimated components japanese macroeconomic series are analyzed by our procedure

testing causality using efficiently parametrized vector arma models we outline a practical methodology for fitting to data vector autoregressive moving average models and employ these models to test for granger causality the procedures are applied to data on employment and hourly wages in manufacturing in the united states

time series of continuous proportions summary a vector of continuous proportions consists of the proportions of some total accounted for by its constituent components an example is the proportions of world motor vehicle production by japan the usa and all other countries we consider the situation where time series data are available and where interest focuses on the proportions rather than the actual amounts reasons for analysing such times series include estimation of the underlying trend estimation of the effect of covariates and interventions and forecasting we develop a state space model for time series of continuous proportions conditionally on the unobserved state the observations are assumed to follow the dirichlet distribution often considered to be the most natural distribution on the simplex the state follows the dirichlet conjugate distribution which is introduced here thus the model although based on the dirichlet distribution does not have its restrictive independence properties covariates trends seasonality and interventions may be incorporated in a natural way the model has worked well when applied to several examples and we illustrate with components of world motor vehicle production

testing causality between two vectors in multivariate autoregressive moving average models abstract in the analysis of economic time series a question often raised is whether a vector of variables causes another one in the sense of granger most of the literature on this topic is concerned with bivariate relationships or uses finite order autoregressive specifications the purpose of this article is to develop a causality analysis in the sense of granger for general vector autoregressive moving average arma models we give a definition of granger noncausality between vectors which is a natural and simple extension of the notion of granger noncausality between two variables in our context this definition is shown to be equivalent to a more complex definition proposed by tjostheim for the class of linear invertible processes we derive a necessary and sufficient condition for noncausality between two vectors of variables when the latter do not necessarily include all the variables considered in the analysis this result is then specialized to the class of stationary invertible arma process

consensus over ergodic stationary graph processes in this technical note we provide a necessary and sufficient condition for convergence of consensus algorithms when the underlying graphs of the network are generated by an ergodic and stationary random process we prove that consensus algorithms converge almost surely if and only if the expected graph of the network contains a directed spanning tree our results contain the case of independent and identically distributed graph processes as a special case we also compute the mean and variance of the random consensus value that the algorithm converges to and provide a necessary and sufficient condition for the distribution of the consensus value to be degenerate

stability of multiagent systems with time dependent communication links we study a simple but compelling model of network of agents interacting via time dependent communication links the model finds application in a variety of fields including synchronization swarming and distributed decision making in the model each agent updates his current state based upon the current information received from neighboring agents necessary and or sufficient conditions for the convergence of the individual agents states to a common value are presented thereby extending recent results reported in the literature the stability analysis is based upon a blend of graph theoretic and system theoretic tools with the notion of convexity playing a central role the analysis is integrated within a formal framework of set valued lyapunov theory which may be of independent interest among others it is observed that more communication does not necessarily lead to faster convergence and may eventually even lead to a loss of convergence even for the simple models discussed in the present paper