bluuebunny/crossref_metadata_2025 huggingface dataset:
Identifying Decision Useful Information with the Matrix Format Income Statement
Authors: Tarca Ann , Hancock Phil , Woodliff David , Brown Philip , Bradbury Michael , Van Zijl Tony
Published: May 2008

We conduct an experiment to investigate the potential benefits of an alternative format for the income statement, the matrix format, initially developed by the International Accounting Standards Board (IASB) and UK Accounting Standards Board in their joint project on performance reporting. Sophisticated financial statement users (financial analysts and professional accountants) and less sophisticated financial statement users (MBA students) were asked to extract information from a set of financial statements that included an income statement either in the IAS 1 format or in the matrix format. We find that the matrix format improves the accuracy with which users extract financial information. This result is driven by greater accuracy, for all user groups, on “below‐the‐line” items. Furthermore, despite lack of familiarity with the matrix format, its use did not appear to affect the time taken, the ease of extracting financial information, or users' task completion confidence; further experience with the matrix format could lead to benefits along these lines as well. Our findings may assist the FASB and IASB in their joint project on financial statement presentation.
Trustworthiness, Risk, and the Transfer of Tacit and Explicit Knowledge Between Alliance Partners
Authors: Becerra Manuel , Lunnan Randi , Huemer Lars
Published: May 2008

abstract  The transfer of knowledge in alliances entails risk to partners, whose willingness to accept it presumably relies on the trustworthiness that they perceive in their partners. We investigate the extent to which the perceptions of trustworthiness and the willingness to take risk determine the transfer of knowledge between alliance partners and their ultimate impact on alliance success. The results show that the transfer of tacit versus explicit knowledge have very different trust and risk profiles. Whereas explicit knowledge is closely associated with the firm's willingness to take risk, tacit knowledge is intimately related to high trustworthiness. The results support the important role of trust and the transfer of tacit knowledge on the success of learning alliances.
Heterogeneity and Specificity of Inter‐Firm Knowledge Flows in Innovation Networks
Authors: Sammarra Alessia , Biggiero Lucio
Published: May 2008

abstract  Literature on innovation networks reveals little of to what extent different types of knowledge are exchanged and combined by collaborating firms to foster innovation. Based on field research in the aerospace industrial cluster of Rome, this study investigates the exchange of technological, market and managerial knowledge. Using social network analysis, the paper shows that the three types of knowledge are unevenly distributed and exchanged, thus revealing that the process of exchange is knowledge‐specific. Further, it is found that in most collaborative relationships, partners exchange technological knowledge together with market and managerial knowledge, emphasizing the complex nature of the innovation process which requires access to and recombination of diverse knowledge. This phenomenon concerns not only large companies, but also small‐to‐medium enterprises. The reconsideration of the relative salience of the three types of knowledge has remarkable managerial and theoretical implications, which are developed in terms of propositions offered for further research.
Inter‐ and Intra‐Organizational Knowledge Transfer: A Meta‐Analytic Review and Assessment of its Antecedents and Consequences
Authors: Van Wijk Raymond , Jansen Justin J. P. , Lyles Marjorie A.
Published: May 2008

abstract  Research on organizational knowledge transfer is burgeoning, and yet our understanding of its antecedents and consequences remains rather unclear. Although conceptual and qualitative reviews of the organizational knowledge transfer literature have emerged, no study has attempted to summarize previous quantitative empirical findings. As a first step towards that goal, we use meta‐analytic techniques to examine how knowledge, organization and network level antecedents differentially impact organizational knowledge transfer. Additionally, we consolidate research on the relationship between knowledge transfer and its consequences. We also demonstrate how the intra‐ and inter‐organizational context, the directionality of knowledge transfers, and measurement characteristics moderate the relationships studied. By aggregating and consolidating existing research, our study not only reveals new insights into the levers and outcomes of organizational knowledge transfer, but also provides meaningful directions for future research.
ETUDE DES FACTEURS CONTINGENTS DU TAUX DE REMBOURSEMENT AU SEIN D'UNE INSTITUTION DE, MICROFINANCE: LE CAS DU TOGO
Authors: HUDON Marek , OURO‐KOURA Tchakodo
Published: May 2008

RESUME**:  Parmi les facteurs contingents du taux de remboursement des microcrédits, la responsabilité solidaire des emprunteurs est peu analysée dans les études empiriques. Grâce à la constitution d'une base de données originale, cet article s'attache à estimer l'impact des facteurs contingents du taux de remboursement, dont la forme du prêt. Les résultats dégagés font apparaître que, d'une part, dans la catégorie des prêts remboursés dans les délais, les prêts individuels offrent un meilleur taux de recouvrement mais aussi, d'autre part, que cet effet s'estompe dès lors que sont incorporés les remboursements tardifs. Parmi les autres facteurs contingents du taux de remboursement, on dénombre principalement la distance entre le lieu de résidence des clients et le siège de l'institution et l'objet du crédit.
DIFFÉRENCE DE SATISFACTION DANS L'EMPLOI ENTRE SECTEURS À BUT LUCRATIF ET À BUT NON LUCRATIF: LE RÔLE JOUÉ PAR LES CARACTÉRISTIQUES D'EMPLOI
Authors: LANFRANCHI Joseph , NARCY Mathieu
Published: May 2008

RÉSUMÉ**:  Cet article cherche à estimer, pour un échantillon issu de sept pays européens, le différentiel de satisfaction dans l'emploi entre les secteurs à but non lucratif et à but lucratif, et à en examiner les causes. La richesse des données utilisées permet, contrairement aux études antérieures, de prendre en compte les différences de caractéristiques d'emploi entre les deux secteurs. Les résultats obtenus révèlent que la plus grande satisfaction dans l'emploi des salariés du secteur à but non lucratif résulte principalement de caractéristiques mesurant l'autonomie et l'intérêt de l'emploi et d'une plus grande satisfaction vis‐à‐vis de leur autonomie et de la nature du travail qu'ils effectuent, éléments susceptibles de contribuer au développement de la motivation intrinsèque des salariés.
INCOMMENSURABILITY, RELATIVISM, SCEPTICISM: REFLECTIONS ON ACQUIRING A CONCEPT
Authors: Goldberg Nathaniel , Rellihan Matthew
Published: May 2008

Some opponents of the incommensurability thesis, such as Davidson and Rorty, have argued that the very idea of incommensurability is incoherent and that the existence of alternative and incommensurable conceptual schemes is a conceptual impossibility. If true, this refutes Kuhnian relativism and Kantian scepticism in one fell swoop. For Kuhnian relativism depends on the possibility of alternative, humanly accessible conceptual schemes that are incommensurable with one another, and the Kantian notion of a realm of unknowable things‐in‐themselves gives rise to the possibility of humanly inaccessible schemes that are incommensurable with even our best current or future science. In what follows we argue that the possibility of incommensurability of either the Kuhnian or the Kantian variety is inescapable and that this conclusion is forced upon us by a simple consideration of what is involved in acquiring a concept. It turns out that the threats from relativism and scepticism are real, and that anyone, including Davidson himself, who has ever defended an account of concept acquisition is committed to one or the other of these two possibilities.1
LOWE'S DEFENCE OF CONSTITUTION AND THE PRINCIPLE OF WEAK EXTENSIONALITY
Authors: Hershenov David B.
Published: May 2008

E.J. Lowe is one of the few philosophers who defend both the existence of spatially coincident entities and the Principle of Weak Extensionality that no two objects which have proper parts have exactly the same proper parts at the same time. Lowe maintains that when spatially coincident things like the statue and the lump of bronze are in a constitution relation, the constituted entity (the statue) has parts that the constituting entity (the lump) doesn't, hence the compatibility with Weak Extensionality. My contention is that his argument for why the statue has parts the lump of bronze lacks can also be used to show that the lump of bronze has parts the statue doesn't. This will mean that there is no basis for saying the statue and the lump are in a constitution relation. I argue for accepting a modified account of constitution and abandoning the Principle of Weak Extensionality.
KANTIAN VALUE REALISM
Authors: Hills Alison
Published: May 2008

Why should we be interested in Kant's ethical theory? One reason is that we find his views about our moral responsibilities appealing. Anyone who thinks that we should treat other people with respect, that we should not use them as a mere means in ways to which they could not possibly consent, will be attracted by a Kantian style of ethical theory.

But according to recent supporters of Kant, the most distinctive and important feature of his ethical theory is not his claims about the particular ethical duties that we owe to each other, but his views about the nature of value. They argue that Kant has an account of the relationship between practical reason and value, known as “Kantian constructivism” that is far superior to the traditional “value realist” theory, and that it is because of this that we should accept his theory.1

It is now standard for both supporters and critics to claim that Kant's moral theory stands or falls with Kantian constructivism.2 But this is a mistake. In this paper, I sketch a rival Kantian theory of value, which I call Kantian value realism. I argue that there is textual evidence that Kant himself accepted value realism rather than constructivism. Whilst my aim in this paper is to set out the theory clearly rather than to defend it, I will try to show that Kantian value realism is preferable to Kantian constructivism and that it is worthy of further study.
INSTITUTIONAL CONSTRAINTS AND MULTIPLE EQUILIBRIA IN DECENTRALIZATION
Authors: Azis Iwan J
Published: May 2008

The economics of decentralization implies that it generates efficiency improvement (higher growth) due to local government's ability to respond to the needs of local communities. However, this is not always the case. While policies do matter, this paper argues that institutional factors hold the key to the problem. The interactions among these factors and the characteristics of leaders in the region determine the outcome of decentralization. By capturing these important properties, multiple equilibria can be generated, avowing the ambiguous effects of local capture. On this basis, a typology of local leaders is developed. To the extent that welfare‐enhancing activities are often related to regional growth, the role of an incentive system in determining the local leaders' behavior is also analyzed. In essence, a lack of incentive mechanisms for local leaders to promote growth and the absence of a stick‐and‐carrot system explain why post‐decentralization growth performance has been generally disappointing.
The Potential of Maps APIs for Internet GIS Applications
Authors: Chow T Edwin
Published: May 2008

Since the launching of Maps Application Programming Interfaces (APIs) in 2005, many web developers, including geographers and non‐geographers, applauded the freely adaptable tools and used them to spawn numerous Internet applications. The success of the Maps APIs is largely attributable to its no‐cost policy, the availability of global data coverage, dynamic navigation, query capability, and ease of implementation. Despite its versatility in dynamic exploration of geographic data online, the existing Maps APIs lack Geographic Information System (GIS) functionalities compared to other Internet Mapping Services. The goal of this research was to review the potential of the Maps APIs for Internet GIS applications. This research employed the Google Maps API and developed a web prototype that disseminates spatial information of urban sprawl in Mundy Township, Michigan. The results revealed that both vector and raster data could be effectively represented by using the Maps API. Moreover, the Geographic Markup Language (GML) approach illustrated great potential for developing Internet GIS solutions around open specifications. This research suggested several potential solutions to expand the spectrum of GIS operations of the Maps APIs by incorporating the XML‐related technology and extending the JavaScript library.
AN INTRODUCTION TO LONG‐MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING
Authors: Granger C. W. J. , Joyeux Roselyne
Published: May 2008

Abstract. The idea of fractional differencing is introduced in terms of the infinite filter that corresponds to the expansion of (1‐B)d. When the filter is applied to white noise, a class of time series is generated with distinctive properties, particularly in the very low frequencies and provides potentially useful long‐memory forecasting properties. Such models are shown to possibly arise from aggregation of independent components. Generation and estimation of these models are considered and applications on generated and real data presented.
GROUP DELAY AND THE TIME‐LAG RELATIONSHIP BETWEEN STOCHASTIC PROCESSES
Authors: Deaton Michael L. , Foutz Robert V.
Published: May 2008

Abstract. A time‐lag interpretation is given for the group delay between continuous‐time weakly‐stationary stochastic processes, and a corresponding time‐lag relationship between stochastic processes is revealed. The group delay for discrete‐time processes and its relationship to time leakage are also discussed.
TIME SERIES ANALYSIS IN ACCOUNTING: A SURVEY AND ANALYSIS OF RECENT ISSUES
Authors: Hopwood W. S. , Newbold P.
Published: May 2008

Abstract.In the last few years there has been considerable interest in the accounting literature in time series methods. This paper briefly surveys those areas of accounting in which time series analysis has proved useful and discusses the analytical procedures that have been employed.
SOME ASPECTS OF MODELLING AND FORECASTING MULTIVARIATE TIME SERIES
Authors: Jenkins Gwilym M. , Alavi Athar S.
Published: May 2008

Abstract.The paper describes model structures for representing multivariate time series containing stochastic trends and stochastic seasonal patterns. Methods for identification, fitting and checking these models are described. Two methods of identification are compared: using statistics calculated from (a) the unprewhitened stationary series, obtained by appropriately transforming and differencing the original series, (b) the series after prewhitening them by their univariate models. We see practical merit in using both methods of approach. A brief description is also given of the use of multivariate stochastic models for forecasting but a fuller description is to be given elsewhere, covering more general situations where the series may have to be aligned or phase‐shifted relative to each other to obtain a more parsimonious representation.
A NOTE ON THE DISTRIBUTIONS OF NON‐LINEAR AUTOREGRESSIVE STOCHASTIC MODELS
Authors: Pemberton J. , Tong H.
Published: May 2008

Abstract. It is shown that an ergodic stationary non‐linear autoregressive stochastic process, perturbed by a white noise process with symmetric distributions, has symmetric stationary distributions if and only if the regression function is skew‐symmetric. The implications of this observation are discussed.
ROBUST REGRESSION AND INTERPOLATION FOR TIME SERIES
Authors: Taniguchi Masanobu
Published: May 2008

Abstract. In this paper we shall consider the interpolation problem under the condition that the spectral density of a stationary process concerned is vaguely known (i.e., Huber's ε ‐contaminated model). Then we can get a minimax robust interpolator for the class of spectral densities S={ g:g(x)=(1‐ε)f(x)+εh(x)ε Ar Do, 0<ε<1}, where f(x) is a known spectral density and D0 is a certain class of spectral densities. Also we shall consider the time series regression problem under the condition that the residual spectral density is vaguely known. Then we can get a minimax robust regression coefficient estimate for the class of the residual spectral densities S.
REPLICATED OBSERVATIONS OF LOW ORDER AUTOREGRESSIVE TIME SERIES
Authors: Azzalini A.
Published: May 2008

Abstract. Independent replicates of first and second order autoregressive stationary time series are considered. Maximum likelihood estimates are studied with special emphasis on asymptotics when the number of replicates tends to infinity and the length of each replicate is fixed.
ACCURACY OF LINEAR SPECTRAL ESTIMATES OF BAND‐LIMITED SIGNALS
Authors: Gordon William B.
Published: May 2008

Abstract. We consider the problem of estimating the spectrum of a band‐limited signal perturbed by additive white noise. Sharp bounds on the mean square errors of linear spectral estimates are computed and expressed as functions of time‐bandwidth products and signal‐to‐noise power ratios.
ESTIMATING THREE‐DIMENSIONAL ENERGY TRANSFER IN ISOTROPIC TURBULENCE*
Authors: Lii K. S. , Helland K. N. , Rosenblatt M.
Published: May 2008

Abstract. In order to obtain an estimate of three‐dimensional energy transfer in grid‐generated turbulence, second and third order spectra are statistically estimated. A Monte Carlo Fourier transformation of bispectra is carried out so as to gauge energy transfer between wavenumber shells.
LYDIA PINKHAM DATA REMODELLED
Authors: Bhattacharyya M. N.
Published: May 2008

Abstract. There is no effective bivariate feedback relationship between Lydia Pinkham monthly dollar sales and advertising. The effectiveness is measured in terms of the forecasting performance of the bivariate models against the forecasting performance of the respective univariate models, outside the fit period. While the future advertising is better forecast by using the past advertising and sales, the future sales are better forecast by using past sales only. There is no Koyck effect or memory effect of advertising on sales. The results are completely opposite to the conclusion of the previous researchers.
AUTOREGRESSIVE PROCESSES WITH A TIME DEPENDENT VARIANCE
Authors: Tyssedal John S. , Tjøstheim Dag
Published: May 2008

Abstract. We study nonstationary autoregressive time series where the variance of the residual process is allowed to depend on time. In earlier publications the variance has been modelled by a step function. We look at more general classes of functions and propose two estimates of the autoregressive coefficients, both of which are consistent under weak assumptions. We also show how it is possible to obtain an estimate in practice using an iterative regression procedure.
APPROXIMATE FILTERING OF PARAMETER DRIVEN PROCESSES
Authors: Azzalini A.
Published: May 2008

Abstract. A stationary stochastic process {θ,} is of interest but cannot be observed directly. Instead, at time t, we observe y, which is sampled from a distribution determined by the value of θt, say f(y∥θ,). A method for estimating the current value of {θ,} is described.
THE SIZE OF THE STATIONARITY AND INVERTIBILITY REGION OF AN AUTOREGRESSIVE‐MOVING AVERAGE PROCESS
Authors: Piccolo Domenico
Published: May 2008

Abstract. The hypervolume of the stationarity and invertivility regions for the parameters of autoregressive‐moving average processes is computed, and the severity of these constraints for higher order models is discussed.
A NOTE ON THE EXISTENCE OF STRICTLY STATIONARY SOLUTIONS TO BILINEAR EQUATIONS
Authors: Quinn B. G.
Published: May 2008

Abstract. Necessary and sufficient conditions for the existence of strictly stationary solutions to a class of bilinear equations are derived, extending the results obtained by Tong (1981) and Quinn (1982).
A NOTE ON ARMA ESTIMATION
Authors: Hong‐Zhi An , Zhao‐Guo Chen , Hannan E. J.
Published: May 2008

Abstract. The ranks of certain matrices composed of autocovariances of an ARMA process are considered. These matrices arise in connection with initial estimates of the parameters of such a system. Conditions for such matrices to be of full rank are expressed in terms of conditions on a dual time reversed system.
SPECTRAL ANALYSIS WITH TAPERED DATA
Authors: Dahlhaus Rainer
Published: May 2008

Abstract. A new method based on an upper bound for spectral windows is presented for investigating the cumulants of time series statistics. Using this method two classical results are proved for tapered data. In particular, the asymptotic normality for a class of spectral estimates including estimates for the spectral function and the covariance function is proved under integrability conditions on the spectra using the method of cumulants.
THE ESTIMATION AND APPLICATION OF LONG MEMORY TIME SERIES MODELS
Authors: Geweke John , Porter‐Hudak Susan
Published: May 2008

Abstract. The definitions of fractional Gaussian noise and integrated (or fractionally differenced) series are generalized, and it is shown that the two concepts are equivalent. A new estimator of the long memory parameter in these models is proposed, based on the simple linear regression of the log periodogram on a deterministic regressor. The estimator is the ordinary least squares estimator of the slope parameter in this regression, formed using only the lowest frequency ordinates of the log periodogram. Its asymptotic distribution is derived, from which it is evident that the conventional interpretation of these least squares statistics is justified in large samples. Using synthetic data the asymptotic theory proves to be reliable in samples of 50 observations or more. For three postwar monthly economic time series, the estimated integrated series model provides more reliable out‐of‐sample forecasts than do more conventional procedures.
ON THE SELECTION OF SUBSET AUTOREGRESSIVE TIME SERIES MODELS
Authors: Haggan V. , Oyetunji O. B.
Published: May 2008

Abstract.The estimation of subset autoregressive time series models has been a difficult problem because of the large number of possible alternative models involved. However, with the advent of model selection criteria based on the maximum likelihood, subset model fitting has become feasible. Using an efficient technique for evaluating the residual variance of all possible subset models, a method is proposed for the fitting of subset autoregressive models. The application of the method is illustrated by means of real and simulated data.
ON SOME AMBIGUITIES ASSOCIATED WITH THE FITTING OF ARMA MODELS TO TIME SERIES
Authors: Findley David F.
Published: May 2008

Abstract. Examples are presented illustrating some ambiguities associated with the application of ARMA models to problems of signal extraction, multistep‐ahead forecasting, spectrum approximation and linear quadratic control. Except in the signal extraction example, the ambiguities arise either from lack of sufficient autocovariance data to completely determine the process, or, often relatedly, from the approximate nature of the models used.
COMPUTATIONALLY EFFICIENT IMPLEMENTATION OF A BAYESIAN SEASONAL ADJUSTMENT PROCEDURE
Authors: Ishiguro Makio
Published: May 2008

Abstract. A Bayesian seasonal adjustment procedure was first proposed by Akaike (1979) and implemented as a computer program, BAYSEA (BAYesian SEasonal Adjustment program), by Akaike and Ishiguro (1980a).

This paper proposes an efficient algorithm to solve the least square problem which arises in the procedure.
FIRST ORDER AUTO‐REGRESSIVE MODEL PARAMETER ESTIMATION WITH PERIODIC OBSERVATIONS
Authors: Murthy D. N. P.
Published: May 2008

Abstract. In this note we study the method of maximum likelihood for estimating the parameters of a first order auto‐regressive model with periodic observation pattern.
A UNIFIED APPROACH TO CONFIDENCE BOUNDS FOR THE AUTOREGRESSIVE SPECTRAL ESTIMATOR
Authors: Koslov Judith W. , Jones Richard H.
Published: May 2008

Abstract. Confidence bounds for the spectral density of a stationary time series are derived. A unified method begins with the autoregressive spectral estimate and produces both confidence intervals at single frequencies chosen a priori and a simultaneous confidence band for multiple a posteriori comparisons. The crux is optimization of a quadratic form subject to the constraint imposed by the F‐statistic. An approximate method that may produce tighter bounds is described. The former methods are demonstrated on the Waldmeier annual mean sunspot numbers.
MINK AND MUSKRAT INTERACTION:A STRUCTURAL ANALYSIS
Authors: Teräsvirta Timo
Published: May 2008

Abstract.There is no unique way of representing a linear vector process, but in practice these processes are often characterized by their ARIMA representations. It is argued that for the purpose of interpretation the choice of canonical form is important, and the chosen form should as closely as possible correspond to existing prior information about the process, if any. This point is demonstrated by reanalysing the well‐known mink and muskrat data of Hudson's Bay Company from the year 1848 till 1909 using two separate single‐equation transfer function noise models to describe the relationship from mink to muskrat and the feedback link from muskrat to mink. The results thus obtained are from the practical point of view more informative than the corresponding ARIMA models.
THE STABILITY OF THE AR(1) PROCESS WITH AN AR(1) COEFFICIENT
Authors: Weiss Andrew A.
Published: May 2008

Abstract. In this paper we consider a simple time varying coefficient ARMA process:the AR (1) process with an AR (1) coefficient. A basic requirement of the process is that the output has finite variance, and we derive a condition on the parameters for this to be satisfied. The analysis is complicated by the interaction between the equations for the data and the varying coefficient.
ON THE ASYMPTOTIC DISTRIBUTION OF BARTLETT'S Up‐STATISTIC
Authors: Dahlhaus Rainer
Published: May 2008

Abstract. In this paper the asymptotic behaviour of Bartlett's Up‐statistic for a goodness‐of‐fit test for stationary processes, is considered. The asymptotic distribution of the test process is given under the assumption that a central limit theorem for the empirical spectral distribution function holds. It is shown that the Up‐statistic tends to the supremum of a tied down Brownian motion. By a counterexample we refute the conjecture that this distribution is in general of the Kolmogorov‐Smirnov type. The validity of the central limit theorem for the spectral distribution function is then discussed. Finally a goodness‐of‐fit test for ARMA‐processes based on the estimated innovation sequence is given, and it is shown that this test statistic is asymptotically Kolmogorov‐Smirnov distributed.
ON AN OPTIMALITY PROPERTY OF WHITTLE'S GAUSSIAN ESTIMATE OF THE PARAMETER OF THE SPECTRUM OF A TIME SERIES
Authors: Kulperger Reg
Published: May 2008

Abstract. Whittle has obtained an optimality property of a method of estimation of the parameter of the spectrum. In this paper we present a proof in the vector parameter case. Gaussian estimation is a natural method to consider, based on finite Fourier transforms and periodograms.
CENTRAL LIMIT THEOREMS FOR FINITE WALSH‐FOURIER TRANSFORMS OF WEAKLY STATIONARY TIME SERIES
Authors: Stoffer David S.
Published: May 2008

Abstract. In this paper we utilize martingale theorems to obtain central limit theorems for the finite Walsh‐Fourier transforms of various second‐order stationary processes.
NOTE ON THE KALMAN FILTER WITH ESTIMATED PARAMETERS
Authors: Watanabe N.
Published: May 2008

Abstract. State estimation and prediction problems are considered for a stochastic process represented by a state space form which involves unknown parameters. We first study the stability of the Kalman filter corresponding to the state space form without assuming the stationarity of the process. Second, we consider the state estimation and prediction when the process is stationary, and show some asymptotic properties of the state estimates and predicted values obtained by the Kalman filter with estimated parameters which converge to the true parameters or to the equivalent classes of the true parameters with probability one.
SOME DOUBLY STOCHASTIC TIME SERIES MODELS
Authors: Tjøstheim Dag
Published: May 2008

Abstract.We consider time series models obtained by replacing the parameters of autoregressive models by stochastic processes. Special attention is given to the problem of finding conditions for stationarity and to the problem of forecasting. For the first problem we are only able to obtain solutions in special cases, and the emphasis is on techniques rather than obtaining the most general results in each case. For the second problem more complete results are obtained by exploiting similarities with discrete time (nonlinear) filtering theory. The methods introduced are illustrated on two standard examples, one of state space type and one where the parameter process is a Markov chain.
ON STATIONARITY OF THE SOLUTION OF A DOUBLY STOCHASTIC MODEL
Authors: Pourahmadi Mohsen
Published: May 2008

Abstract. Consider the discrete parameter process {XI} satisfying the doubly stochastic model Xt=øtXt‐1+εt where {ø} and {εt} are also stochastic processes. Necessary and sufficient conditions on {ø} are given for {X1} to be a second order process. When {øt} is a strictly stationary process, some sufficient conditions in terms of {ø} are given which guarantee the wide sense stationarity of {Xt}. It turns out that for these problems the distribution and dependence structure of the process {log |ø|} play an important role.
ON LINEAR PROCESSES WITH GIVEN MOMENTS
Authors: Anděl Jiří
Published: May 2008

Abstract. Let Xt=c0Yt+c1Yt‐1+… be a linear process with known coefficients ck, where Yt is a strict white noise. Let m1, …, m2r be given numbers. A method is presented to determine whether there exists a distribution of Yt such that EXkt=mk for k= 1, …, 2r. In the positive case, such a distribution of Yt is described. Some explicit formulas for AR(1) and AR(2) models are derived. The results can be used for simulating a process with given moments of its stationary distribution. The procedure also enables proof that some stationary distributions cannot belong to the given linear process.
A NOTE ON NON‐STATIONARITY AND CANONICAL ANALYSIS OF MULTIPLE TIME SERIES MODELS
Authors: Velu Raja P. , Wichern Dean W. , Reinsel Gregory C.
Published: May 2008

Abstract. Box and Tiao (1977) established the correspondence between non‐stationary roots and canonical correlations of an AR(1) process. In this paper, we give an alternative, more direct, proof of the correspondence and extend a special case of that result to AR(p) processes. The usefulness of these results for multiple time series modelling is also briefly discussed.
DISTRIBUTIONS OF LEAST SQUARES ESTIMATORS OF AUTOREGRESSIVE PARAMETERS FOR A PROCESS WITH COMPLEX ROOTS ON THE UNIT CIRCLE
Authors: Ahtola Juha , Tiao George C.
Published: May 2008

Abstract.Asymptotic distributions of the autoregressive parameters in the AR(2) model are derived, when the characteristic polynomial has a pair of complex roots on the unit circle. Percentage points are tabulated based on simulations from the asymptotic formulae. The usefulness of the asymptotic results in finite sample situations is investigated by a Monte Carlo study, and an illustrative example is given.
A NOTE ON ASYMPTOTIC INFERENCE IN AUTOREGRESSIVE MODELS WITH ROOTS ON THE UNIT CIRCLE
Authors: Ahtola Juha , Tiao George C.
Published: May 2008

Abstract. An estimation and inference procedure is proposed for parameters of the pth order autoregressive model with roots both on the unit circle and outside the unit circle. The procedure is motivated by the fact that the parameter estimates of the nonstationary part of the model have higher order consistency properties than the parameter estimates of the stationary part. The procedure allows the use of the known asymptotic distributional results of purely nonstationary models and purely stationary models. Only ordinary least squares routines are needed.
DETECTION OF PERIODICITIES BY HIGHER‐ORDER CROSSINGS
Authors: Kedem Benjamin
Published: May 2008

Abstract. The axis‐crossing counts in a time series and in its successive differences are called higher‐order crossings (HOC). Under the Gaussian assumption the sequence of expected HOC is monotone increasing and admits a spectral representation which establishes a clear connection between HOC and the spectrum. In particular the normalized number of axis‐crossings (first HOC) tends to admit values at or near a dominant frequency, while the sequence of normalized HOC converges to the highest frequency in the spectrum. When the series is first lowpass filtered, the resulting normalized HOC tend to ‘visit’ true discrete frequencies on their way to the highest frequency. If the successive differences are replaced by successive summation, the resulting modified normalized HOC converge monotonically to the lowest frequency in the spectrum.
RATE OF CONVERGENCE OF CENTRED ESTIMATES OF AUTOREGRESSIVE PARAMETERS FOR INFINITE VARIANCE AUTOREGRESSIONS
Authors: Knight Keith
Published: May 2008

Abstract. Let Yn=μ+Σβj (Yn–j–μ) +ɛn be a pth order autoregressive process with innovations {ɛn} in the domain of attraction of a stable law with index α < 2. Hannan and Kanter (1977) showed that when the location parameter μ is known that least squares estimates of the autoregressive parameters have a very fast rate of convergence; specifically, N1/δ(βj–βj) → O almost surely for δ > α. It is shown here that if α is estimated by the sample mean, N1/δ(βj–βj) → O almost surely for δ > max(1, α). In addition, some statements are made regarding estimators of α which will give the full (Hannan and Kanter) rate of convergence, in particular when α < 1.
TIME SERIES ANALYSIS OF BOUNDED ECONOMIC VARIABLES
Authors: Wallis Kenneth F.
Published: May 2008

Abstract.We propose the logistic transformation as an appropriate instantaneous transformation for time series variables known to be bounded, such as those measured as proportions, and present an application to two unemployment rate series. Since such series are often analysed in logarithmic form, the two transformations are compared, and a likelihood ratio selection criterion is developed, which marginally favours the logistic transformation. We consider the question of transformation bias and its removal. The logistic transformation of bounded variables ensures consistency with the limiting behaviour and is symmetric, thus on transforming and modelling the unemployment rate or its complement, the employment rate, equivalent results are obtained.
IMPROVING THE COMPUTATIONAL EFFICIENCY OF THE BAYESIAN DECOMPOSITION OF A TIME SERIES: A COMMENT
Authors: Corradi Corrado , Scarani Claudia
Published: May 2008

Abstract. This note describes an improvement of Ishiguro's algorithm for the Bayesian decomposition of a time series based on the use of a specialized band Cholesky factorization.
SUR UN MODÉLE AUTORÉGRESSIF NON LINÉAIRE, ERGODICITÉ ET ERGODICITÉ GÉOMÉTRIQUE
Authors: Mokkadem Abdelkader
Published: May 2008

Resumé. On établit des conditions suffisantes d'ergodicité et d'ergodicité géométrique pour la chaine de Markov (Xn) définie par

On donne également des exemples d'application.

Abstract. We obtain sufficient conditions of ergodicity and geometric ergodicity for the Markov chain (Xn) defined by

We give also some applications.
SOME ASYMPTOTIC PROPERTIES OF THE SAMPLE COVARIANCES OF GAUSSIAN AUTOREGRESSIVE MOVING‐AVERAGE PROCESSES
Authors: Porat Boaz
Published: May 2008

Abstract. The paper deals with the asymptotic variances of the sample covariances of autoregressive moving average processes. Using state‐space representations and some matrix Lyapunov equation theory, closed‐form expressions are derived for the asymptotic variances of the sample covariances and for the Cramer‐Rao bounds on the process covariances. The main results obtained from these expressions are as follows: For ARMA (p, q) processes with p≥q, the sample covariance of order n is asymptotically efficient if and only if 0 ≤n≤p – q.

For ARMA (p, q) processes with p < q, none of the sample covariances is asymptotically efficient.
FIRST‐ORDER INTEGER‐VALUED AUTOREGRESSIVE (INAR(1)) PROCESS
Authors: Al‐Osh M. A. , Alzaid A. A.
Published: May 2008

Abstract.A simple model for a stationary sequence of integer‐valued random variables with lag‐one dependence is given and is referred to as the integer‐valued autoregressive of order one (INAR(1)) process. The model is suitable for counting processes in which an element of the process at timetcan be either the survival of an element of the process at timet‐ 1 or the outcome of an innovation process. The correlation structure and the distributional properties of the INAR(1) model are similar to those of the continuous‐valued AR(1) process. Several methods for estimating the parameters of the model are discussed, and the results of a simulation study for these estimation methods are presented.
ASYMPTOTIC EXPANSIONS FOR THE DISTRIBUTIONS OF SERIAL CORRELATIONS
Authors: Chanda Kamal C.
Published: May 2008

Abstract. Let X1, …, Xn be a random sample from a population with a distribution function F and let E(X1) = 0, E(X12) < ∞. Let r1=Σt=1n‐1XtXt+1/Σt=1n‐1(Xt2+Xt+12). We derive a proper Edgeworth type expansion for the sampling distribution of r1 under the assumption that F is a mixture of Gaussian distributions of one of two given types. The result can easily be extended to the sampling distributions of serial correlations of arbitrary lag s.
TIME SERIES RESIDUALS WITH APPLICATION TO PROBABILITY DENSITY ESTIMATION
Authors: Robinson P. M.
Published: May 2008

Abstract. A linear stationary and invertible process yt models the second‐order properties of T observations on a discrete time series, up to finitely many unknown parameters θ. Two estimators of the residuals or innovations ɛt of yt are presented, based on a θ estimator which is root‐T consistent with respect to a wide class of ɛt distributions, such as a Gaussian estimator. One sets unobserved yt equal to their mean, the other treats yt as a circulant and may be best computed via two passes of the fast Fourier transform. The convergence of both estimators to ɛt is investigated. We apply the estimated ɛt to estimate the probability density function of ɛt. Kernel density estimators are shown to converge uniformly in probability to the true density. A new sub‐class of linear time series models is motivated.
ASYMPTOTIC DISTRIBUTIONS OF LIKELIHOOD RATIOS FOR OVERPARAMETRIZED ARMA PROCESSES
Authors: Veres Sándor
Published: May 2008

Abstract. This paper is devoted to an extension of a classical problem of statistics to the asymptotic distribution of likelihood ratios. Two main types of likelihood ratios are considered for Gaussian ARMA processes. It is assumed in both cases that the asymptotic Fisher information matrix of estimation is singular in the higher order models. It is proved that the asymptotic distributions of the log likelihood ratios are invariant with respect to the parameters generating the process. A simulation shows that the sample distribution of the log likelihood ratio approaches the asymptotic one. Finally, the likelihood ratio test is proposed for model order reduction.
IDENTIFICATION THEORY FOR VARYING COEFFICIENT REGRESSION MODELS1
Authors: Wall Kent D.
Published: May 2008

Abstract. The identification problem for regression models with time‐varying coefficients is investigated using state‐space model representations and some results from linear dynamic systems theory. The approach taken focuses on second moment information and yields global as opposed to local asymptotic results. The form of the equivalence classes that generate the identification problem is first derived and then employed to establish the identification of one of the most natural structural specifications currently employed in econometrics.
ON THE ESTIMATION OF THE INVERSE CORRELATION FUNCTION
Authors: Battaglia Francesco
Published: May 2008

Two new methods for estimating the inverse covariance and inverse correlation functions of a time series are proposed. One of them is based on an orthogonality property, the other is suggested by interpolation considerations. The two methods are shown to be asymptotically equivalent, and their asymptotic distribution is derived. The asymptotic distribution turns out to be the same as that of the autoregressive estimates of the inverse correlations. The problem of choosing an estimation method in practice is discussed.
ESTIMATION IN LONG‐MEMORY TIME SERIES MODEL
Authors: Kashyap R. L. , Eom Kie‐Bum
Published: May 2008

This study deals with the parameter estimation in long‐memory time series models. An unbiased and consistent estimator is proposed. The proposed estimator is based on a least‐squares method in the frequency domain, and it is computationally simple. Also, the Cramer–Rao lower bound is derived. The mean‐square error of the proposed estimator is order of O(1/N), where N is the number of samples. The accuracy of the estimates is verified using synthetic long‐memory time series data.
STRUCTURAL, DYNAMIC MODELLING IN UNOBSERVABLE SPACES OF COVARIANCE‐STATIONARY STOCHASTIC PROCESSES
Authors: Otter Pieter W.
Published: May 2008

In this paper a structural, stationary version of the well‐known state‐space model is used to model covariance‐stationary stochastic processes. The identifiability of the model parameters is discussed and a rank condition for local parameter identifiability is given. Ljung's results on prediction‐error estimation are used to establish strong consistency and asymptotic efficiency of the non‐linear ML‐estimates obtained from dependent observations. It turns out that the model can be identified by using simultaneously the steady‐state Kalman filter for the unobservable state vector and the prediction‐error estimation method for the model parameters.
ON THE CORRELATION STRUCTURE FOR THE GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTIC PROCESS
Authors: Bollerslev Tim
Published: May 2008

Abstract. The correlation structure for the squares from the generalized autoregressive conditional heteroskedastic (GARCH) process is presented. It is shown that the behaviour of the correlations for the squares mimics the usual correlations of an appropriately defined ARMA process, although the admissible regions for the correlations are somewhat more restrictive. Simulation experiments are used to study the applicability of the theoretical results for order identification and diagnostic checking. Finally, an empirical example is given for the IBM stock market price series from Box and Jenkins (1976).
ON A CLASS OF NONSTATIONARY PROCESSES
Authors: Gray H. L. , Zhang Nien Fan
Published: May 2008

Abstract. In this paper a class of nonstationary processes, referred to as multiplicative stationary processes, is investigated. It is shown that although these processes are not stationary with regard to an additive binary operation, i.e. in the classical sense, they are stationary with respect to a multiplicative binary operation. This property is then exploited in such a way as to guarantee essentially the same structure as is available for stationary processes. In particular, suitable definitions for the autocorrelation, power spectrum and linear processes are given. In addition, the Euler process is introduced as the nonstationary or multiplicative stationary dual of the classical autoregressive processes. Some ergodic theorems are also obtained and numerous examples are given.
ON THE EFFICIENCY OF THE SAMPLE MEAN IN LONG‐MEMORY NOISE
Authors: Samarov Alexander , Taqqu Murad S.
Published: May 2008

Abstract. When estimating the unknown mean of a stationary time series, the best linear unbiased estimator is often a significantly better estimator than the ordinary least squares estimates X̄n. The relative efficiency of these two estimators is investigated for time series whose spectrum behaves like a power at the origin (e.g., fractional Gaussian noise and fractional ARIMA).
ON PREDICTION WITH FRACTIONALLY DIFFERENCED ARIMA MODELS
Authors: Peiris M. S. , Perera B. J. C.
Published: May 2008

Abstract. This paper considers some extended results associated with the predictors of long‐memory time series models. These direct methods of obtaining predictors of fractionally differenced autoregressive integrated moving‐average (ARIMA) processes have advantages from the theoretical point of view.
DISCRIMINATING BETWEEN TWO SPECTRAL DENSITIES IN CASE OF REPLICATED OBSERVATIONS
Authors: Pötscher B. M. , Reschenhofer E.
Published: May 2008

Abstract. Coates and Diggle (1986) introduced a test procedure for the comparison of the spectral densities of two stationary processes. We extend this test to a situation in which replicated observations are available for each process.
ESTIMATION OF AUTOREGRESSIVE PARAMETERS AND ORDER SELECTION FOR ARMA MODELS
Authors: Tuan Pham Dinh
Published: May 2008

Abstract. A quick algorithm for obtaining estimates of autoregressive parameters for autoregressive moving‐average model is presented. The algorithm is recursive in the orders, and can be used for model selection by providing a criterion and a two‐way table of certain partial covariances. Consistency and asymptotic normality of the estimates are shown.
SUBORDINATION OF STATIONARY PROCESSES
Authors: Willekens E. , Teugels J. L.
Published: May 2008

Abstract. Let X={X(t), tεT∁ R} be a (L2 ‐) stationary process and suppose that N={N(t), t≥ 0} is an infinitely divisible process, independent of X. Then X̂={X̂(t) =X(N(t)), t≥ 0} is again a stationary process. In this paper, we relate the spectral properties of the original process X and the derived or subordinated process X̂.
AN ALTERNATIVE CONSISTENT PROCEDURE FOR DETECTING HIDDEN FREQUENCIES
Authors: Zhao‐Guo Chen
Published: May 2008

Abstract.An alternative procedure is developed to detect the hidden frequencies in linear processes which differs from the procedure proposed earlier by the author. The advantage of the new procedure is that it enables us to detect a hidden frequency in the noise when the spectral density possesses high peaks. In such cases the earlier procedure often fails in practice. We also prove the convergence of a spectral estimate designed to reduce the influence of hidden frequencies.
MODELS THAT GENERATE TRENDS
Authors: Granger C. W. J.
Published: May 2008

Abstract. Using the theory of divergent series, a class of linear filters are considered that when applied to a constant generate a wide class of deterministic trends in mean. If these filters are applied to a white noise, series are produced that have changing variances and may correspond to fractional integrated series. Some other trend‐generating mechanisms are also considered and methods of characterizing different trends are discussed.
A NEW VERSION OF STRUCTURAL PERSISTENCE IN PREDICTION
Authors: Raveh Adi
Published: May 2008

Abstract. A data analysis estimation method called structural persistence is presented in this paper. Prediction applications of the method to time series with trend and seasonal components are discussed. The basic underlying assumption is that the structure of a given series does not change in the forecasting range. Therefore, when forecasts are made, the values which measure the structure (e.g. the trend shape) of the original and of the forecast‐extended time series should be the same. We propose relaxed ‘model‐free’ use of the principle which nevertheless provides an explicit prediction formula for the one‐stepahead prediction Pn+1. Missing data can also be estimated using this method. The procedure is applied to some previously published time series data and the prediction results are compared with those obtained using the Box‐Jenkins approach.